Remove deprecated task in load_dataset

.circleci/config.yml

@@ -7,25 +7,12 @@ parameters:
     nightly:
         type: boolean
         default: false
-    GHA_Actor:
-        type: string
-        default: ""
-    GHA_Action:
-        type: string
-        default: ""
-    GHA_Event:
-        type: string
-        default: ""
-    GHA_Meta:
-        type: string
-        default: ""
 
 jobs:
     # Ensure running with CircleCI/huggingface
     check_circleci_user:
         docker:
             - image: python:3.10-slim
-        resource_class: small
         parallelism: 1
         steps:
             - run: echo $CIRCLE_PROJECT_USERNAME
@@ -60,13 +47,13 @@ jobs:
 
             - run:
                 name: "Retrieve Artifact Paths"
-                # [reference] https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts
-                # `CIRCLE_TOKEN` is defined as an environment variables set within a context, see `https://circleci.com/docs/contexts/`
+                env:
+                    CIRCLE_TOKEN: ${{ secrets.CI_ARTIFACT_TOKEN }}
                 command: |
                     project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}"
                     job_number=${CIRCLE_BUILD_NUM}
                     url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts"
-                    curl -o test_preparation/artifacts.json ${url} --header "Circle-Token: $CIRCLE_TOKEN"
+                    curl -o  test_preparation/artifacts.json ${url}
             - run:
                 name: "Prepare pipeline parameters"
                 command: |
@@ -95,46 +82,21 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: uv pip install -U -e .
-            - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
-            - run: mkdir -p test_preparation
-            - run: python utils/tests_fetcher.py --fetch_all | tee tests_fetched_summary.txt
-            - run: python utils/tests_fetcher.py --filter_tests
-            - run: export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)" && echo $GIT_COMMIT_MESSAGE && python .circleci/create_circleci_config.py --fetcher_folder test_preparation
+            - run: uv pip install -e .
             - run: |
-                if [ ! -s test_preparation/generated_config.yml ]; then
-                    echo "No tests to run, exiting early!"
-                    circleci-agent step halt
-                fi
-
+                  mkdir test_preparation
+                  echo -n "tests" > test_preparation/test_list.txt
+                  echo -n "all" > test_preparation/examples_test_list.txt
+                  echo -n "tests/repo_utils" > test_preparation/test_repo_utils.txt
+            - run: |
+                  echo -n "tests" > test_list.txt
+                  python utils/tests_fetcher.py --filter_tests
+                  mv test_list.txt test_preparation/filtered_test_list.txt
+            - run: python .circleci/create_circleci_config.py --fetcher_folder test_preparation
+            - run: cp test_preparation/generated_config.yml test_preparation/generated_config.txt
             - store_artifacts:
-                path: test_preparation
-
-            - run:
-                name: "Retrieve Artifact Paths"
-                command: |
-                    project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}"
-                    job_number=${CIRCLE_BUILD_NUM}
-                    url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts"
-                    curl -o  test_preparation/artifacts.json ${url}
-            - run:
-                name: "Prepare pipeline parameters"
-                command: |
-                    python utils/process_test_artifacts.py
-
-            # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
-            # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
-            # We used:
-
-            # https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts : to get the job artifacts
-            # We could not pass a nested dict, which is why we create the test_file_... parameters for every single job
-
-            - store_artifacts:
-                path: test_preparation/transformed_artifacts.json
-            - store_artifacts:
-                path: test_preparation/artifacts.json
+                  path: test_preparation/generated_config.txt
             - continuation/continue:
-                parameters:  test_preparation/transformed_artifacts.json
                   configuration_path: test_preparation/generated_config.yml
 
     check_code_quality:
@@ -148,7 +110,7 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: uv pip install -e ".[quality]"
+            - run: uv pip install -e .
             - run:
                 name: Show installed libraries and their versions
                 command: pip freeze | tee installed.txt
@@ -156,7 +118,7 @@ jobs:
                   path: ~/transformers/installed.txt
             - run: python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
             - run: ruff check examples tests src utils
-            - run: ruff format examples tests src utils --check
+            - run: ruff format tests src utils --check
             - run: python utils/custom_init_isort.py --check_only
             - run: python utils/sort_auto_mappings.py --check_only
             - run: python utils/check_doc_toc.py
@@ -173,51 +135,36 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: uv pip install -e ".[quality]"
+            - run: uv pip install -e .
             - run:
                 name: Show installed libraries and their versions
                 command: pip freeze | tee installed.txt
             - store_artifacts:
                   path: ~/transformers/installed.txt
             - run: python utils/check_copies.py
-            - run: python utils/check_modular_conversion.py
+            - run: python utils/check_table.py
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py
             - run: python utils/check_inits.py
-            - run: python utils/check_pipeline_typing.py
             - run: python utils/check_config_docstrings.py
             - run: python utils/check_config_attributes.py
             - run: python utils/check_doctest_list.py
             - run: make deps_table_check_updated
             - run: python utils/update_metadata.py --check-only
             - run: python utils/check_docstrings.py
+            - run: python utils/check_support_list.py
 
 workflows:
     version: 2
     setup_and_quality:
         when:
-            and:
-                - equal: [<<pipeline.project.git_url>>, https://github.com/huggingface/transformers]
-                - not: <<pipeline.parameters.nightly>>
+            not: <<pipeline.parameters.nightly>>
         jobs:
             - check_circleci_user
             - check_code_quality
             - check_repository_consistency
             - fetch_tests
 
-    setup_and_quality_2:
-        when:
-            not:
-                 equal: [<<pipeline.project.git_url>>, https://github.com/huggingface/transformers]
-        jobs:
-            - check_circleci_user
-            - check_code_quality
-            - check_repository_consistency
-            - fetch_tests:
-                # [reference] https://circleci.com/docs/contexts/
-                context:
-                    - TRANSFORMERS_CONTEXT
-
     nightly:
         when: <<pipeline.parameters.nightly>>
         jobs:

.circleci/create_circleci_config.py

@@ -28,54 +28,21 @@ COMMON_ENV_VARIABLES = {
     "TRANSFORMERS_IS_CI": True,
     "PYTEST_TIMEOUT": 120,
     "RUN_PIPELINE_TESTS": False,
-    # will be adjust in `CircleCIJob.to_dict`.
-    "RUN_FLAKY": True,
+    "RUN_PT_TF_CROSS_TESTS": False,
+    "RUN_PT_FLAX_CROSS_TESTS": False,
 }
 # Disable the use of {"s": None} as the output is way too long, causing the navigation on CircleCI impractical
-COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "vvv": None, "rsfE":None}
+COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "vvv": None, "rsf":None}
 DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.8.12"}]
 
-# Strings that commonly appear in the output of flaky tests when they fail. These are used with `pytest-rerunfailures`
-# to rerun the tests that match these patterns.
-FLAKY_TEST_FAILURE_PATTERNS = [
-    "OSError",  # Machine/connection transient error
-    "Timeout",  # Machine/connection transient error
-    "ConnectionError",  # Connection transient error
-    "FileNotFoundError",  # Raised by `datasets` on Hub failures
-    "PIL.UnidentifiedImageError",  # Raised by `PIL.Image.open` on connection issues
-    "HTTPError",  # Also catches HfHubHTTPError
-    "AssertionError: Tensor-likes are not close!",  # `torch.testing.assert_close`, we might have unlucky random values
-    # TODO: error downloading tokenizer's `merged.txt` from hub can cause all the exceptions below. Throw and handle
-    # them under a single message.
-    "TypeError: expected str, bytes or os.PathLike object, not NoneType",
-    "TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType",
-    "Converting from Tiktoken failed",
-    "KeyError: <class ",
-    "TypeError: not a string",
-]
-
 
 class EmptyJob:
     job_name = "empty"
 
     def to_dict(self):
-        steps = [{"run": 'ls -la'}]
-        if self.job_name == "collection_job":
-            steps.extend(
-                [
-                    "checkout",
-                    {"run": "pip install requests || true"},
-                    {"run": """while [[ $(curl --location --request GET "https://circleci.com/api/v2/workflow/$CIRCLE_WORKFLOW_ID/job" --header "Circle-Token: $CCI_TOKEN"| jq -r '.items[]|select(.name != "collection_job")|.status' | grep -c "running") -gt 0 ]]; do sleep 5; done || true"""},
-                    {"run": 'python utils/process_circleci_workflow_test_reports.py --workflow_id $CIRCLE_WORKFLOW_ID || true'},
-                    {"store_artifacts": {"path": "outputs"}},
-                    {"run": 'echo "All required jobs have now completed"'},
-                ]
-            )
-
         return {
             "docker": copy.deepcopy(DEFAULT_DOCKER_IMAGE),
-            "resource_class": "small",
-            "steps": steps,
+            "steps":["checkout"],
         }
 
 
@@ -87,9 +54,9 @@ class CircleCIJob:
     install_steps: List[str] = None
     marker: Optional[str] = None
     parallelism: Optional[int] = 0
-    pytest_num_workers: int = 8
+    pytest_num_workers: int = 12
     pytest_options: Dict[str, Any] = None
-    resource_class: Optional[str] = "xlarge"
+    resource_class: Optional[str] = "2xlarge"
     tests_to_run: Optional[List[str]] = None
     num_test_files_per_worker: Optional[int] = 10
     # This should be only used for doctest job!
@@ -128,8 +95,6 @@ class CircleCIJob:
 
     def to_dict(self):
         env = COMMON_ENV_VARIABLES.copy()
-        # Do not run tests decorated by @is_flaky on pull requests
-        env['RUN_FLAKY'] = os.environ.get("CIRCLE_PULL_REQUEST", "") == ""
         env.update(self.additional_env)
 
         job = {
@@ -147,15 +112,17 @@ class CircleCIJob:
                 # Examples special case: we need to download NLTK files in advance to avoid cuncurrency issues
         timeout_cmd = f"timeout {self.command_timeout} " if self.command_timeout else ""
         marker_cmd = f"-m '{self.marker}'" if self.marker is not None else ""
-        junit_flags = f" -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml"
-        joined_flaky_patterns = "|".join(FLAKY_TEST_FAILURE_PATTERNS)
-        repeat_on_failure_flags = f"--reruns 5 --reruns-delay 2 --only-rerun '({joined_flaky_patterns})'"
+        additional_flags = f" -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml"
         parallel = f' << pipeline.parameters.{self.job_name}_parallelism >> '
         steps = [
             "checkout",
             {"attach_workspace": {"at": "test_preparation"}},
             {"run": "apt-get update && apt-get install -y curl"},
             {"run": " && ".join(self.install_steps)},
+            {"run": {"name": "Install datasets@main",
+                     "command": 'pip uninstall datasets -y && pip install "datasets @ git+https://github.com/huggingface/datasets@main#egg=datasets"'}},
+            {"run": {"name": "Install evaluate@main",
+                     "command": 'pip uninstall evaluate -y && pip install "evaluate @ git+https://github.com/huggingface/evaluate@main#egg=evaluate"'}},
             {"run": {"name": "Download NLTK files", "command": """python -c "import nltk; nltk.download('punkt', quiet=True)" """} if "example" in self.name else "echo Skipping"},
             {"run": {
                     "name": "Show installed libraries and their size",
@@ -170,15 +137,14 @@ class CircleCIJob:
                 "command": """dpkg-query --show --showformat='${Installed-Size}\t${Package}\n' | sort -rh | head -25 | sort -h | awk '{ package=$2; sub(".*/", "", package); printf("%.5f GB %s\n", $1/1024/1024, package)}' || true"""}
             },
             {"run": {"name": "Create `test-results` directory", "command": "mkdir test-results"}},
-            {"run": {"name": "Get files to test", "command":f'curl -L -o {self.job_name}_test_list.txt <<pipeline.parameters.{self.job_name}_test_list>> --header "Circle-Token: $CIRCLE_TOKEN"' if self.name != "pr_documentation_tests" else 'echo "Skipped"'}},
+            {"run": {"name": "Get files to test", "command":f'curl -L -o {self.job_name}_test_list.txt <<pipeline.parameters.{self.job_name}_test_list>>' if self.name != "pr_documentation_tests" else 'echo "Skipped"'}},
                         {"run": {"name": "Split tests across parallel nodes: show current parallel tests",
                     "command": f"TESTS=$(circleci tests split  --split-by=timings {self.job_name}_test_list.txt) && echo $TESTS > splitted_tests.txt && echo $TESTS | tr ' ' '\n'" if self.parallelism else f"awk '{{printf \"%s \", $0}}' {self.job_name}_test_list.txt > splitted_tests.txt"
                     }
             },
-            {"run": {"name": "fetch hub objects before pytest", "command": "python3 utils/fetch_hub_objects_for_ci.py"}},
             {"run": {
                 "name": "Run tests",
-                "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {junit_flags} {repeat_on_failure_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"}
+                "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {additional_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"}
             },
             {"run": {"name": "Expand to show skipped tests", "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --skip"}},
             {"run": {"name": "Failed tests: show reasons",   "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --fail"}},
@@ -201,43 +167,87 @@ class CircleCIJob:
 
 
 # JOBS
+torch_and_tf_job = CircleCIJob(
+    "torch_and_tf",
+    docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
+    additional_env={"RUN_PT_TF_CROSS_TESTS": True},
+    marker="is_pt_tf_cross_test",
+    pytest_options={"rA": None, "durations": 0},
+)
+
+
+torch_and_flax_job = CircleCIJob(
+    "torch_and_flax",
+    additional_env={"RUN_PT_FLAX_CROSS_TESTS": True},
+    docker_image=[{"image":"huggingface/transformers-torch-jax-light"}],
+    marker="is_pt_flax_cross_test",
+    pytest_options={"rA": None, "durations": 0},
+)
+
 torch_job = CircleCIJob(
     "torch",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     marker="not generate",
     parallelism=6,
+    pytest_num_workers=8
 )
 
 generate_job = CircleCIJob(
     "generate",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
-    # networkx==3.3 (after #36957) cause some issues
-    # TODO: remove this once it works directly
-    install_steps=["uv venv && uv pip install ."],
     marker="generate",
     parallelism=6,
+    pytest_num_workers=8
 )
 
 tokenization_job = CircleCIJob(
     "tokenization",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     parallelism=8,
+    pytest_num_workers=16
 )
 
 processor_job = CircleCIJob(
     "processors",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     parallelism=8,
+    pytest_num_workers=6
 )
 
+tf_job = CircleCIJob(
+    "tf",
+    docker_image=[{"image":"huggingface/transformers-tf-light"}],
+    parallelism=6,
+    pytest_num_workers=16,
+)
+
+
+flax_job = CircleCIJob(
+    "flax",
+    docker_image=[{"image":"huggingface/transformers-jax-light"}],
+    parallelism=6,
+    pytest_num_workers=16
+)
+
+
 pipelines_torch_job = CircleCIJob(
     "pipelines_torch",
     additional_env={"RUN_PIPELINE_TESTS": True},
     docker_image=[{"image":"huggingface/transformers-torch-light"}],
     marker="is_pipeline_test",
-    parallelism=4,
+    parallelism=4
 )
 
+
+pipelines_tf_job = CircleCIJob(
+    "pipelines_tf",
+    additional_env={"RUN_PIPELINE_TESTS": True},
+    docker_image=[{"image":"huggingface/transformers-tf-light"}],
+    marker="is_pipeline_test",
+    parallelism=4
+)
+
+
 custom_tokenizers_job = CircleCIJob(
     "custom_tokenizers",
     additional_env={"RUN_CUSTOM_TOKENIZERS": True},
@@ -251,9 +261,18 @@ examples_torch_job = CircleCIJob(
     docker_image=[{"image":"huggingface/transformers-examples-torch"}],
     # TODO @ArthurZucker remove this once docker is easier to build
     install_steps=["uv venv && uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
-    pytest_num_workers=4,
+    pytest_num_workers=8,
 )
 
+
+examples_tensorflow_job = CircleCIJob(
+    "examples_tensorflow",
+    additional_env={"OMP_NUM_THREADS": 8},
+    docker_image=[{"image":"huggingface/transformers-examples-tf"}],
+    pytest_num_workers=16,
+)
+
+
 hub_job = CircleCIJob(
     "hub",
     additional_env={"HUGGINGFACE_CO_STAGING": True},
@@ -265,7 +284,6 @@ hub_job = CircleCIJob(
     ],
     marker="is_staging_test",
     pytest_num_workers=2,
-    resource_class="medium",
 )
 
 
@@ -274,17 +292,17 @@ onnx_job = CircleCIJob(
     docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
     install_steps=[
         "uv venv",
-        "uv pip install .[testing,sentencepiece,onnxruntime,vision,rjieba]",
+        "uv pip install .[torch,tf,testing,sentencepiece,onnxruntime,vision,rjieba]",
     ],
     pytest_options={"k onnx": None},
     pytest_num_workers=1,
-    resource_class="small",
 )
 
 
 exotic_models_job = CircleCIJob(
     "exotic_models",
     docker_image=[{"image":"huggingface/transformers-exotic-models"}],
+    pytest_num_workers=12,
     parallelism=4,
     pytest_options={"durations": 100},
 )
@@ -298,17 +316,6 @@ repo_utils_job = CircleCIJob(
 )
 
 
-non_model_job = CircleCIJob(
-    "non_model",
-    docker_image=[{"image": "huggingface/transformers-torch-light"}],
-    # networkx==3.3 (after #36957) cause some issues
-    # TODO: remove this once it works directly
-    install_steps=["uv venv && uv pip install ."],
-    marker="not generate",
-    parallelism=6,
-)
-
-
 # We also include a `dummy.py` file in the files to be doc-tested to prevent edge case failure. Otherwise, the pytest
 # hangs forever during test collection while showing `collecting 0 items / 21 errors`. (To see this, we have to remove
 # the bash output redirection.)
@@ -333,14 +340,13 @@ doc_test_job = CircleCIJob(
     pytest_num_workers=1,
 )
 
-REGULAR_TESTS = [torch_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
-EXAMPLES_TESTS = [examples_torch_job]
-PIPELINE_TESTS = [pipelines_torch_job]
+REGULAR_TESTS = [torch_and_tf_job, torch_and_flax_job, torch_job, tf_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job] # fmt: skip
+EXAMPLES_TESTS = [examples_torch_job, examples_tensorflow_job]
+PIPELINE_TESTS = [pipelines_torch_job, pipelines_tf_job]
 REPO_UTIL_TESTS = [repo_utils_job]
 DOC_TESTS = [doc_test_job]
 ALL_TESTS = REGULAR_TESTS + EXAMPLES_TESTS + PIPELINE_TESTS + REPO_UTIL_TESTS + DOC_TESTS + [custom_tokenizers_job] + [exotic_models_job]  # fmt: skip
 
-
 def create_circleci_config(folder=None):
     if folder is None:
         folder = os.getcwd()
@@ -350,35 +356,19 @@ def create_circleci_config(folder=None):
 
     if len(jobs) == 0:
         jobs = [EmptyJob()]
-    else:
     print("Full list of job name inputs", {j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs})
-        # Add a job waiting all the test jobs and aggregate their test summary files at the end
-        collection_job = EmptyJob()
-        collection_job.job_name = "collection_job"
-        jobs = [collection_job] + jobs
-
     config = {
         "version": "2.1",
         "parameters": {
             # Only used to accept the parameters from the trigger
             "nightly": {"type": "boolean", "default": False},
-            # Only used to accept the parameters from GitHub Actions trigger
-            "GHA_Actor": {"type": "string", "default": ""},
-            "GHA_Action": {"type": "string", "default": ""},
-            "GHA_Event": {"type": "string", "default": ""},
-            "GHA_Meta": {"type": "string", "default": ""},
-            "tests_to_run": {"type": "string", "default": ""},
+            "tests_to_run": {"type": "string", "default": ''},
             **{j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs},
             **{j.job_name + "_parallelism":{"type":"integer", "default":1} for j in jobs},
         },
-        "jobs": {j.job_name: j.to_dict() for j in jobs}
+        "jobs" : {j.job_name: j.to_dict() for j in jobs},
+        "workflows": {"version": 2, "run_tests": {"jobs": [j.job_name for j in jobs]}}
     }
-    if "CIRCLE_TOKEN" in os.environ:
-        # For private forked repo. (e.g. new model addition)
-        config["workflows"] = {"version": 2, "run_tests": {"jobs": [{j.job_name: {"context": ["TRANSFORMERS_CONTEXT"]}} for j in jobs]}}
-    else:
-        # For public repo. (e.g. `transformers`)
-        config["workflows"] = {"version": 2, "run_tests": {"jobs": [j.job_name for j in jobs]}}
     with open(os.path.join(folder, "generated_config.yml"), "w") as f:
         f.write(yaml.dump(config, sort_keys=False, default_flow_style=False).replace("' << pipeline", " << pipeline").replace(">> '", " >>"))
 

.coveragerc

@@ -0,0 +1,12 @@
+[run]
+source=transformers
+omit =
+    # skip convertion scripts from testing for now
+    */convert_*
+    */__main__.py
+[report]
+exclude_lines =
+    pragma: no cover
+    raise
+    except
+    register_parameter

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
       placeholder: transformers version, platform, python version, ...
     validations:
       required: true
@@ -38,30 +38,24 @@ body:
 
           - text models: @ArthurZucker
           - vision models: @amyeroberts, @qubvel
-          - speech models: @eustlb
+          - speech models: @ylacombe, @eustlb
           - graph models: @clefourrier
 
         Library:
 
-          - flax: @gante and @Rocketknight1
+          - flax: @sanchit-gandhi
           - generate: @zucchini-nlp (visual-language models) or @gante (all others)
           - pipelines: @Rocketknight1
           - tensorflow: @gante and @Rocketknight1
           - tokenizers: @ArthurZucker and @itazap
-          - trainer: @zach-huggingface @SunMarc
+          - trainer: @muellerzr @SunMarc
 
         Integrations:
 
-          - deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
+          - deepspeed: HF Trainer/Accelerate: @muellerzr
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @SunMarc
-          - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
-        
-        Devices/Backends:
-        
-          - AMD ROCm: @ivarflakstad
-          - Intel XPU: @IlyasMoutawwakil
-          - Ascend NPU: @ivarflakstad 
+          - quantization (bitsandbytes, autogpt): @SunMarc
 
         Documentation: @stevhliu
 
@@ -78,7 +72,7 @@ body:
 
         Maintained examples (not research project or legacy):
 
-          - Flax: @Rocketknight1
+          - Flax: @sanchit-gandhi
           - PyTorch: See Models above and tag the person corresponding to the modality of the example.
           - TensorFlow: @Rocketknight1
 
@@ -112,7 +106,6 @@ body:
       label: Reproduction
       description: |
         Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        Please include relevant config information with your code, for example your Trainers, TRL, Peft, and DeepSpeed configs.
         If you have code snippets, error messages, stack traces please provide them here as well.
         Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
         Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.

.github/ISSUE_TEMPLATE/i18n.md

@@ -23,7 +23,7 @@ Some notes:
 * Please translate in a gender-neutral way.
 * Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
 * Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
-* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.
+* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu and @MKhalusova for review.
 * 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
 
 ## Get Started section

.github/ISSUE_TEMPLATE/migration.yml

@@ -6,7 +6,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
       render: shell
       placeholder: transformers version, platform, python version, ...
     validations:

.github/PULL_REQUEST_TEMPLATE.md

@@ -41,25 +41,25 @@ Models:
 
 - text models: @ArthurZucker
 - vision models: @amyeroberts, @qubvel
-- speech models: @eustlb
+- speech models: @ylacombe, @eustlb
 - graph models: @clefourrier
 
 Library:
 
-- flax: @gante and @Rocketknight1
+- flax: @sanchit-gandhi
 - generate: @zucchini-nlp (visual-language models) or @gante (all others)
 - pipelines: @Rocketknight1
 - tensorflow: @gante and @Rocketknight1
 - tokenizers: @ArthurZucker
-- trainer: @zach-huggingface, @SunMarc and @qgallouedec
+- trainer: @muellerzr and @SunMarc
 - chat templates: @Rocketknight1
 
 Integrations:
 
-- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
+- deepspeed: HF Trainer/Accelerate: @muellerzr
 - ray/raytune: @richardliaw, @amogkam
 - Big Model Inference: @SunMarc
-- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
+- quantization (bitsandbytes, autogpt): @SunMarc
 
 Documentation: @stevhliu
 
@@ -72,7 +72,7 @@ HF projects:
 
 Maintained examples (not research project or legacy):
 
-- Flax: @Rocketknight1
+- Flax: @sanchit-gandhi
 - PyTorch: See Models above and tag the person corresponding to the modality of the example.
 - TensorFlow: @Rocketknight1
 

.github/scripts/assign_reviewers.py

@@ -1,120 +0,0 @@
-# coding=utf-8
-# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import github
-import json
-from github import Github
-import re
-from collections import Counter
-from pathlib import Path
-
-def pattern_to_regex(pattern):
-    if pattern.startswith("/"):
-        start_anchor = True
-        pattern = re.escape(pattern[1:])
-    else:
-        start_anchor = False
-        pattern = re.escape(pattern)
-    # Replace `*` with "any number of non-slash characters"
-    pattern = pattern.replace(r"\*", "[^/]*")
-    if start_anchor:
-        pattern = r"^\/?" + pattern  # Allow an optional leading slash after the start of the string
-    return pattern
-
-def get_file_owners(file_path, codeowners_lines):
-    # Process lines in reverse (last matching pattern takes precedence)
-    for line in reversed(codeowners_lines):
-        # Skip comments and empty lines, strip inline comments
-        line = line.split('#')[0].strip()
-        if not line:
-            continue
-
-        # Split into pattern and owners
-        parts = line.split()
-        pattern = parts[0]
-        # Can be empty, e.g. for dummy files with explicitly no owner!
-        owners = [owner.removeprefix("@") for owner in parts[1:]]
-
-        # Check if file matches pattern
-        file_regex = pattern_to_regex(pattern)
-        if re.search(file_regex, file_path) is not None:
-            return owners  # Remember, can still be empty!
-    return []  # Should never happen, but just in case
-
-def pr_author_is_in_hf(pr_author, codeowners_lines):
-    # Check if the PR author is in the codeowners file
-    for line in codeowners_lines:
-        line = line.split('#')[0].strip()
-        if not line:
-            continue
-
-        # Split into pattern and owners
-        parts = line.split()
-        owners = [owner.removeprefix("@") for owner in parts[1:]]
-
-        if pr_author in owners:
-            return True
-    return False
-
-def main():
-    script_dir = Path(__file__).parent.absolute()
-    with open(script_dir / "codeowners_for_review_action") as f:
-        codeowners_lines = f.readlines()
-
-    g = Github(os.environ['GITHUB_TOKEN'])
-    repo = g.get_repo("huggingface/transformers")
-    with open(os.environ['GITHUB_EVENT_PATH']) as f:
-        event = json.load(f)
-
-    # The PR number is available in the event payload
-    pr_number = event['pull_request']['number']
-    pr = repo.get_pull(pr_number)
-    pr_author = pr.user.login
-    if pr_author_is_in_hf(pr_author, codeowners_lines):
-        print(f"PR author {pr_author} is in codeowners, skipping review request.")
-        return
-
-    existing_reviews = list(pr.get_reviews())
-    if existing_reviews:
-        print(f"Already has reviews: {[r.user.login for r in existing_reviews]}")
-        return
-
-    users_requested, teams_requested = pr.get_review_requests()
-    users_requested = list(users_requested)
-    if users_requested:
-        print(f"Reviewers already requested: {users_requested}")
-        return
-
-    locs_per_owner = Counter()
-    for file in pr.get_files():
-        owners = get_file_owners(file.filename, codeowners_lines)
-        for owner in owners:
-            locs_per_owner[owner] += file.changes
-
-    # Assign the top 2 based on locs changed as reviewers, but skip the owner if present
-    locs_per_owner.pop(pr_author, None)
-    top_owners = locs_per_owner.most_common(2)
-    print("Top owners", top_owners)
-    top_owners = [owner[0] for owner in top_owners]
-    try:
-        pr.create_review_request(top_owners)
-    except github.GithubException as e:
-        print(f"Failed to request review for {top_owners}: {e}")
-
-
-
-if __name__ == "__main__":
-    main()

.github/scripts/codeowners_for_review_action

@@ -1,370 +0,0 @@
-# Top-level rules are matched only if nothing else matches
-* @Rocketknight1 @ArthurZucker # if no one is pinged based on the other rules, he will do the dispatch
-*.md @stevhliu
-*tokenization* @ArthurZucker
-docs/ @stevhliu
-/benchmark/ @McPatate
-/docker/ @ydshieh @ArthurZucker
-
-# More high-level globs catch cases when specific rules later don't apply
-/src/transformers/models/*/processing* @molbap @yonigozlan @qubvel
-/src/transformers/models/*/image_processing* @qubvel
-/src/transformers/models/*/image_processing_*_fast* @yonigozlan
-
-# Owners of subsections of the library
-/src/transformers/generation/ @gante
-/src/transformers/pipeline/ @Rocketknight1 @yonigozlan
-/src/transformers/integrations/ @SunMarc @MekkCyber @zach-huggingface
-/src/transformers/quantizers/ @SunMarc @MekkCyber
-tests/ @ydshieh
-tests/generation/ @gante
-
-/src/transformers/models/auto/ @ArthurZucker
-/src/transformers/utils/ @ArthurZucker @Rocketknight1
-/src/transformers/loss/ @ArthurZucker
-/src/transformers/onnx/ @michaelbenayoun
-
-# Specific files come after the sections/globs, so they take priority
-/.circleci/config.yml @ArthurZucker @ydshieh
-/utils/tests_fetcher.py @ydshieh
-trainer.py @zach-huggingface @SunMarc
-trainer_utils.py @zach-huggingface @SunMarc
-/utils/modular_model_converter.py @Cyrilvallez @ArthurZucker
-
-# Owners of individual models are specific / high priority, and so they come last
-# mod* captures modeling and modular files
-
-# Text models
-/src/transformers/models/albert/mod*_albert* @ArthurZucker
-/src/transformers/models/bamba/mod*_bamba* @ArthurZucker
-/src/transformers/models/bart/mod*_bart* @ArthurZucker
-/src/transformers/models/barthez/mod*_barthez* @ArthurZucker
-/src/transformers/models/bartpho/mod*_bartpho* @ArthurZucker
-/src/transformers/models/bert/mod*_bert* @ArthurZucker
-/src/transformers/models/bert_generation/mod*_bert_generation* @ArthurZucker
-/src/transformers/models/bert_japanese/mod*_bert_japanese* @ArthurZucker
-/src/transformers/models/bertweet/mod*_bertweet* @ArthurZucker
-/src/transformers/models/big_bird/mod*_big_bird* @ArthurZucker
-/src/transformers/models/bigbird_pegasus/mod*_bigbird_pegasus* @ArthurZucker
-/src/transformers/models/biogpt/mod*_biogpt* @ArthurZucker
-/src/transformers/models/blenderbot/mod*_blenderbot* @ArthurZucker
-/src/transformers/models/blenderbot_small/mod*_blenderbot_small* @ArthurZucker
-/src/transformers/models/bloom/mod*_bloom* @ArthurZucker
-/src/transformers/models/bort/mod*_bort* @ArthurZucker
-/src/transformers/models/byt5/mod*_byt5* @ArthurZucker
-/src/transformers/models/camembert/mod*_camembert* @ArthurZucker
-/src/transformers/models/canine/mod*_canine* @ArthurZucker
-/src/transformers/models/codegen/mod*_codegen* @ArthurZucker
-/src/transformers/models/code_llama/mod*_code_llama* @ArthurZucker
-/src/transformers/models/cohere/mod*_cohere* @ArthurZucker
-/src/transformers/models/cohere2/mod*_cohere2* @ArthurZucker
-/src/transformers/models/convbert/mod*_convbert* @ArthurZucker
-/src/transformers/models/cpm/mod*_cpm* @ArthurZucker
-/src/transformers/models/cpmant/mod*_cpmant* @ArthurZucker
-/src/transformers/models/ctrl/mod*_ctrl* @ArthurZucker
-/src/transformers/models/dbrx/mod*_dbrx* @ArthurZucker
-/src/transformers/models/deberta/mod*_deberta* @ArthurZucker
-/src/transformers/models/deberta_v2/mod*_deberta_v2* @ArthurZucker
-/src/transformers/models/dialogpt/mod*_dialogpt* @ArthurZucker
-/src/transformers/models/diffllama/mod*_diffllama* @ArthurZucker
-/src/transformers/models/distilbert/mod*_distilbert* @ArthurZucker
-/src/transformers/models/dpr/mod*_dpr* @ArthurZucker
-/src/transformers/models/electra/mod*_electra* @ArthurZucker
-/src/transformers/models/encoder_decoder/mod*_encoder_decoder* @ArthurZucker
-/src/transformers/models/ernie/mod*_ernie* @ArthurZucker
-/src/transformers/models/ernie_m/mod*_ernie_m* @ArthurZucker
-/src/transformers/models/esm/mod*_esm* @ArthurZucker
-/src/transformers/models/falcon/mod*_falcon* @ArthurZucker
-/src/transformers/models/falcon3/mod*_falcon3* @ArthurZucker
-/src/transformers/models/falcon_mamba/mod*_falcon_mamba* @ArthurZucker
-/src/transformers/models/fastspeech2_conformer/mod*_fastspeech2_conformer* @ArthurZucker
-/src/transformers/models/flan_t5/mod*_flan_t5* @ArthurZucker
-/src/transformers/models/flan_ul2/mod*_flan_ul2* @ArthurZucker
-/src/transformers/models/flaubert/mod*_flaubert* @ArthurZucker
-/src/transformers/models/fnet/mod*_fnet* @ArthurZucker
-/src/transformers/models/fsmt/mod*_fsmt* @ArthurZucker
-/src/transformers/models/funnel/mod*_funnel* @ArthurZucker
-/src/transformers/models/fuyu/mod*_fuyu* @ArthurZucker
-/src/transformers/models/gemma/mod*_gemma* @ArthurZucker
-/src/transformers/models/gemma2/mod*_gemma2* @ArthurZucker
-/src/transformers/models/glm/mod*_glm* @ArthurZucker
-/src/transformers/models/openai_gpt/mod*_openai_gpt* @ArthurZucker
-/src/transformers/models/gpt_neo/mod*_gpt_neo* @ArthurZucker
-/src/transformers/models/gpt_neox/mod*_gpt_neox* @ArthurZucker
-/src/transformers/models/gpt_neox_japanese/mod*_gpt_neox_japanese* @ArthurZucker
-/src/transformers/models/gptj/mod*_gptj* @ArthurZucker
-/src/transformers/models/gpt2/mod*_gpt2* @ArthurZucker
-/src/transformers/models/gpt_bigcode/mod*_gpt_bigcode* @ArthurZucker
-/src/transformers/models/gptsan_japanese/mod*_gptsan_japanese* @ArthurZucker
-/src/transformers/models/gpt_sw3/mod*_gpt_sw3* @ArthurZucker
-/src/transformers/models/granite/mod*_granite* @ArthurZucker
-/src/transformers/models/granitemoe/mod*_granitemoe* @ArthurZucker
-/src/transformers/models/herbert/mod*_herbert* @ArthurZucker
-/src/transformers/models/ibert/mod*_ibert* @ArthurZucker
-/src/transformers/models/jamba/mod*_jamba* @ArthurZucker
-/src/transformers/models/jetmoe/mod*_jetmoe* @ArthurZucker
-/src/transformers/models/jukebox/mod*_jukebox* @ArthurZucker
-/src/transformers/models/led/mod*_led* @ArthurZucker
-/src/transformers/models/llama/mod*_llama* @ArthurZucker @Cyrilvallez
-/src/transformers/models/longformer/mod*_longformer* @ArthurZucker
-/src/transformers/models/longt5/mod*_longt5* @ArthurZucker
-/src/transformers/models/luke/mod*_luke* @ArthurZucker
-/src/transformers/models/m2m_100/mod*_m2m_100* @ArthurZucker
-/src/transformers/models/madlad_400/mod*_madlad_400* @ArthurZucker
-/src/transformers/models/mamba/mod*_mamba* @ArthurZucker
-/src/transformers/models/mamba2/mod*_mamba2* @ArthurZucker
-/src/transformers/models/marian/mod*_marian* @ArthurZucker
-/src/transformers/models/markuplm/mod*_markuplm* @ArthurZucker
-/src/transformers/models/mbart/mod*_mbart* @ArthurZucker
-/src/transformers/models/mega/mod*_mega* @ArthurZucker
-/src/transformers/models/megatron_bert/mod*_megatron_bert* @ArthurZucker
-/src/transformers/models/megatron_gpt2/mod*_megatron_gpt2* @ArthurZucker
-/src/transformers/models/mistral/mod*_mistral* @ArthurZucker
-/src/transformers/models/mixtral/mod*_mixtral* @ArthurZucker
-/src/transformers/models/mluke/mod*_mluke* @ArthurZucker
-/src/transformers/models/mobilebert/mod*_mobilebert* @ArthurZucker
-/src/transformers/models/modernbert/mod*_modernbert* @ArthurZucker
-/src/transformers/models/mpnet/mod*_mpnet* @ArthurZucker
-/src/transformers/models/mpt/mod*_mpt* @ArthurZucker
-/src/transformers/models/mra/mod*_mra* @ArthurZucker
-/src/transformers/models/mt5/mod*_mt5* @ArthurZucker
-/src/transformers/models/mvp/mod*_mvp* @ArthurZucker
-/src/transformers/models/myt5/mod*_myt5* @ArthurZucker
-/src/transformers/models/nemotron/mod*_nemotron* @ArthurZucker
-/src/transformers/models/nezha/mod*_nezha* @ArthurZucker
-/src/transformers/models/nllb/mod*_nllb* @ArthurZucker
-/src/transformers/models/nllb_moe/mod*_nllb_moe* @ArthurZucker
-/src/transformers/models/nystromformer/mod*_nystromformer* @ArthurZucker
-/src/transformers/models/olmo/mod*_olmo* @ArthurZucker
-/src/transformers/models/olmo2/mod*_olmo2* @ArthurZucker
-/src/transformers/models/olmoe/mod*_olmoe* @ArthurZucker
-/src/transformers/models/open_llama/mod*_open_llama* @ArthurZucker
-/src/transformers/models/opt/mod*_opt* @ArthurZucker
-/src/transformers/models/pegasus/mod*_pegasus* @ArthurZucker
-/src/transformers/models/pegasus_x/mod*_pegasus_x* @ArthurZucker
-/src/transformers/models/persimmon/mod*_persimmon* @ArthurZucker
-/src/transformers/models/phi/mod*_phi* @ArthurZucker
-/src/transformers/models/phi3/mod*_phi3* @ArthurZucker
-/src/transformers/models/phimoe/mod*_phimoe* @ArthurZucker
-/src/transformers/models/phobert/mod*_phobert* @ArthurZucker
-/src/transformers/models/plbart/mod*_plbart* @ArthurZucker
-/src/transformers/models/prophetnet/mod*_prophetnet* @ArthurZucker
-/src/transformers/models/qdqbert/mod*_qdqbert* @ArthurZucker
-/src/transformers/models/qwen2/mod*_qwen2* @ArthurZucker
-/src/transformers/models/qwen2_moe/mod*_qwen2_moe* @ArthurZucker
-/src/transformers/models/rag/mod*_rag* @ArthurZucker
-/src/transformers/models/realm/mod*_realm* @ArthurZucker
-/src/transformers/models/recurrent_gemma/mod*_recurrent_gemma* @ArthurZucker
-/src/transformers/models/reformer/mod*_reformer* @ArthurZucker
-/src/transformers/models/rembert/mod*_rembert* @ArthurZucker
-/src/transformers/models/retribert/mod*_retribert* @ArthurZucker
-/src/transformers/models/roberta/mod*_roberta* @ArthurZucker
-/src/transformers/models/roberta_prelayernorm/mod*_roberta_prelayernorm* @ArthurZucker
-/src/transformers/models/roc_bert/mod*_roc_bert* @ArthurZucker
-/src/transformers/models/roformer/mod*_roformer* @ArthurZucker
-/src/transformers/models/rwkv/mod*_rwkv* @ArthurZucker
-/src/transformers/models/splinter/mod*_splinter* @ArthurZucker
-/src/transformers/models/squeezebert/mod*_squeezebert* @ArthurZucker
-/src/transformers/models/stablelm/mod*_stablelm* @ArthurZucker
-/src/transformers/models/starcoder2/mod*_starcoder2* @ArthurZucker
-/src/transformers/models/switch_transformers/mod*_switch_transformers* @ArthurZucker
-/src/transformers/models/t5/mod*_t5* @ArthurZucker
-/src/transformers/models/t5v1.1/mod*_t5v1.1* @ArthurZucker
-/src/transformers/models/tapex/mod*_tapex* @ArthurZucker
-/src/transformers/models/transfo_xl/mod*_transfo_xl* @ArthurZucker
-/src/transformers/models/ul2/mod*_ul2* @ArthurZucker
-/src/transformers/models/umt5/mod*_umt5* @ArthurZucker
-/src/transformers/models/xmod/mod*_xmod* @ArthurZucker
-/src/transformers/models/xglm/mod*_xglm* @ArthurZucker
-/src/transformers/models/xlm/mod*_xlm* @ArthurZucker
-/src/transformers/models/xlm_prophetnet/mod*_xlm_prophetnet* @ArthurZucker
-/src/transformers/models/xlm_roberta/mod*_xlm_roberta* @ArthurZucker
-/src/transformers/models/xlm_roberta_xl/mod*_xlm_roberta_xl* @ArthurZucker
-/src/transformers/models/xlm_v/mod*_xlm_v* @ArthurZucker
-/src/transformers/models/xlnet/mod*_xlnet* @ArthurZucker
-/src/transformers/models/yoso/mod*_yoso* @ArthurZucker
-/src/transformers/models/zamba/mod*_zamba* @ArthurZucker
-
-# Vision models
-/src/transformers/models/beit/mod*_beit* @amyeroberts @qubvel
-/src/transformers/models/bit/mod*_bit* @amyeroberts @qubvel
-/src/transformers/models/conditional_detr/mod*_conditional_detr* @amyeroberts @qubvel
-/src/transformers/models/convnext/mod*_convnext* @amyeroberts @qubvel
-/src/transformers/models/convnextv2/mod*_convnextv2* @amyeroberts @qubvel
-/src/transformers/models/cvt/mod*_cvt* @amyeroberts @qubvel
-/src/transformers/models/deformable_detr/mod*_deformable_detr* @amyeroberts @qubvel
-/src/transformers/models/deit/mod*_deit* @amyeroberts @qubvel
-/src/transformers/models/depth_anything/mod*_depth_anything* @amyeroberts @qubvel
-/src/transformers/models/depth_anything_v2/mod*_depth_anything_v2* @amyeroberts @qubvel
-/src/transformers/models/deta/mod*_deta* @amyeroberts @qubvel
-/src/transformers/models/detr/mod*_detr* @amyeroberts @qubvel
-/src/transformers/models/dinat/mod*_dinat* @amyeroberts @qubvel
-/src/transformers/models/dinov2/mod*_dinov2* @amyeroberts @qubvel
-/src/transformers/models/dinov2_with_registers/mod*_dinov2_with_registers* @amyeroberts @qubvel
-/src/transformers/models/dit/mod*_dit* @amyeroberts @qubvel
-/src/transformers/models/dpt/mod*_dpt* @amyeroberts @qubvel
-/src/transformers/models/efficientformer/mod*_efficientformer* @amyeroberts @qubvel
-/src/transformers/models/efficientnet/mod*_efficientnet* @amyeroberts @qubvel
-/src/transformers/models/focalnet/mod*_focalnet* @amyeroberts @qubvel
-/src/transformers/models/glpn/mod*_glpn* @amyeroberts @qubvel
-/src/transformers/models/hiera/mod*_hiera* @amyeroberts @qubvel
-/src/transformers/models/ijepa/mod*_ijepa* @amyeroberts @qubvel
-/src/transformers/models/imagegpt/mod*_imagegpt* @amyeroberts @qubvel
-/src/transformers/models/levit/mod*_levit* @amyeroberts @qubvel
-/src/transformers/models/mask2former/mod*_mask2former* @amyeroberts @qubvel
-/src/transformers/models/maskformer/mod*_maskformer* @amyeroberts @qubvel
-/src/transformers/models/mobilenet_v1/mod*_mobilenet_v1* @amyeroberts @qubvel
-/src/transformers/models/mobilenet_v2/mod*_mobilenet_v2* @amyeroberts @qubvel
-/src/transformers/models/mobilevit/mod*_mobilevit* @amyeroberts @qubvel
-/src/transformers/models/mobilevitv2/mod*_mobilevitv2* @amyeroberts @qubvel
-/src/transformers/models/nat/mod*_nat* @amyeroberts @qubvel
-/src/transformers/models/poolformer/mod*_poolformer* @amyeroberts @qubvel
-/src/transformers/models/pvt/mod*_pvt* @amyeroberts @qubvel
-/src/transformers/models/pvt_v2/mod*_pvt_v2* @amyeroberts @qubvel
-/src/transformers/models/regnet/mod*_regnet* @amyeroberts @qubvel
-/src/transformers/models/resnet/mod*_resnet* @amyeroberts @qubvel
-/src/transformers/models/rt_detr/mod*_rt_detr* @amyeroberts @qubvel
-/src/transformers/models/segformer/mod*_segformer* @amyeroberts @qubvel
-/src/transformers/models/seggpt/mod*_seggpt* @amyeroberts @qubvel
-/src/transformers/models/superpoint/mod*_superpoint* @amyeroberts @qubvel
-/src/transformers/models/swiftformer/mod*_swiftformer* @amyeroberts @qubvel
-/src/transformers/models/swin/mod*_swin* @amyeroberts @qubvel
-/src/transformers/models/swinv2/mod*_swinv2* @amyeroberts @qubvel
-/src/transformers/models/swin2sr/mod*_swin2sr* @amyeroberts @qubvel
-/src/transformers/models/table_transformer/mod*_table_transformer* @amyeroberts @qubvel
-/src/transformers/models/textnet/mod*_textnet* @amyeroberts @qubvel
-/src/transformers/models/timm_wrapper/mod*_timm_wrapper* @amyeroberts @qubvel
-/src/transformers/models/upernet/mod*_upernet* @amyeroberts @qubvel
-/src/transformers/models/van/mod*_van* @amyeroberts @qubvel
-/src/transformers/models/vit/mod*_vit* @amyeroberts @qubvel
-/src/transformers/models/vit_hybrid/mod*_vit_hybrid* @amyeroberts @qubvel
-/src/transformers/models/vitdet/mod*_vitdet* @amyeroberts @qubvel
-/src/transformers/models/vit_mae/mod*_vit_mae* @amyeroberts @qubvel
-/src/transformers/models/vitmatte/mod*_vitmatte* @amyeroberts @qubvel
-/src/transformers/models/vit_msn/mod*_vit_msn* @amyeroberts @qubvel
-/src/transformers/models/vitpose/mod*_vitpose* @amyeroberts @qubvel
-/src/transformers/models/yolos/mod*_yolos* @amyeroberts @qubvel
-/src/transformers/models/zoedepth/mod*_zoedepth* @amyeroberts @qubvel
-
-# Audio models
-/src/transformers/models/audio_spectrogram_transformer/mod*_audio_spectrogram_transformer* @eustlb
-/src/transformers/models/bark/mod*_bark* @eustlb
-/src/transformers/models/clap/mod*_clap* @eustlb
-/src/transformers/models/dac/mod*_dac* @eustlb
-/src/transformers/models/encodec/mod*_encodec* @eustlb
-/src/transformers/models/hubert/mod*_hubert* @eustlb
-/src/transformers/models/mctct/mod*_mctct* @eustlb
-/src/transformers/models/mimi/mod*_mimi* @eustlb
-/src/transformers/models/mms/mod*_mms* @eustlb
-/src/transformers/models/moshi/mod*_moshi* @eustlb
-/src/transformers/models/musicgen/mod*_musicgen* @eustlb
-/src/transformers/models/musicgen_melody/mod*_musicgen_melody* @eustlb
-/src/transformers/models/pop2piano/mod*_pop2piano* @eustlb
-/src/transformers/models/seamless_m4t/mod*_seamless_m4t* @eustlb
-/src/transformers/models/seamless_m4t_v2/mod*_seamless_m4t_v2* @eustlb
-/src/transformers/models/sew/mod*_sew* @eustlb
-/src/transformers/models/sew_d/mod*_sew_d* @eustlb
-/src/transformers/models/speech_to_text/mod*_speech_to_text* @eustlb
-/src/transformers/models/speech_to_text_2/mod*_speech_to_text_2* @eustlb
-/src/transformers/models/speecht5/mod*_speecht5* @eustlb
-/src/transformers/models/unispeech/mod*_unispeech* @eustlb
-/src/transformers/models/unispeech_sat/mod*_unispeech_sat* @eustlb
-/src/transformers/models/univnet/mod*_univnet* @eustlb
-/src/transformers/models/vits/mod*_vits* @eustlb
-/src/transformers/models/wav2vec2/mod*_wav2vec2* @eustlb
-/src/transformers/models/wav2vec2_bert/mod*_wav2vec2_bert* @eustlb
-/src/transformers/models/wav2vec2_conformer/mod*_wav2vec2_conformer* @eustlb
-/src/transformers/models/wav2vec2_phoneme/mod*_wav2vec2_phoneme* @eustlb
-/src/transformers/models/wavlm/mod*_wavlm* @eustlb
-/src/transformers/models/whisper/mod*_whisper* @eustlb
-/src/transformers/models/xls_r/mod*_xls_r* @eustlb
-/src/transformers/models/xlsr_wav2vec2/mod*_xlsr_wav2vec2* @eustlb
-
-# Video models
-/src/transformers/models/timesformer/mod*_timesformer* @Rocketknight1
-/src/transformers/models/videomae/mod*_videomae* @Rocketknight1
-/src/transformers/models/vivit/mod*_vivit* @Rocketknight1
-
-# Multimodal models
-/src/transformers/models/align/mod*_align* @zucchini-nlp
-/src/transformers/models/altclip/mod*_altclip* @zucchini-nlp
-/src/transformers/models/aria/mod*_aria* @zucchini-nlp
-/src/transformers/models/blip/mod*_blip* @zucchini-nlp
-/src/transformers/models/blip_2/mod*_blip_2* @zucchini-nlp
-/src/transformers/models/bridgetower/mod*_bridgetower* @zucchini-nlp
-/src/transformers/models/bros/mod*_bros* @zucchini-nlp
-/src/transformers/models/chameleon/mod*_chameleon* @zucchini-nlp
-/src/transformers/models/chinese_clip/mod*_chinese_clip* @zucchini-nlp
-/src/transformers/models/clip/mod*_clip* @zucchini-nlp
-/src/transformers/models/clipseg/mod*_clipseg* @zucchini-nlp
-/src/transformers/models/clvp/mod*_clvp* @zucchini-nlp
-/src/transformers/models/colpali/mod*_colpali* @zucchini-nlp @yonigozlan
-/src/transformers/models/data2vec/mod*_data2vec* @zucchini-nlp
-/src/transformers/models/deplot/mod*_deplot* @zucchini-nlp
-/src/transformers/models/donut/mod*_donut* @zucchini-nlp
-/src/transformers/models/flava/mod*_flava* @zucchini-nlp
-/src/transformers/models/git/mod*_git* @zucchini-nlp
-/src/transformers/models/grounding_dino/mod*_grounding_dino* @qubvel
-/src/transformers/models/groupvit/mod*_groupvit* @zucchini-nlp
-/src/transformers/models/idefics/mod*_idefics* @zucchini-nlp
-/src/transformers/models/idefics2/mod*_idefics2* @zucchini-nlp
-/src/transformers/models/idefics3/mod*_idefics3* @zucchini-nlp
-/src/transformers/models/instructblip/mod*_instructblip* @zucchini-nlp
-/src/transformers/models/instructblipvideo/mod*_instructblipvideo* @zucchini-nlp
-/src/transformers/models/kosmos_2/mod*_kosmos_2* @zucchini-nlp
-/src/transformers/models/layoutlm/mod*_layoutlm* @NielsRogge
-/src/transformers/models/layoutlmv2/mod*_layoutlmv2* @NielsRogge
-/src/transformers/models/layoutlmv3/mod*_layoutlmv3* @NielsRogge
-/src/transformers/models/layoutxlm/mod*_layoutxlm* @NielsRogge
-/src/transformers/models/lilt/mod*_lilt* @zucchini-nlp
-/src/transformers/models/llava/mod*_llava* @zucchini-nlp @arthurzucker
-/src/transformers/models/llava_next/mod*_llava_next* @zucchini-nlp
-/src/transformers/models/llava_next_video/mod*_llava_next_video* @zucchini-nlp
-/src/transformers/models/llava_onevision/mod*_llava_onevision* @zucchini-nlp
-/src/transformers/models/lxmert/mod*_lxmert* @zucchini-nlp
-/src/transformers/models/matcha/mod*_matcha* @zucchini-nlp
-/src/transformers/models/mgp_str/mod*_mgp_str* @zucchini-nlp
-/src/transformers/models/mllama/mod*_mllama* @zucchini-nlp
-/src/transformers/models/nougat/mod*_nougat* @NielsRogge
-/src/transformers/models/omdet_turbo/mod*_omdet_turbo* @qubvel @yonigozlan
-/src/transformers/models/oneformer/mod*_oneformer* @zucchini-nlp
-/src/transformers/models/owlvit/mod*_owlvit* @qubvel
-/src/transformers/models/owlv2/mod*_owlv2* @qubvel
-/src/transformers/models/paligemma/mod*_paligemma* @zucchini-nlp @molbap
-/src/transformers/models/perceiver/mod*_perceiver* @zucchini-nlp
-/src/transformers/models/pix2struct/mod*_pix2struct* @zucchini-nlp
-/src/transformers/models/pixtral/mod*_pixtral* @zucchini-nlp @ArthurZucker
-/src/transformers/models/qwen2_audio/mod*_qwen2_audio* @zucchini-nlp @ArthurZucker
-/src/transformers/models/qwen2_vl/mod*_qwen2_vl* @zucchini-nlp @ArthurZucker
-/src/transformers/models/sam/mod*_sam* @zucchini-nlp @ArthurZucker
-/src/transformers/models/siglip/mod*_siglip* @zucchini-nlp
-/src/transformers/models/speech_encoder_decoder/mod*_speech_encoder_decoder* @zucchini-nlp
-/src/transformers/models/tapas/mod*_tapas* @NielsRogge
-/src/transformers/models/trocr/mod*_trocr* @zucchini-nlp
-/src/transformers/models/tvlt/mod*_tvlt* @zucchini-nlp
-/src/transformers/models/tvp/mod*_tvp* @zucchini-nlp
-/src/transformers/models/udop/mod*_udop* @zucchini-nlp
-/src/transformers/models/video_llava/mod*_video_llava* @zucchini-nlp
-/src/transformers/models/vilt/mod*_vilt* @zucchini-nlp
-/src/transformers/models/vipllava/mod*_vipllava* @zucchini-nlp
-/src/transformers/models/vision_encoder_decoder/mod*_vision_encoder_decoder* @Rocketknight1
-/src/transformers/models/vision_text_dual_encoder/mod*_vision_text_dual_encoder* @Rocketknight1
-/src/transformers/models/visual_bert/mod*_visual_bert* @zucchini-nlp
-/src/transformers/models/xclip/mod*_xclip* @zucchini-nlp
-
-# Reinforcement learning models
-/src/transformers/models/decision_transformer/mod*_decision_transformer* @Rocketknight1
-/src/transformers/models/trajectory_transformer/mod*_trajectory_transformer* @Rocketknight1
-
-# Time series models
-/src/transformers/models/autoformer/mod*_autoformer* @Rocketknight1
-/src/transformers/models/informer/mod*_informer* @Rocketknight1
-/src/transformers/models/patchtsmixer/mod*_patchtsmixer* @Rocketknight1
-/src/transformers/models/patchtst/mod*_patchtst* @Rocketknight1
-/src/transformers/models/time_series_transformer/mod*_time_series_transformer* @Rocketknight1
-
-# Graph models
-/src/transformers/models/graphormer/mod*_graphormer* @clefourrier
-
-# Finally, files with no owners that shouldn't generate pings, usually automatically generated and checked in the CI
-utils/dummy*

.github/workflows/add-model-like.yml

@@ -54,7 +54,7 @@ jobs:
       - name: Create model files
         run: |
           . ~/venv/bin/activate
-          transformers add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
+          transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
           make style
           make fix-copies
 

.github/workflows/assign-reviewers.yml

@@ -1,26 +0,0 @@
-name: Assign PR Reviewers
-on:
-  pull_request_target:
-    branches:
-      - main
-    types: [ready_for_review]
-
-jobs:
-  assign_reviewers:
-    permissions:
-       pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.13'
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install PyGithub
-      - name: Run assignment script
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: python .github/scripts/assign_reviewers.py

.github/workflows/benchmark.yml

@@ -1,76 +1,42 @@
 name: Self-hosted runner (benchmark)
 
 on:
-  push:
-    branches: [main]
-  pull_request:
-    types: [ opened, labeled, reopened, synchronize ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
+  schedule:
+    - cron: "17 2 * * *"
+  workflow_call:
 
 env:
   HF_HOME: /mnt/cache
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+
 
 jobs:
   benchmark:
     name: Benchmark
-    strategy:
-      matrix:
-        # group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus] (A100 runner is not enabled)
-        group: [aws-g5-4xlarge-cache]
-    runs-on:
-      group: ${{ matrix.group }}
-    if: |
-      (github.event_name == 'pull_request' && contains( github.event.pull_request.labels.*.name, 'run-benchmark') )||
-      (github.event_name == 'push' && github.ref == 'refs/heads/main')
+    runs-on: [single-gpu, nvidia-gpu, a10, ci]
     container:
-      image: huggingface/transformers-pytorch-gpu
-      options: --gpus all --privileged --ipc host
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
-      - name: Get repo
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.pull_request.head.sha || github.sha }}
-
-      - name: Install libpq-dev & psql
+      - name: Update clone
+        working-directory: /transformers
         run: |
-          apt update
-          apt install -y libpq-dev postgresql-client
-
-      - name: Install benchmark script dependencies
-        run: python3 -m pip install -r benchmark/requirements.txt
+          git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]"
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
 
-      - name: Run database init script
+      - name: Benchmark (daily)
+        if: github.event_name == 'schedule'
+        working-directory: /transformers
         run: |
-          psql -f benchmark/init_db.sql
-        env:
-          PGDATABASE: metrics
-          PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
-          PGUSER: transformers_benchmarks
-          PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
+          python3 -m pip install optimum-benchmark>=0.3.0
+          HF_TOKEN=${{ secrets.TRANSFORMERS_BENCHMARK_TOKEN }} python3 benchmark/benchmark.py --repo_id hf-internal-testing/benchmark_results --path_in_repo $(date +'%Y-%m-%d') --config-dir benchmark/config --config-name generation --commit=${{ github.sha }} backend.model=google/gemma-2b backend.cache_implementation=null,static backend.torch_compile=false,true --multirun
 
-      - name: Run benchmark
+      - name: Benchmark (merged to main event)
+        if: github.event_name == 'push' && github.ref_name == 'main'
+        working-directory: /transformers
         run: |
-          git config --global --add safe.directory /__w/transformers/transformers
-          if [ "$GITHUB_EVENT_NAME" = "pull_request" ]; then
-            commit_id=$(echo "${{ github.event.pull_request.head.sha }}")
-          elif [ "$GITHUB_EVENT_NAME" = "push" ]; then
-            commit_id=$GITHUB_SHA
-          fi
-          commit_msg=$(git show -s --format=%s | cut -c1-70)
-          python3 benchmark/benchmarks_entrypoint.py "huggingface/transformers" "$BRANCH_NAME" "$commit_id" "$commit_msg"
-        env:
-          HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-          # Enable this to see debug logs
-          # HF_HUB_VERBOSITY: debug
-          # TRANSFORMERS_VERBOSITY: debug
-          PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
-          PGUSER: transformers_benchmarks
-          PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+          python3 -m pip install optimum-benchmark>=0.3.0
+          HF_TOKEN=${{ secrets.TRANSFORMERS_BENCHMARK_TOKEN }} python3 benchmark/benchmark.py --repo_id hf-internal-testing/benchmark_results_merge_event --path_in_repo $(date +'%Y-%m-%d') --config-dir benchmark/config --config-name generation --commit=${{ github.sha }} backend.model=google/gemma-2b backend.cache_implementation=null,static backend.torch_compile=false,true --multirun

.github/workflows/build-ci-docker-images.yml

@@ -26,7 +26,7 @@ jobs:
 
     strategy:
       matrix:
-        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "jax-light", "examples-torch",  "examples-tf"]
+        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "torch-jax-light", "jax-light", "examples-torch",  "examples-tf"]
     continue-on-error: true
 
     steps:

.github/workflows/build-docker-images.yml

@@ -19,9 +19,8 @@ concurrency:
 
 jobs:
   latest-docker:
-    name: "Latest PyTorch [dev]"
-    runs-on:
-      group: aws-general-8-plus
+    name: "Latest PyTorch + TensorFlow [dev]"
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -69,8 +68,7 @@ jobs:
 
   latest-torch-deepspeed-docker:
     name: "Latest PyTorch + DeepSpeed"
-    runs-on:
-      group: aws-g4dn-2xlarge-cache
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -106,8 +104,7 @@ jobs:
   # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`)
   latest-torch-deepspeed-docker-for-push-ci-daily-build:
     name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)"
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -148,8 +145,7 @@ jobs:
     name: "Doc builder"
     # Push CI doesn't need this image
     if: inputs.image_postfix != '-push-ci'
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -184,8 +180,7 @@ jobs:
     name: "Latest PyTorch [dev]"
     # Push CI doesn't need this image
     if: inputs.image_postfix != '-push-ci'
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -220,8 +215,7 @@ jobs:
 
   latest-pytorch-amd:
     name: "Latest PyTorch (AMD) [dev]"
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       - 
         name: Set up Docker Buildx
@@ -267,10 +261,46 @@ jobs:
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
+  latest-tensorflow:
+    name: "Latest TensorFlow [dev]"
+    # Push CI doesn't need this image
+    if: inputs.image_postfix != '-push-ci'
+    runs-on: [intel-cpu, 8-cpu, ci]
+    steps:
+      -
+        name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      -
+        name: Check out code
+        uses: actions/checkout@v4
+      -
+        name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      -
+        name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: ./docker/transformers-tensorflow-gpu
+          build-args: |
+            REF=main
+          push: true
+          tags: huggingface/transformers-tensorflow-gpu
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
+          title: 🤗 Results of the huggingface/transformers-tensorflow-gpu build 
+          status: ${{ job.status }}
+          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+
   latest-pytorch-deepspeed-amd:
     name: "PyTorch + DeepSpeed (AMD) [dev]"
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       - 
         name: Set up Docker Buildx
@@ -320,8 +350,7 @@ jobs:
     name: "Latest Pytorch + Quantization [dev]"
      # Push CI doesn't need this image
     if: inputs.image_postfix != '-push-ci'
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx

.github/workflows/build-nightly-ci-docker-images.yml

@@ -13,8 +13,7 @@ concurrency:
 jobs:
   latest-with-torch-nightly-docker:
     name: "Nightly PyTorch + Stable TensorFlow"
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -41,8 +40,7 @@ jobs:
 
   nightly-torch-deepspeed-docker:
     name: "Nightly PyTorch + DeepSpeed"
-    runs-on:
-      group: aws-g4dn-2xlarge-cache
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx

.github/workflows/build-past-ci-docker-images.yml

@@ -16,8 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         version: ["1.13", "1.12", "1.11"]
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx
@@ -61,8 +60,7 @@ jobs:
       fail-fast: false
       matrix:
         version: ["2.11", "2.10", "2.9", "2.8", "2.7", "2.6", "2.5"]
-    runs-on:
-      group: aws-general-8-plus
+    runs-on: [intel-cpu, 8-cpu, ci]
     steps:
       -
         name: Set up Docker Buildx

.github/workflows/build_documentation.yml

@@ -1,7 +1,6 @@
 name: Build documentation
 
 on:
-  workflow_dispatch:
   push:
     branches:
       - main
@@ -16,7 +15,7 @@ jobs:
       commit_sha: ${{ github.sha }}
       package: transformers
       notebook_folder: transformers_doc
-      languages: ar de en es fr hi it ko pt tr zh ja te
+      languages: de en es fr hi it ko pt tr zh ja te
       custom_container: huggingface/transformers-doc-builder
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -14,4 +14,5 @@ jobs:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}
       package: transformers
-      languages: en
+      languages: de en es fr hi it ko pt tr zh ja te
+      custom_container: huggingface/transformers-doc-builder

.github/workflows/check_failed_tests.yml

@@ -1,205 +0,0 @@
-name: Process failed tests
-
-on:
-  workflow_call:
-    inputs:
-      docker:
-        required: true
-        type: string
-      start_sha:
-        required: true
-        type: string
-      job:
-        required: true
-        type: string
-      slack_report_channel:
-        required: true
-        type: string
-      ci_event:
-        required: true
-        type: string
-      report_repo_id:
-        required: true
-        type: string
-
-
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  RUN_SLOW: yes
-  # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
-  # This token is created under the bot `hf-transformers-bot`.
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  TF_FORCE_GPU_ALLOW_GROWTH: true
-  CUDA_VISIBLE_DEVICES: 0,1
-
-
-jobs:
-  check_new_failures:
-    name: " "
-    runs-on:
-      group: aws-g5-4xlarge-cache
-    container:
-      image: ${{ inputs.docker }}
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - uses: actions/download-artifact@v4
-        with:
-          name: ci_results_${{ inputs.job }}
-          path: /transformers/ci_results_${{ inputs.job }}
-
-      - name: Check file
-        working-directory: /transformers
-        run: |
-          if [ -f ci_results_${{ inputs.job }}/new_failures.json ]; then
-            echo "`ci_results_${{ inputs.job }}/new_failures.json` exists, continue ..."
-            echo "process=true" >> $GITHUB_ENV
-          else
-            echo "`ci_results_${{ inputs.job }}/new_failures.json` doesn't exist, abort."
-            echo "process=false" >> $GITHUB_ENV
-          fi
-
-      - uses: actions/download-artifact@v4
-        if: ${{ env.process == 'true' }}
-        with:
-          pattern: setup_values*
-          path: setup_values
-          merge-multiple: true
-
-      - name: Prepare some setup values
-        if: ${{ env.process == 'true' }}
-        run: |
-          if [ -f setup_values/prev_workflow_run_id.txt ]; then
-            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
-          else
-            echo "PREV_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
-          fi
-
-          if [ -f setup_values/other_workflow_run_id.txt ]; then
-            echo "OTHER_WORKFLOW_RUN_ID=$(cat setup_values/other_workflow_run_id.txt)" >> $GITHUB_ENV
-          else
-            echo "OTHER_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
-          fi
-
-      - name: Update clone
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: git fetch && git checkout ${{ github.sha }}
-
-      - name: Get target commit
-        working-directory: /transformers/utils
-        if: ${{ env.process == 'true' }}
-        run: |
-          echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"], workflow_run_id=os.environ["PREV_WORKFLOW_RUN_ID"]); print(commit)')" >> $GITHUB_ENV
-
-      - name: Checkout to `start_sha`
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: git fetch && git checkout ${{ inputs.start_sha }}
-
-      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-
-      - name: NVIDIA-SMI
-        if: ${{ env.process == 'true' }}
-        run: |
-          nvidia-smi
-
-      - name: Environment
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: pip freeze
-
-      - name: Check failed tests
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_${{ inputs.job }}/new_failures.json --output_file new_failures_with_bad_commit.json
-
-      - name: Show results
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: |
-          ls -l new_failures_with_bad_commit.json
-          cat new_failures_with_bad_commit.json
-
-      - name: Checkout back
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: |
-          git checkout ${{ inputs.start_sha }}
-
-      - name: Process report
-        shell: bash
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        env:
-          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-          JOB_NAME: ${{ inputs.job }}
-          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
-        run: |
-          python3 utils/process_bad_commit_report.py
-
-      - name: Process report
-        shell: bash
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        env:
-          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-          JOB_NAME: ${{ inputs.job }}
-          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
-        run: |
-          {
-            echo 'REPORT_TEXT<<EOF'
-            python3 utils/process_bad_commit_report.py
-            echo EOF
-          } >> "$GITHUB_ENV"
-
-      - name: Prepare Slack report title
-        working-directory: /transformers
-        if: ${{ env.process == 'true' }}
-        run: |
-          pip install slack_sdk
-          echo "title=$(python3 -c 'import sys; sys.path.append("utils"); from utils.notification_service import job_to_test_map; ci_event = "${{ inputs.ci_event }}"; job = "${{ inputs.job }}"; test_name = job_to_test_map[job]; title = f"New failed tests of {ci_event}" + ":" + f" {test_name}"; print(title)')" >> $GITHUB_ENV
-
-      - name: Send processed report
-        if: ${{ env.process == 'true' && !endsWith(env.REPORT_TEXT, '{}') }}
-        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
-        with:
-          # Slack channel id, channel name, or user id to post message.
-          # See also: https://api.slack.com/methods/chat.postMessage#channels
-          channel-id: '#${{ inputs.slack_report_channel }}'
-          # For posting a rich message using Block Kit
-          payload: |
-            {
-              "blocks": [
-                {
-                  "type": "header",
-                  "text": {
-                    "type": "plain_text",
-                    "text": "${{ env.title }}"
-                  }
-                },
-                {
-                  "type": "section",
-                  "text": {
-                    "type": "mrkdwn",
-                    "text": "${{ env.REPORT_TEXT }}"
-                  }
-                }
-              ]
-            }
-        env:
-          SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/doctest_job.yml

@@ -27,8 +27,7 @@ jobs:
       fail-fast: false
       matrix:
         split_keys: ${{ fromJson(inputs.split_keys) }}
-    runs-on: 
-      group: aws-g5-4xlarge-cache
+    runs-on: [single-gpu, nvidia-gpu, t4, ci]
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

.github/workflows/doctests.yml

@@ -14,8 +14,7 @@ env:
 jobs:
   setup:
     name: Setup
-    runs-on: 
-      group: aws-g5-4xlarge-cache
+    runs-on: [single-gpu, nvidia-gpu, t4, ci]
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

.github/workflows/model_jobs.yml

@@ -12,16 +12,12 @@ on:
       slice_id:
         required: true
         type: number
-      runner_map:
-        required: false
+      runner:
+        required: true
         type: string
       docker:
         required: true
         type: string
-      report_name_prefix:
-        required: false
-        default: run_models_gpu
-        type: string
 
 env:
   HF_HOME: /mnt/cache
@@ -34,6 +30,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -44,8 +41,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
-    runs-on:
-      group: ${{ fromJson(inputs.runner_map)[matrix.folders][inputs.machine_type] }}
+    runs-on: ['${{ inputs.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -97,54 +93,29 @@ jobs:
         run: |
           python3 utils/print_env.py
 
-      - name: Install datasets main
-        working-directory: /transformers
-        run: python3 -m pip install --no-cache-dir git+https://github.com/huggingface/datasets.git@main
-
-      - name: Install torchcodec
-        working-directory: /transformers
-        run: python3 -m pip install --no-cache-dir torch torchvision torchaudio torchcodec --index-url https://download.pytorch.org/whl/cu126
-
       - name: Show installed libraries and their versions
         working-directory: /transformers
         run: pip freeze
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ inputs.machine_type }}"
-
-          if [ "${{ inputs.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ inputs.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ inputs.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Run all tests on GPU
         working-directory: /transformers
-        run: python3 -m pytest -rsfE -v --make-reports=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+        run: python3 -m pytest -rsfE -v --make-reports=${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
 
       - name: Run test
         shell: bash
         run: |
-          mkdir -p /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports"
+          mkdir -p /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+          echo "hello" > /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt
+          echo "${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports"
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports"
+      - name: "Test suite reports artifacts: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
+          name: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports

.github/workflows/model_jobs_intel_gaudi.yml

@@ -1,121 +0,0 @@
-name: model jobs
-
-on:
-  workflow_call:
-    inputs:
-      folder_slices:
-        required: true
-        type: string
-      slice_id:
-        required: true
-        type: number
-      runner:
-        required: true
-        type: string
-      machine_type:
-        required: true
-        type: string
-      report_name_prefix:
-        required: false
-        default: run_models_gpu
-        type: string
-
-env:
-  RUN_SLOW: yes
-  PT_HPU_LAZY_MODE: 0
-  TRANSFORMERS_IS_CI: yes
-  PT_ENABLE_INT64_SUPPORT: 1
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  HF_HOME: /mnt/cache/.cache/huggingface
-
-jobs:
-  run_models_gpu:
-    name: " "
-    strategy:
-      max-parallel: 8
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
-    runs-on:
-      group: ${{ inputs.runner }}
-    container:
-      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
-      options: --runtime=habana
-        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
-        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
-        --env HABANA_VISIBLE_DEVICES
-        --env HABANA_VISIBLE_MODULES
-        --cap-add=sys_nice
-        --shm-size=64G
-    steps:
-      - name: Echo input and matrix info
-        shell: bash
-        run: |
-          echo "${{ inputs.folder_slices }}"
-          echo "${{ matrix.folders }}"
-          echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}"
-
-      - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          matrix_folders=${matrix_folders/'models/'/'models_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
-
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn
-
-      - name: HL-SMI
-        run: |
-          hl-smi
-          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
-          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
-
-      - name: Environment
-        run: python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        run: pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        shell: bash
-        run: |
-          if [ "${{ inputs.machine_type }}" = "1gaudi" ]; then
-            machine_type=single-gpu
-          elif [ "${{ inputs.machine_type }}" = "2gaudi" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ inputs.machine_type }}
-          fi
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Run all tests on Gaudi
-        run: python3 -m pytest -v --make-reports=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/failures_short.txt
-
-      - name: Run test
-        shell: bash
-        run: |
-          mkdir -p reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
-          echo "hello" > reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports"
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-          path: reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports

.github/workflows/new_model_pr_merged_notification.yml

@@ -1,68 +0,0 @@
-# Used to notify core maintainers about new model PR being merged
-name: New model PR merged notification
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'src/transformers/models/*/modeling_*'
-
-jobs:
-  notify_new_model:
-    name: Notify new model
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - name: Check new model
-        shell: bash
-        run: |
-          python -m pip install gitpython
-          python -c 'from utils.pr_slow_ci_models import get_new_model; new_model = get_new_model(diff_with_last_commit=True); print(new_model)' | tee output.txt
-          echo "NEW_MODEL=$(tail -n 1 output.txt)" >> $GITHUB_ENV
-          echo "COMMIT_SHA=$(git log -1 --format=%H)" >> $GITHUB_ENV
-
-      - name: print commit sha
-        if: ${{ env.NEW_MODEL != ''}}
-        shell: bash
-        run: |
-          echo "$COMMIT_SHA"
-
-      - name: print new model
-        if: ${{ env.NEW_MODEL != ''}}
-        shell: bash
-        run: |
-          echo "$NEW_MODEL"
-
-      - name: Notify
-        if: ${{ env.NEW_MODEL != ''}}
-        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
-        with:
-          # Slack channel id, channel name, or user id to post message.
-          # See also: https://api.slack.com/methods/chat.postMessage#channels
-          channel-id: transformers-new-model-notification
-          # For posting a rich message using Block Kit
-          payload: |
-            {
-              "blocks": [
-                {
-                  "type": "header",
-                  "text": {
-                    "type": "plain_text",
-                    "text": "New model!",
-                    "emoji": true
-                  }
-                },
-                {
-                  "type": "section",
-                  "text": {
-                    "type": "mrkdwn",
-                    "text": "<https://github.com/huggingface/transformers/commit/${{ env.COMMIT_SHA }}|New model: ${{ env.NEW_MODEL }}> GH_ArthurZucker, GH_lysandrejik, GH_ydshieh\ncommit SHA: ${{ env.COMMIT_SHA }}"
-                  }
-                }
-              ]
-            }
-        env:
-          SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/pr-style-bot.yml

@@ -1,18 +0,0 @@
-# To run this bot, comment "@bot /style" on a PR
-name: Style Bot
-
-on:
-  issue_comment:
-    types: [created]
-
-permissions:
-  pull-requests: write
-
-jobs:
-  style:
-    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
-    with:
-      python_quality_dependencies: "[quality]"
-      style_command_type: "default"
-    secrets:
-      bot_token: ${{ secrets.HF_STYLE_BOT_ACTION }}

.github/workflows/push-important-models.yml

@@ -14,6 +14,7 @@ env:
   RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`. 
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} 
   TF_FORCE_GPU_ALLOW_GROWTH: true 
+  RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
   get_modified_models:
@@ -27,7 +28,7 @@ jobs:
       
       - name: Get changed files
         id: changed-files
-        uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
+        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
         with:
           files: src/transformers/models/**
       
@@ -51,8 +52,7 @@ jobs:
   test_modified_files:
     needs: get_modified_models
     name: Slow & FA2 tests
-    runs-on:
-      group: aws-g5-4xlarge-cache
+    runs-on: [single-gpu, nvidia-gpu, a10, ci]
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -133,3 +133,10 @@ jobs:
           slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
           slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
           waitForSSH: true
+
+  benchmark:
+    name: Benchmark workflow
+    needs: get_modified_models
+    if: ${{ needs.get_modified_models.outputs.matrix != '[]' && needs.get_modified_models.outputs.matrix != '' && fromJson(needs.get_modified_models.outputs.matrix)[0] != null }}
+    uses: ./.github/workflows/benchmark.yml
+    secrets: inherit

.github/workflows/self-comment-ci.yml

@@ -1,416 +0,0 @@
-name: PR comment GitHub CI
-
-on:
-  issue_comment:
-    types:
-      - created
-    branches-ignore:
-      - main
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.issue.number }}-${{ startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow') }}
-  cancel-in-progress: true
-permissions: read-all
-
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  RUN_SLOW: yes
-  # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
-  # This token is created under the bot `hf-transformers-bot`.
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  TF_FORCE_GPU_ALLOW_GROWTH: true
-  CUDA_VISIBLE_DEVICES: 0,1
-
-jobs:
-  get-pr-number:
-    runs-on: ubuntu-22.04
-    name: Get PR number
-    # For security: only allow team members to run
-    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "vasqu", "ivarflakstad"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
-    outputs:
-      PR_NUMBER: ${{ steps.set_pr_number.outputs.PR_NUMBER }}
-    steps:
-      - name: Get PR number
-        shell: bash
-        run: |
-          if [[ "${{ github.event.issue.number }}" != "" && "${{ github.event.issue.pull_request }}" != "" ]]; then
-            echo "PR_NUMBER=${{ github.event.issue.number }}" >> $GITHUB_ENV
-          else
-            echo "PR_NUMBER=" >> $GITHUB_ENV
-          fi
-
-      - name: Check PR number
-        shell: bash
-        run: |
-          echo "${{ env.PR_NUMBER }}"
-
-      - name: Set PR number
-        id: set_pr_number
-        run: echo "PR_NUMBER=${{ env.PR_NUMBER }}" >> "$GITHUB_OUTPUT"
-
-  get-sha:
-    runs-on: ubuntu-22.04
-    needs: get-pr-number
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    outputs:
-      PR_HEAD_SHA: ${{ steps.get_sha.outputs.PR_HEAD_SHA }}
-      PR_MERGE_SHA: ${{ steps.get_sha.outputs.PR_MERGE_SHA }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: "0"
-          ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge"
-
-      - name: Get SHA (and verify timestamps against the issue comment date)
-        id: get_sha
-        env:
-          PR_NUMBER: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-          COMMENT_DATE: ${{ github.event.comment.created_at }}
-        run: |
-            git fetch origin refs/pull/$PR_NUMBER/head:refs/remotes/pull/$PR_NUMBER/head
-            git checkout refs/remotes/pull/$PR_NUMBER/head
-            echo "PR_HEAD_SHA: $(git log -1 --format=%H)"
-            echo "PR_HEAD_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT"
-            git fetch origin refs/pull/$PR_NUMBER/merge:refs/remotes/pull/$PR_NUMBER/merge
-            git checkout refs/remotes/pull/$PR_NUMBER/merge
-            echo "PR_MERGE_SHA: $(git log -1 --format=%H)"
-            echo "PR_MERGE_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT"
-            PR_MERGE_COMMIT_TIMESTAMP=$(git log -1 --date=unix --format=%cd)
-            echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP"
-            COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
-            echo "COMMENT_DATE: $COMMENT_DATE"
-            echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
-            if [ $COMMENT_TIMESTAMP -le $PR_MERGE_COMMIT_TIMESTAMP ]; then
-              echo "Last commit on the pull request is newer than the issue comment triggering this run! Abort!";
-              exit -1;
-            fi
-
-  # use a python script to handle this complex logic
-  # case 1: `run-slow` (auto. infer with limited number of models, but in particular, new model)
-  # case 2: `run-slow model_1, model_2`
-  get-tests:
-    runs-on: ubuntu-22.04
-    needs: [get-pr-number, get-sha]
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    outputs:
-      models: ${{ steps.models_to_run.outputs.models }}
-      quantizations: ${{ steps.models_to_run.outputs.quantizations }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: "0"
-          ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge"
-
-      - name: Verify merge commit SHA
-        env:
-          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
-        run: |
-            PR_MERGE_SHA=$(git log -1 --format=%H)
-            if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
-              echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-              exit -1;
-            fi
-
-      - name: Get models to test
-        env:
-          PR_COMMENT: ${{ github.event.comment.body }}
-        run: |
-          python -m pip install GitPython
-          python utils/pr_slow_ci_models.py --message "$PR_COMMENT" | tee output.txt
-          echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV
-          python utils/pr_slow_ci_models.py --message "$PR_COMMENT" --quantization | tee output2.txt
-          echo "quantizations=$(tail -n 1 output2.txt)" >> $GITHUB_ENV
-
-      - name: Show models to test
-        id: models_to_run
-        run: |
-          echo "${{ env.models }}"
-          echo "models=${{ env.models }}" >> $GITHUB_ENV
-          echo "models=${{ env.models }}" >> $GITHUB_OUTPUT
-          echo "${{ env.quantizations }}"
-          echo "quantizations=${{ env.quantizations }}" >> $GITHUB_OUTPUT
-
-  reply_to_comment:
-    name: Reply to the comment
-    if: ${{ needs.get-tests.outputs.models != '[]'  || needs.get-tests.outputs.quantizations != '[]' }}
-    needs: [get-pr-number, get-tests]
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Reply to the comment
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          MODELS: ${{ needs.get-tests.outputs.models }}
-          BODY: "\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}"
-        run: |
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f "body=This comment contains run-slow, running the specified jobs: ${{ env.BODY }} ..."
-
-  create_run:
-    name: Create run
-    if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }}
-    needs: [get-sha, get-tests, reply_to_comment]
-    permissions:
-      statuses: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Create Run
-        id: create_run
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # Create a commit status (pending) for a run of this workflow. The status has to be updated later in `update_run_status`.
-          # See https://docs.github.com/en/rest/commits/statuses?apiVersion=2022-11-28#create-a-commit-status
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-        run: |
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Slow CI job" -f "context=pytest/custom-tests"
-
-  run_models_gpu:
-    name: Run all tests for the model
-    if: ${{ needs.get-tests.outputs.models != '[]' }}
-    needs: [get-pr-number, get-sha, get-tests, create_run]
-    strategy:
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-       group: '${{ matrix.machine_type }}'
-    container:
-      image: huggingface/transformers-all-latest-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Echo input and matrix info
-        shell: bash
-        run: |
-          echo "${{ matrix.folders }}"
-
-      - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
-        # set the artifact folder names (because the character `/` is not allowed).
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          matrix_folders=${matrix_folders/'models/'/'models_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
-
-      - name: Checkout to PR merge commit
-        working-directory: /transformers
-        run: |
-          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git log -1 --format=%H
-
-      - name: Verify merge commit SHA
-        env:
-          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
-        working-directory: /transformers
-        run: |
-          PR_MERGE_SHA=$(git log -1 --format=%H)
-          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
-            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-            exit -1;
-          fi
-
-      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Environment
-        working-directory: /transformers
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Run all tests on GPU
-        working-directory: /transformers
-        run: |
-          export CUDA_VISIBLE_DEVICES="$(python3 utils/set_cuda_devices_for_ci.py --test_folder ${{ matrix.folders }})"
-          echo $CUDA_VISIBLE_DEVICES
-          python3 -m pytest -v -rsfE --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
-
-      - name: Make sure report directory exists
-        shell: bash
-        run: |
-          mkdir -p /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports"
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
-
-  run_quantization_torch_gpu:
-    name: Run all tests for a quantization
-    if: ${{ needs.get-tests.outputs.quantizations != '[]' }}
-    needs: [get-pr-number, get-sha, get-tests, create_run]
-    strategy:
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
-    container:
-      image: huggingface/transformers-quantization-latest-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          matrix_folders=${matrix_folders/'quantization/'/'quantization_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
-
-      - name: Checkout to PR merge commit
-        working-directory: /transformers
-        run: |
-          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git log -1 --format=%H
-
-      - name: Verify merge commit SHA
-        env:
-          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
-        working-directory: /transformers
-        run: |
-          PR_MERGE_SHA=$(git log -1 --format=%H)
-          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
-            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-            exit -1;
-          fi
-
-      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Environment
-        working-directory: /transformers
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Run quantization tests on GPU
-        working-directory: /transformers
-        run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
-
-      - name: Make sure report directory exists
-        shell: bash
-        run: |
-          mkdir -p /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports"
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
-
-  update_run_status:
-    name: Update Check Run Status
-    needs: [get-sha, create_run, run_models_gpu, run_quantization_torch_gpu]
-    permissions:
-      statuses: write
-    if: ${{ always() && needs.create_run.result == 'success' }}
-    runs-on: ubuntu-22.04
-    env:
-      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-      STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.run_models_gpu.result) && contains(fromJSON('["skipped", "success"]'), needs.run_quantization_torch_gpu.result) }}
-    steps:
-      - name: Get `run_models_gpu` job status
-        run: |
-          echo "${{ needs.run_models_gpu.result }}"
-          echo "${{ needs.run_quantization_torch_gpu.result }}"
-          echo $STATUS_OK
-          if [ "$STATUS_OK" = "true" ]; then
-            echo "STATUS=success" >> $GITHUB_ENV
-          else
-            echo "STATUS=failure" >> $GITHUB_ENV
-          fi
-
-      - name: Update PR commit statuses
-        run: |
-          echo "${{ needs.run_models_gpu.result }}"
-          echo "${{ env.STATUS }}"
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=${{ env.STATUS }}" -f "description=Slow CI job" -f "context=pytest/custom-tests"

.github/workflows/self-nightly-past-ci-caller.yml

@@ -21,6 +21,39 @@ jobs:
           echo "$(python3 -c 'print(int(${{ github.run_number }}) % 10)')"
           echo "run_number=$(python3 -c 'print(int(${{ github.run_number }}) % 10)')" >> $GITHUB_OUTPUT
 
+  run_past_ci_pytorch_1-13:
+    name: PyTorch 1.13
+    needs: get_number
+    if: needs.get_number.outputs.run_number == 0 && (cancelled() != true) && ((github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')))
+    uses: ./.github/workflows/self-past-caller.yml
+    with:
+      framework: pytorch
+      version: "1.13"
+      sha: ${{ github.sha }}
+    secrets: inherit
+
+  run_past_ci_pytorch_1-12:
+    name: PyTorch 1.12
+    needs: get_number
+    if: needs.get_number.outputs.run_number == 1 && (cancelled() != true) && ((github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')))
+    uses: ./.github/workflows/self-past-caller.yml
+    with:
+      framework: pytorch
+      version: "1.12"
+      sha: ${{ github.sha }}
+    secrets: inherit
+
+  run_past_ci_pytorch_1-11:
+    name: PyTorch 1.11
+    needs: get_number
+    if: needs.get_number.outputs.run_number == 2 && (cancelled() != true) && ((github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')))
+    uses: ./.github/workflows/self-past-caller.yml
+    with:
+      framework: pytorch
+      version: "1.11"
+      sha: ${{ github.sha }}
+    secrets: inherit
+
   run_past_ci_tensorflow_2-11:
     name: TensorFlow 2.11
     needs: get_number

.github/workflows/self-pr-slow-ci.yml

@@ -0,0 +1,135 @@
+name: PR slow CI
+
+on:
+  pull_request:
+    paths:
+      - "src/transformers/models/*/modeling_*.py"
+      - "tests/**/test_*.py"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  RUN_SLOW: yes
+  # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
+  # This token is created under the bot `hf-transformers-bot`.
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
+  CUDA_VISIBLE_DEVICES: 0,1
+
+jobs:
+  find_models_to_run:
+      runs-on: ubuntu-22.04
+      name: Find models to run slow tests
+      # Triggered only if the required label `run-slow` is added
+      if: ${{ contains(github.event.pull_request.labels.*.name, 'run-slow') }}
+      outputs:
+        models: ${{ steps.models_to_run.outputs.models }}
+      steps:
+        - uses: actions/checkout@v4
+          with:
+            fetch-depth: "0"
+            ref: ${{ github.event.pull_request.head.sha }}
+
+        - name: Get commit message
+          run: |
+            echo "commit_message=$(git show -s --format=%s)" >> $GITHUB_ENV
+
+        - name: Get models to run slow tests
+          run: |
+            echo "${{ env.commit_message }}"
+            python -m pip install GitPython
+            python utils/pr_slow_ci_models.py --commit_message "${{ env.commit_message }}" | tee output.txt
+            echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV
+
+        - name: Models to run slow tests
+          id: models_to_run
+          run: |
+            echo "${{ env.models }}"
+            echo "models=${{ env.models }}" >> $GITHUB_OUTPUT
+
+  run_models_gpu:
+      name: Run all tests for the model
+      # Triggered only `find_models_to_run` is triggered (label `run-slow` is added) which gives the models to run
+      # (either a new model PR or via a commit message)
+      if: ${{ needs.find_models_to_run.outputs.models != '[]' }}
+      needs: find_models_to_run
+      strategy:
+        fail-fast: false
+        matrix:
+          folders: ${{ fromJson(needs.find_models_to_run.outputs.models) }}
+          machine_type: [single-gpu, multi-gpu]
+      runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, ci]
+      container:
+        image: huggingface/transformers-all-latest-gpu
+        options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      steps:
+      - name: Echo input and matrix info
+        shell: bash
+        run: |
+          echo "${{ matrix.folders }}"
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git fetch origin pull/${{ github.event.pull_request.number }}/head:pull/${{ github.event.pull_request.number }}/merge && git checkout pull/${{ github.event.pull_request.number }}/merge
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: |
+          export CUDA_VISIBLE_DEVICES="$(python3 utils/set_cuda_devices_for_ci.py --test_folder ${{ matrix.folders }})"
+          echo $CUDA_VISIBLE_DEVICES
+          python3 -m pytest -v -rsfE --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: Make sure report directory exists
+        shell: bash
+        run: |
+          mkdir -p /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+          echo "hello" > /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt
+          echo "${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports"
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports

.github/workflows/self-push-amd-mi210-caller.yml

@@ -1,10 +1,10 @@
 name: Self-hosted runner (AMD mi210 CI caller)
 
 on:
-  #workflow_run:
-  #  workflows: ["Self-hosted runner (push-caller)"]
-  #  branches: ["main"]
-  #  types: [completed]
+  workflow_run:
+    workflows: ["Self-hosted runner (push-caller)"]
+    branches: ["main"]
+    types: [completed]
   push:
     branches:
       - run_amd_push_ci_caller*

.github/workflows/self-push-amd-mi250-caller.yml

@@ -1,10 +1,10 @@
 name: Self-hosted runner (AMD mi250 CI caller)
 
 on:
-  #workflow_run:
-  #  workflows: ["Self-hosted runner (push-caller)"]
-  #  branches: ["main"]
-  #  types: [completed]
+  workflow_run:
+    workflows: ["Self-hosted runner (push-caller)"]
+    branches: ["main"]
+    types: [completed]
   push:
     branches:
       - run_amd_push_ci_caller*

.github/workflows/self-push-amd-mi300-caller.yml

@@ -1,10 +1,10 @@
 name: Self-hosted runner (AMD mi300 CI caller)
 
 on:
-  #workflow_run:
-  #  workflows: ["Self-hosted runner (push-caller)"]
-  #  branches: ["main"]
-  #  types: [completed]
+  workflow_run:
+    workflows: ["Self-hosted runner (push-caller)"]
+    branches: ["main"]
+    types: [completed]
   push:
     branches:
       - run_amd_push_ci_caller*

.github/workflows/self-push-amd.yml

@@ -14,6 +14,7 @@ env:
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
 
 jobs:

.github/workflows/self-push-caller.yml

@@ -25,7 +25,7 @@ jobs:
         
         - name: Get changed files
           id: changed-files
-          uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
+          uses: tj-actions/changed-files@v41
         
         - name: Was setup changed 
           id: was_changed

.github/workflows/self-push.yml

@@ -24,6 +24,7 @@ env:
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -31,9 +32,8 @@ jobs:
     name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, push-ci]
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -131,9 +131,8 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g5-4xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, push-ci]
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -163,23 +162,6 @@ jobs:
           echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Update clone using environment variables
         working-directory: /transformers
         run: |
@@ -221,19 +203,19 @@ jobs:
       - name: Run all non-slow selected tests on GPU
         working-directory: /transformers
         run: |
-          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
+          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}
+          name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
 
   run_tests_multi_gpu:
     name: Model tests
@@ -244,9 +226,8 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, push-ci]
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -276,23 +257,6 @@ jobs:
           echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Update clone using environment variables
         working-directory: /transformers
         run: |
@@ -336,19 +300,19 @@ jobs:
           MKL_SERVICE_FORCE_INTEL: 1
         working-directory: /transformers
         run: |
-          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
+          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}
+          name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}
 
   run_tests_torch_cuda_extensions_single_gpu:
     name: Torch CUDA extension tests
@@ -357,9 +321,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, push-ci]
     container:
       image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -389,23 +352,6 @@ jobs:
           echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /workspace/transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -446,19 +392,19 @@ jobs:
         working-directory: /workspace/transformers
         # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests.
         run: |
-          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
+          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+        run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
-          path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          name: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: /workspace/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
 
   run_tests_torch_cuda_extensions_multi_gpu:
     name: Torch CUDA extension tests
@@ -467,9 +413,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, push-ci]
     container:
       image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -499,23 +444,6 @@ jobs:
           echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /workspace/transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -556,19 +484,19 @@ jobs:
         working-directory: /workspace/transformers
         # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests.
         run: |
-          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
+          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+        run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
-          path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          name: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: /workspace/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
 
   send_results:
     name: Send results to webhook

.github/workflows/self-scheduled-amd-mi210-caller.yml

@@ -0,0 +1,20 @@
+name: Self-hosted runner (AMD mi210 scheduled CI caller)
+
+on:
+  workflow_run:
+    workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
+    branches: ["main"]
+    types: [completed]
+  push:
+    branches:
+      - run_amd_scheduled_ci_caller*
+
+jobs:
+  run_amd_ci:
+    name: AMD mi210
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_scheduled_ci_caller')))
+    uses: ./.github/workflows/self-scheduled-amd.yml
+    with:
+      gpu_flavor: mi210
+      slack_report_channel: "#transformers-ci-daily-amd"
+    secrets: inherit

.github/workflows/self-scheduled-amd-mi250-caller.yml

@@ -10,50 +10,11 @@ on:
       - run_amd_scheduled_ci_caller*
 
 jobs:
-  model-ci:
-    name: Model CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
+  run_amd_ci:
+    name: AMD mi250
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_scheduled_ci_caller')))
+    uses: ./.github/workflows/self-scheduled-amd.yml
     with:
-      job: run_models_gpu
+      gpu_flavor: mi250
       slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi250
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi250
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  torch-pipeline:
-    name: Torch pipeline CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_pipelines_torch_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi250
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi250
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_examples_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi250
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi250
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi250
-      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi250
-      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit

.github/workflows/self-scheduled-amd-mi300-caller.yml

@@ -1,8 +1,4 @@
-name: Self-hosted runner scale set (AMD mi300 scheduled CI caller)
-
-# Note: For every job in this workflow, the name of the runner scale set is finalized in the runner yaml i.e. huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml
-# For example, 1gpu scale set: amd-mi300-ci-1gpu
-#              2gpu scale set: amd-mi300-ci-2gpu
+name: Self-hosted runner (AMD mi300 scheduled CI caller)
 
 on:
   workflow_run:
@@ -14,50 +10,12 @@ on:
       - run_amd_scheduled_ci_caller*
 
 jobs:
-  model-ci:
-    name: Model CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+  run_amd_ci:
+    name: AMD mi300
+    needs: build-docker-containers
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && (startsWith(github.ref_name, 'run_amd_push_ci_caller') || startsWith(github.ref_name, 'mi300-ci'))))
+    uses: ./.github/workflows/self-scheduled-amd.yml
     with:
-      job: run_models_gpu
-      slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi300-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi300
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  torch-pipeline:
-    name: Torch pipeline CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
-    with:
-      job: run_pipelines_torch_gpu
-      slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi300-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi300
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
-    with:
-      job: run_examples_gpu
-      slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi300-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi300
-      report_repo_id: optimum-amd/transformers_daily_ci
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
-    with:
-      job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi300-ci
-      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi300
-      report_repo_id: optimum-amd/transformers_daily_ci
+      gpu_flavor: mi300
+      slack_report_channel: "#transformers-ci-daily-amd"
     secrets: inherit

.github/workflows/self-scheduled-amd.yml

@@ -0,0 +1,520 @@
+name: Self-hosted runner (scheduled-amd)
+
+# Note: For the AMD CI, we rely on a caller workflow and on the workflow_call event to trigger the
+# CI in order to run it on both MI210 and MI250, without having to use matrix here which pushes
+# us towards the limit of allowed jobs on GitHub Actions.
+on:
+  workflow_call:
+    inputs:
+      gpu_flavor:
+        required: true
+        type: string
+
+env:
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  RUN_SLOW: yes
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
+
+
+# Important note: each job (run_tests_single_gpu, run_tests_multi_gpu, run_examples_gpu, run_pipelines_torch_gpu) requires all the previous jobs before running.
+# This is done so that we avoid parallelizing the scheduled tests, to leave available
+# runners for the push CI that is running on the same machine.
+jobs:
+  check_runner_status:
+    name: Check Runner Status
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Checkout transformers
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Check Runner Status
+        run: python utils/check_self_hosted_runner.py --target_runners hf-amd-mi210-ci-1gpu-1,hf-amd-mi250-ci-1gpu-1,hf-amd-mi300-ci-1gpu-1 --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+
+  check_runners:
+    name: Check Runners
+    needs: check_runner_status
+    strategy:
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+  setup:
+    name: Setup
+    needs: check_runners
+    strategy:
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: |
+          git fetch && git checkout ${{ github.sha }}
+
+      - name: Cleanup
+        working-directory: /transformers
+        run: |
+          rm -rf tests/__pycache__
+          rm -rf tests/models/__pycache__
+          rm -rf reports
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - id: set-matrix
+        name: Identify models to test
+        working-directory: /transformers/tests
+        run: |
+          echo "matrix=$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')" >> $GITHUB_OUTPUT
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+  run_models_gpu_single_gpu:
+    name: Single GPU tests
+    strategy:
+      max-parallel: 1  # For now, not to parallelize. Can change later if it works well.
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.setup.outputs.matrix) }}
+        machine_type: [single-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    needs: setup
+    steps:
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+
+  run_models_gpu_multi_gpu:
+    name: Multi GPU tests
+    strategy:
+      max-parallel: 1
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.setup.outputs.matrix) }}
+        machine_type: [multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    needs: setup
+    steps:
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+
+  run_examples_gpu:
+    name: Examples tests
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [single-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    needs: setup
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run examples tests on GPU
+        working-directory: /transformers
+        run: |
+          pip install -r examples/pytorch/_tests_requirements.txt
+          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_examples_gpu_test_reports examples/pytorch -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_examples_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_examples_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports
+
+  run_pipelines_torch_gpu:
+    name: PyTorch pipelines tests
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    needs: setup
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all pipeline tests on GPU
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports
+
+  run_torch_cuda_extensions_gpu:
+    name: Torch ROCm deepspeed tests
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    needs: setup
+    container:
+      image: huggingface/transformers-pytorch-deepspeed-amd-gpu
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+
+  run_extract_warnings:
+    name: Extract warnings in CI artifacts
+    runs-on: ubuntu-22.04
+    if: always()
+    needs: [
+      check_runner_status,
+      check_runners,
+      setup,
+      run_models_gpu_single_gpu,
+      run_models_gpu_multi_gpu,
+      run_examples_gpu,
+      run_pipelines_torch_gpu,
+      run_torch_cuda_extensions_gpu
+    ]
+    steps:
+      - name: Checkout transformers
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Install transformers
+        run: pip install transformers
+
+      - name: Show installed libraries and their versions
+        run: pip freeze
+
+      - name: Create output directory
+        run: mkdir warnings_in_ci
+
+      - uses: actions/download-artifact@v4
+        with:
+          path: warnings_in_ci
+
+      - name: Show artifacts
+        run: echo "$(python3 -c 'import os; d = os.listdir(); print(d)')"
+        working-directory: warnings_in_ci
+
+      - name: Extract warnings in CI artifacts
+        run: |
+          python3 utils/extract_warnings.py --workflow_run_id ${{ github.run_id }} --output_dir warnings_in_ci --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} --from_gh
+          echo "$(python3 -c 'import os; import json; fp = open("warnings_in_ci/selected_warnings.json"); d = json.load(fp); d = "\n".join(d) ;print(d)')"
+
+      - name: Upload artifact
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: warnings_in_ci
+          path: warnings_in_ci/selected_warnings.json
+
+  send_results:
+    name: Send results to webhook
+    runs-on: ubuntu-22.04
+    if: always()
+    needs: [
+      check_runner_status,
+      check_runners,
+      setup,
+      run_models_gpu_single_gpu,
+      run_models_gpu_multi_gpu,
+      run_examples_gpu,
+      run_pipelines_torch_gpu,
+      run_torch_cuda_extensions_gpu,
+      run_extract_warnings
+    ]
+    steps:
+      - name: Preliminary job status
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          echo "Runner availability: ${{ needs.check_runner_status.result }}"
+          echo "Runner status: ${{ needs.check_runners.result }}"
+          echo "Setup status: ${{ needs.setup.result }}"
+
+      - uses: actions/checkout@v4
+      - uses: actions/download-artifact@v4
+      - name: Send message to Slack
+        env:
+          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+          CI_SLACK_CHANNEL_ID_DAILY_AMD: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_AMD }}
+          CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
+          CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_AMD }}
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          CI_EVENT: Scheduled CI (AMD) - ${{ inputs.gpu_flavor }}
+          CI_SHA: ${{ github.sha }}
+          CI_WORKFLOW_REF: ${{ github.workflow_ref }}
+          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
+          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
+          SETUP_STATUS: ${{ needs.setup.result }}
+        # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
+        # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
+        run: |
+          sudo apt-get install -y curl
+          pip install huggingface_hub
+          pip install slack_sdk
+          pip show slack_sdk
+          python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"
+
+      # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
+      - name: Failure table artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: test_failure_tables
+          path: test_failure_tables

.github/workflows/self-scheduled-caller.yml

@@ -2,56 +2,74 @@ name: Self-hosted runner (scheduled)
 
 
 on:
-  repository_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
   push:
     branches:
-      - fix-dataset-run_object_detection-and-add-torchcodec-trigger-ci
-  workflow_dispatch:
-    inputs:
-      prev_workflow_run_id:
-        description: 'previous workflow run id to compare'
-        type: string
-        required: false
-        default: ""
-      other_workflow_run_id:
-        description: 'other workflow run id to compare'
-        type: string
-        required: false
-        default: ""
-
-
-# Used for `push` to easily modify the target workflow runs to compare against
-env:
-    prev_workflow_run_id: "15988665799"
-    other_workflow_run_id: ""
-
+      - run_scheduled_ci*
 
 jobs:
-  setup:
-    name: Setup
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Setup
-        run: |
-          mkdir "setup_values"
-          echo "${{ inputs.prev_workflow_run_id || env.prev_workflow_run_id }}" > "setup_values/prev_workflow_run_id.txt"
-          echo "${{ inputs.other_workflow_run_id || env.other_workflow_run_id }}" > "setup_values/other_workflow_run_id.txt"
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: setup_values
-          path: setup_values
-
   model-ci:
     name: Model CI
     uses: ./.github/workflows/self-scheduled.yml
     with:
       job: run_models_gpu
-      slack_report_channel: "#transformers-dummy"
+      slack_report_channel: "#transformers-ci-daily-models"
+      runner: daily-ci
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
+    secrets: inherit
+
+  torch-pipeline:
+    name: Torch pipeline CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_pipelines_torch_gpu
+      slack_report_channel: "#transformers-ci-daily-pipeline-torch"
+      runner: daily-ci
+      docker: huggingface/transformers-pytorch-gpu
+      ci_event: Daily CI
+    secrets: inherit
+
+  tf-pipeline:
+    name: TF pipeline CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_pipelines_tf_gpu
+      slack_report_channel: "#transformers-ci-daily-pipeline-tf"
+      runner: daily-ci
+      docker: huggingface/transformers-tensorflow-gpu
+      ci_event: Daily CI
+    secrets: inherit
+
+  example-ci:
+    name: Example CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_examples_gpu
+      slack_report_channel: "#transformers-ci-daily-examples"
+      runner: daily-ci
+      docker: huggingface/transformers-all-latest-gpu
+      ci_event: Daily CI
+    secrets: inherit
+
+  deepspeed-ci:
+    name: DeepSpeed CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_torch_cuda_extensions_gpu
+      slack_report_channel: "#transformers-ci-daily-deepspeed"
+      runner: daily-ci
+      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
+      ci_event: Daily CI
+      working-directory-prefix: /workspace
+    secrets: inherit
+
+  quantization-ci:
+    name: Quantization CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_quantization_torch_gpu
+      slack_report_channel: "#transformers-ci-daily-quantization"
+      runner: daily-ci
+      docker: huggingface/transformers-quantization-latest-gpu
+      ci_event: Daily CI
     secrets: inherit

.github/workflows/self-scheduled-intel-gaudi.yml

@@ -1,345 +0,0 @@
-name: Self-hosted runner (scheduled-intel-gaudi)
-
-on:
-  workflow_call:
-    inputs:
-      job:
-        required: true
-        type: string
-      slack_report_channel:
-        required: true
-        type: string
-      runner_scale_set:
-        required: true
-        type: string
-      ci_event:
-        required: true
-        type: string
-      report_repo_id:
-        required: true
-        type: string
-
-env:
-  NUM_SLICES: 2
-  RUN_SLOW: yes
-  PT_HPU_LAZY_MODE: 0
-  TRANSFORMERS_IS_CI: yes
-  PT_ENABLE_INT64_SUPPORT: 1
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  HF_HOME: /mnt/cache/.cache/huggingface
-
-jobs:
-  setup:
-    if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
-    name: Setup
-    runs-on: ubuntu-latest
-    outputs:
-      slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
-      folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
-      quantization_matrix: ${{ steps.set-matrix.outputs.quantization_matrix }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-
-      - id: set-matrix
-        if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
-        name: Identify models to test
-        working-directory: tests
-        run: |
-          if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
-            echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
-            echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT
-          elif [ "${{ inputs.job }}" = "run_trainer_and_fsdp_gpu" ]; then
-            echo "folder_slices=[['trainer'], ['fsdp']]" >> $GITHUB_OUTPUT
-            echo "slice_ids=[0, 1]" >> $GITHUB_OUTPUT
-          fi
-
-      - id: set-matrix-quantization
-        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
-        name: Identify quantization method to test
-        working-directory: tests
-        run: |
-          echo "quantization_matrix=$(python3 -c 'import os; tests = os.getcwd(); quantization_tests = os.listdir(os.path.join(tests, "quantization")); d = sorted(list(filter(os.path.isdir, [f"quantization/{x}" for x in quantization_tests]))) ;  print(d)')" >> $GITHUB_OUTPUT
-
-  run_models_gpu:
-    if: ${{ inputs.job == 'run_models_gpu' }}
-    name: " "
-    needs: setup
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [1gaudi, 2gaudi]
-        slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
-    uses: ./.github/workflows/model_jobs_intel_gaudi.yml
-    with:
-      slice_id: ${{ matrix.slice_id }}
-      machine_type: ${{ matrix.machine_type }}
-      folder_slices: ${{ needs.setup.outputs.folder_slices }}
-      runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-      report_name_prefix: run_models_gpu
-
-    secrets: inherit
-
-  run_trainer_and_fsdp_gpu:
-    if: ${{ inputs.job == 'run_trainer_and_fsdp_gpu' }}
-    name: " "
-    needs: setup
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [1gaudi, 2gaudi]
-        slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
-    uses: ./.github/workflows/model_jobs_intel_gaudi.yml
-    with:
-      slice_id: ${{ matrix.slice_id }}
-      machine_type: ${{ matrix.machine_type }}
-      folder_slices: ${{ needs.setup.outputs.folder_slices }}
-      runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-      report_name_prefix: run_trainer_and_fsdp_gpu
-
-    secrets: inherit
-
-  run_pipelines_gpu:
-    if: ${{ inputs.job == 'run_pipelines_gpu' }}
-    name: Pipelines
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [1gaudi, 2gaudi]
-    runs-on:
-      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-    container:
-      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
-      options: --runtime=habana
-        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
-        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
-        --env HABANA_VISIBLE_DEVICES
-        --env HABANA_VISIBLE_MODULES
-        --cap-add=sys_nice
-        --shm-size=64G
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
-
-      - name: HL-SMI
-        run: |
-          hl-smi
-          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
-          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
-
-      - name: Environment
-        run: python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        run: pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        shell: bash
-        run: |
-          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Run all pipeline tests on Intel Gaudi
-        run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_pipelines_gpu_test_reports tests/pipelines -m "not not_device_test"
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: |
-          cat reports/${{ env.machine_type }}_run_pipelines_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_pipelines_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_pipelines_gpu_test_reports
-
-  run_examples_gpu:
-    if: ${{ inputs.job == 'run_examples_gpu' }}
-    name: Examples directory
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [1gaudi]
-    runs-on:
-      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-    container:
-      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
-      options: --runtime=habana
-        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
-        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
-        --env HABANA_VISIBLE_DEVICES
-        --env HABANA_VISIBLE_MODULES
-        --cap-add=sys_nice
-        --shm-size=64G
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
-
-      - name: HL-SMI
-        run: |
-          hl-smi
-          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
-          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
-
-      - name: Environment
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        run: |
-          pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        shell: bash
-        run: |
-          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Run examples tests on Intel Gaudi
-        run: |
-          pip install -r examples/pytorch/_tests_requirements.txt
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_examples_gpu_test_reports examples/pytorch -m "not not_device_test"
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: |
-          cat reports/${{ env.machine_type }}_run_examples_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_examples_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_examples_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_examples_gpu_test_reports
-
-  run_deepspeed_gpu:
-    if: ${{ inputs.job == 'run_deepspeed_gpu' }}
-    name: Intel Gaudi deepspeed tests
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [1gaudi, 2gaudi]
-    runs-on:
-      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-    container:
-      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
-      options: --runtime=habana
-        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
-        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
-        --env HABANA_VISIBLE_DEVICES
-        --env HABANA_VISIBLE_MODULES
-        --cap-add=sys_nice
-        --shm-size=64G
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
-          pip install git+https://github.com/HabanaAI/DeepSpeed.git@1.20.0
-
-      - name: HL-SMI
-        run: |
-          hl-smi
-          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
-          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
-
-      - name: Environment
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        run: |
-          pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        shell: bash
-        run: |
-          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Run all deepspeed tests on intel Gaudi
-        run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_deepspeed_gpu_test_reports tests/deepspeed -m "not not_device_test"
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: |
-          cat reports/${{ env.machine_type }}_run_deepspeed_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_deepspeed_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_deepspeed_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_deepspeed_gpu_test_reports
-
-  send_results:
-    name: Slack Report
-    needs:
-      [
-        setup,
-        run_models_gpu,
-        run_examples_gpu,
-        run_pipelines_gpu,
-        run_deepspeed_gpu,
-        run_trainer_and_fsdp_gpu,
-      ]
-    if: ${{ always() }}
-    uses: ./.github/workflows/slack-report.yml
-    with:
-      job: ${{ inputs.job }}
-      setup_status: ${{ needs.setup.result }}
-      slack_report_channel: ${{ inputs.slack_report_channel }}
-      quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }}
-      folder_slices: ${{ needs.setup.outputs.folder_slices }}
-      report_repo_id: ${{ inputs.report_repo_id }}
-      ci_event: ${{ inputs.ci_event }}
-
-    secrets: inherit

.github/workflows/self-scheduled-intel-gaudi3-caller.yml

@@ -1,67 +0,0 @@
-name: Self-hosted runner (Intel Gaudi3 scheduled CI caller)
-
-on:
-  repository_dispatch:
-  workflow_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
-
-jobs:
-  model-ci:
-    name: Model CI
-    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
-    with:
-      job: run_models_gpu
-      ci_event: Scheduled CI (Intel) - Gaudi3
-      runner_scale_set: itac-bm-emr-gaudi3-dell
-      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
-      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
-
-    secrets: inherit
-
-  pipeline-ci:
-    name: Pipeline CI
-    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
-    with:
-      job: run_pipelines_gpu
-      ci_event: Scheduled CI (Intel) - Gaudi3
-      runner_scale_set: itac-bm-emr-gaudi3-dell
-      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
-      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
-
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
-    with:
-      job: run_examples_gpu
-      ci_event: Scheduled CI (Intel) - Gaudi3
-      runner_scale_set: itac-bm-emr-gaudi3-dell
-      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
-      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
-
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
-    with:
-      job: run_deepspeed_gpu
-      ci_event: Scheduled CI (Intel) - Gaudi3
-      runner_scale_set: itac-bm-emr-gaudi3-dell
-      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
-      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
-
-    secrets: inherit
-
-  trainer-fsdp-ci:
-    name: Trainer/FSDP CI
-    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
-    with:
-      job: run_trainer_and_fsdp_gpu
-      ci_event: Scheduled CI (Intel) - Gaudi3
-      runner_scale_set: itac-bm-emr-gaudi3-dell
-      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
-      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
-    secrets: inherit

.github/workflows/self-scheduled.yml

@@ -15,6 +15,9 @@ on:
       slack_report_channel:
         required: true
         type: string
+      runner:
+        required: true
+        type: string
       docker:
         required: true
         type: string
@@ -25,10 +28,6 @@ on:
         default: ''
         required: false
         type: string
-      report_repo_id:
-        required: true
-        type: string
-
 
 env:
   HF_HOME: /mnt/cache
@@ -41,25 +40,24 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
   NUM_SLICES: 2
 
 jobs:
   setup:
-    if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu", "run_quantization_torch_gpu"]'), inputs.job)
+    if: contains(fromJSON('["run_models_gpu", "run_quantization_torch_gpu"]'), inputs.job)
     name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     outputs:
       folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
       slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
-      runner_map: ${{ steps.set-matrix.outputs.runner_map }}
       quantization_matrix: ${{ steps.set-matrix-quantization.outputs.quantization_matrix }}
     steps:
       - name: Update clone
@@ -79,18 +77,12 @@ jobs:
         run: pip freeze
 
       - id: set-matrix
-        if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
+        if: ${{ inputs.job == 'run_models_gpu' }}
         name: Identify models to test
         working-directory: /transformers/tests
         run: |
-          if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
           echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
           echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT
-            echo "runner_map=$(python3 ../utils/get_runner_map.py)" >> $GITHUB_OUTPUT
-          elif [ "${{ inputs.job }}" = "run_trainer_and_fsdp_gpu" ]; then
-            echo "folder_slices=[['trainer'], ['fsdp']]" >> $GITHUB_OUTPUT
-            echo "slice_ids=[0, 1]" >> $GITHUB_OUTPUT
-          fi
       
       - id: set-matrix-quantization
         if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
@@ -117,37 +109,18 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       machine_type: ${{ matrix.machine_type }}
       slice_id: ${{ matrix.slice_id }}
-      runner_map: ${{ needs.setup.outputs.runner_map }}
+      runner: ${{ inputs.runner }}
       docker: ${{ inputs.docker }}
     secrets: inherit
 
-  run_trainer_and_fsdp_gpu:
-    if: ${{ inputs.job == 'run_trainer_and_fsdp_gpu' }}
-    name: " "
-    needs: setup
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-        slice_id: [0, 1]
-    uses: ./.github/workflows/model_jobs.yml
-    with:
-      folder_slices: ${{ needs.setup.outputs.folder_slices }}
-      machine_type: ${{ matrix.machine_type }}
-      slice_id: ${{ matrix.slice_id }}
-      docker: ${{ inputs.docker }}
-      report_name_prefix: run_trainer_and_fsdp_gpu
-    secrets: inherit
-
   run_pipelines_torch_gpu:
     if: ${{ inputs.job == 'run_pipelines_torch_gpu' }}
     name: PyTorch pipelines
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: huggingface/transformers-pytorch-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -173,39 +146,73 @@ jobs:
         working-directory: /transformers
         run: pip freeze
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Run all pipeline tests on GPU
         working-directory: /transformers
         run: |
-          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines
+          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
+          name: ${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_pipelines_torch_gpu_test_reports
+
+  run_pipelines_tf_gpu:
+    if: ${{ inputs.job == 'run_pipelines_tf_gpu' }}
+    name: TensorFlow pipelines
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
+    container:
+      image: huggingface/transformers-tensorflow-gpu
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: |
+          git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all pipeline tests on GPU
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_run_pipelines_tf_gpu_test_reports tests/pipelines
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: |
+          cat /transformers/reports/${{ matrix.machine_type }}_run_pipelines_tf_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_pipelines_tf_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_pipelines_tf_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_pipelines_tf_gpu_test_reports
 
   run_examples_gpu:
     if: ${{ inputs.job == 'run_examples_gpu' }}
@@ -213,9 +220,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -241,40 +247,23 @@ jobs:
         working-directory: /transformers
         run: pip freeze
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Run examples tests on GPU
         working-directory: /transformers
         run: |
           pip install -r examples/pytorch/_tests_requirements.txt
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_examples_gpu_test_reports examples/pytorch
+          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_examples_gpu_test_reports examples/pytorch
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_examples_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_examples_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_examples_gpu_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports
+          name: ${{ matrix.machine_type }}_run_examples_gpu_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_examples_gpu_test_reports
 
   run_torch_cuda_extensions_gpu:
     if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }}
@@ -282,9 +271,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -322,7 +310,7 @@ jobs:
         run: |
           python3 -m pip uninstall -y deepspeed
           rm -rf DeepSpeed
-          git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build
+          git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
           DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI
@@ -338,39 +326,22 @@ jobs:
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
         run: pip freeze
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: ${{ inputs.working-directory-prefix }}/transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Run all tests on GPU
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
         run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
+          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+        run: cat ${{ inputs.working-directory-prefix }}/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
-          path: ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          name: ${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: ${{ inputs.working-directory-prefix }}/transformers/reports/${{ matrix.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
 
   run_quantization_torch_gpu:
     if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
@@ -381,9 +352,8 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, '${{ inputs.runner }}']
     container:
       image: huggingface/transformers-quantization-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -418,39 +388,22 @@ jobs:
         working-directory: /transformers
         run: pip freeze
 
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
       - name: Run quantization tests on GPU
         working-directory: /transformers
         run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
+          name: ${{ matrix.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
 
   run_extract_warnings:
     # Let's only do this for the job `run_models_gpu` to simplify the (already complex) logic.
@@ -498,8 +451,8 @@ jobs:
     needs: [
       setup,
       run_models_gpu,
-      run_trainer_and_fsdp_gpu,
       run_pipelines_torch_gpu,
+      run_pipelines_tf_gpu,
       run_examples_gpu,
       run_torch_cuda_extensions_gpu,
       run_quantization_torch_gpu,
@@ -516,21 +469,5 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }}
       ci_event: ${{ inputs.ci_event }}
-      report_repo_id: ${{ inputs.report_repo_id }}
-
-    secrets: inherit
-
-  check_new_failures:
-    if: ${{ always() && inputs.ci_event == 'Daily CI' && needs.send_results.result == 'success' }}
-    name: Check new failures
-    needs: send_results
-    uses: ./.github/workflows/check_failed_tests.yml
-    with:
-      docker: ${{ inputs.docker }}
-      start_sha: ${{ github.sha }}
-      job: ${{ inputs.job }}
-      slack_report_channel: ${{ inputs.slack_report_channel }}
-      ci_event: ${{ inputs.ci_event }}
-      report_repo_id: ${{ inputs.report_repo_id }}
 
     secrets: inherit

.github/workflows/slack-report.yml

@@ -21,9 +21,6 @@ on:
       ci_event:
         required: true
         type: string
-      report_repo_id:
-        required: true
-        type: string
 
 env:
   TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
@@ -42,23 +39,8 @@ jobs:
 
       - uses: actions/checkout@v4
       - uses: actions/download-artifact@v4
-
-      - name: Prepare some setup values
-        run: |
-          if [ -f setup_values/prev_workflow_run_id.txt ]; then
-            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
-          else
-            echo "PREV_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
-          fi
-
-          if [ -f setup_values/other_workflow_run_id.txt ]; then
-            echo "OTHER_WORKFLOW_RUN_ID=$(cat setup_values/other_workflow_run_id.txt)" >> $GITHUB_ENV
-          else
-            echo "OTHER_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
-          fi
-
       - name: Send message to Slack
-        shell: bash
+        if: ${{ inputs.job != 'run_quantization_torch_gpu' }}
         env:
           CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
           CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
@@ -68,22 +50,19 @@ jobs:
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           CI_EVENT: ${{ inputs.ci_event }}
           CI_SHA: ${{ github.sha }}
+          CI_WORKFLOW_REF: ${{ github.workflow_ref }}
           CI_TEST_JOB: ${{ inputs.job }}
           SETUP_STATUS: ${{ inputs.setup_status }}
-          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         # For a job that doesn't depend on (i.e. `needs`) `setup`, the value for `inputs.folder_slices` would be an
         # empty string, and the called script still get one argument (which is the emtpy string).
         run: |
+          sudo apt-get install -y curl
           pip install huggingface_hub
           pip install slack_sdk
           pip show slack_sdk
-          if [ "${{ inputs.quantization_matrix }}" != "" ]; then
-            python utils/notification_service.py "${{ inputs.quantization_matrix }}"
-          else
           python utils/notification_service.py "${{ inputs.folder_slices }}"
-          fi          
 
       # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
       - name: Failure table artifacts
@@ -91,3 +70,32 @@ jobs:
         with:
           name: ci_results_${{ inputs.job }}
           path: ci_results_${{ inputs.job }}
+      
+      - uses: actions/checkout@v4
+      - uses: actions/download-artifact@v4
+      - name: Send message to Slack for quantization workflow
+        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
+        env:
+          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }}
+          CI_EVENT: ${{ inputs.ci_event }}
+          CI_SHA: ${{ github.sha }}
+          CI_TEST_JOB: ${{ inputs.job }}
+          SETUP_STATUS: ${{ inputs.setup_status }}
+        # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change
+        # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`.
+        run: |
+          sudo apt-get install -y curl
+          pip install huggingface_hub
+          pip install slack_sdk
+          pip show slack_sdk
+          python utils/notification_service_quantization.py "${{ inputs.quantization_matrix }}" 
+
+      # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
+      - name: Failure table artifacts
+        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ci_results_${{ inputs.job }}
+          path: ci_results_${{ inputs.job }}

.github/workflows/ssh-runner.yml

@@ -23,40 +23,12 @@ env:
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} 
   TF_FORCE_GPU_ALLOW_GROWTH: true 
   CUDA_VISIBLE_DEVICES: 0,1
+  RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
-  get_runner:
-    name: "Get runner to use"
-    runs-on: ubuntu-22.04
-    outputs:
-      RUNNER: ${{ steps.set_runner.outputs.RUNNER }}
-    steps:
-      - name: Get runner to use
-        shell: bash
-        run: |
-          if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
-            echo "RUNNER=aws-g4dn-4xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
-            echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
-            echo "RUNNER=aws-g5-4xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
-            echo "RUNNER=aws-g5-12xlarge-cache" >> $GITHUB_ENV
-          else
-            echo "RUNNER=" >> $GITHUB_ENV
-          fi
-
-      - name: Set runner to use
-        id: set_runner
-        run: |
-          echo ${{ env.RUNNER }}
-          echo "RUNNER=${{ env.RUNNER }}" >> $GITHUB_OUTPUT
-
   ssh_runner:
     name: "SSH"
-    needs: get_runner
-    runs-on:
-      group: ${{ needs.get_runner.outputs.RUNNER }}
+    runs-on: ["${{ github.event.inputs.num_gpus }}-gpu", nvidia-gpu, "${{ github.event.inputs.runner_type }}", ci]
     container:
       image: ${{ github.event.inputs.docker_image }}
       options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -82,32 +54,11 @@ jobs:
         run: |
           nvidia-smi
       
-      - name: Store Slack infos
-        #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step
-        shell: bash
-        run: |
-          echo "${{ github.actor }}"
-          github_actor=${{ github.actor }}
-          github_actor=${github_actor/'-'/'_'}
-          echo "$github_actor"
-          echo "github_actor=$github_actor" >> $GITHUB_ENV
-
-      - name: Store Slack infos
-        #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step
-        shell: bash
-        run: |
-          echo "${{ env.github_actor }}"
-          if [ "${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" != "" ]; then
-            echo "SLACKCHANNEL=${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" >> $GITHUB_ENV
-          else
-            echo "SLACKCHANNEL=${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}" >> $GITHUB_ENV
-          fi
-
       - name: Tailscale # In order to be able to SSH when a test fails
         uses: huggingface/tailscale-action@main
         with:
           authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
-          slackChannel: ${{ env.SLACKCHANNEL }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
           slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
           waitForSSH: true
           sshTimeout: 15m

.github/workflows/stale.yml

@@ -9,8 +9,6 @@ jobs:
     name: Close Stale Issues
     if: github.repository == 'huggingface/transformers'
     runs-on: ubuntu-22.04
-    permissions:
-      issues: write
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:

.github/workflows/trufflehog.yml

@@ -16,5 +16,3 @@ jobs:
           fetch-depth: 0
       - name: Secret Scanning
         uses: trufflesecurity/trufflehog@main
-        with:
-          extra_args: --results=verified,unknown

.github/workflows/update_metdata.yml

@@ -19,7 +19,7 @@ jobs:
       - name: Setup environment
         run: |
           pip install --upgrade pip
-          pip install datasets pandas
+          pip install datasets pandas==2.0.3
           pip install .[torch,tf,flax]
 
       - name: Update metadata

AGENTS.md

@@ -1,39 +0,0 @@
-# AGENTS.md Guide for Hugging Face Transformers
-
-This AGENTS.md file provides guidance for code agents working with this codebase.
-
-## Core Project Structure
-
-- `/src/transformers`: This contains the core source code for the library
-  - `/models`: Code for individual models. Models inherit from base classes in the root `/src/transformers` directory.
-- `/tests`: This contains the core test classes for the library. These are usually inherited rather than directly run.
-  - `/models`: Tests for individual models. Model tests inherit from common tests in the root `/tests` directory.
-- `/docs`: This contains the documentation for the library, including guides, tutorials, and API references.
-
-## Coding Conventions for Hugging Face Transformers
-
-- PRs should be as brief as possible. Bugfix PRs in particular can often be only one or two lines long, and do not need large comments, docstrings or new functions in this case. Aim to minimize the size of the diff.
-- When writing tests, they should be added to an existing file. The only exception is for PRs to add a new model, when a new test directory should be created for that model.
-- Code style is enforced in the CI. You can install the style tools with `pip install -e .[quality]`. You can then run `make fixup` to apply style and consistency fixes to your code.
-
-## Copying and inheritance
-
-Many models in the codebase have similar code, but it is not shared by inheritance because we want each model file to be self-contained.
-We use two mechanisms to keep this code in sync:
-
-- "Copied from" syntax. Functions or entire classes can have a comment at the top like this: `# Copied from transformers.models.llama.modeling_llama.rotate_half` or `# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->MT5`
-  These comments are actively checked by the style tools, and copies will automatically be updated when the base code is updated. If you need to update a copied function, you should
-  either update the base function and use `make fixup` to propagate the change to all copies, or simply remove the `# Copied from` comment if that is inappropriate.
-- "Modular" files. These files briefly define models by composing them using inheritance from other models. They are not meant to be used directly. Instead, the style tools
-  automatically generate a complete modeling file, like `modeling_bert.py`, from the modular file like `modular_bert.py`. If a model has a modular file, the modeling file
-  should never be edited directly! Instead, changes should be made in the modular file, and then you should run `make fixup` to update the modeling file automatically.
-
-When adding new models, you should prefer `modular` style.
-
-## Testing
-
-After making changes, you should usually run `make fixup` to ensure any copies and modular files are updated, and then test all affected models. This includes both
-the model you made the changes in and any other models that were updated by `make fixup`. Tests can be run with `pytest tests/models/[name]/test_modeling_[name].py`
-If your changes affect code in other classes like tokenizers or processors, you should run those tests instead, like `test_processing_[name].py` or `test_tokenization_[name].py`.
-
-In order to run tests, you may need to install dependencies. You can do this with `pip install -e .[testing]`. You will probably also need to `pip install torch accelerate` if your environment does not already have them.

CONTRIBUTING.md

@@ -78,7 +78,7 @@ Once you've confirmed the bug hasn't already been reported, please include the f
 To get the OS and software versions automatically, run the following command:
 
 ```bash
-transformers env
+transformers-cli env
 ```
 
 You can also run the same command from the root of the repository:
@@ -132,7 +132,7 @@ You will need basic `git` proficiency to contribute to
 manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro
 Git](https://git-scm.com/book/en/v2) is a very good reference.
 
-You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main/setup.py#L449)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:
+You'll need **[Python 3.8](https://github.com/huggingface/transformers/blob/main/setup.py#L449)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:
 
 1. Fork the [repository](https://github.com/huggingface/transformers) by
    clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code
@@ -221,10 +221,10 @@ You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main
    [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide.
 
    If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check
-   make sure you install the [documentation builder](https://github.com/huggingface/doc-builder).
+   make sure you install the documentation builder:
 
    ```bash
-   pip install hf-doc-builder
+   pip install ".[docs]"
    ```
 
    Run the following command from the root of the repository:
@@ -343,6 +343,8 @@ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/t
 
 Like the slow tests, there are other environment variables available which are not enabled by default during testing:
 - `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers.
+- `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration.
+- `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration.
 
 More environment variables and additional information can be found in the [testing_utils.py](https://github.com/huggingface/transformers/blob/main/src/transformers/testing_utils.py).
 

ISSUES.md

@@ -26,7 +26,7 @@ There are two main venues to receive support: [the forums](https://discuss.huggi
 
 [The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed.
 
-If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystallized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
+If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
 
 In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions:
 
@@ -263,9 +263,9 @@ You are not required to read the following guidelines before opening an issue. H
     But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like:
 
     ```
-    > How big is your GPU cluster?
+    > How big is your gpu cluster?
 
-    Our cluster is made of 256 GPUs.
+    Our cluster is made of 256 gpus.
     ```
 
     If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment.

Makefile

@@ -8,19 +8,13 @@ check_dirs := examples tests src utils
 exclude_folders :=  ""
 
 modified_only_fixup:
-	@current_branch=$$(git branch --show-current); \
-	if [ "$$current_branch" = "main" ]; then \
-		echo "On main branch, running 'style' target instead..."; \
-		$(MAKE) style; \
-	else \
-		modified_py_files=$$(python utils/get_modified_files.py $(check_dirs)); \
-		if [ -n "$$modified_py_files" ]; then \
-			echo "Checking/fixing files: $${modified_py_files}"; \
-			ruff check $${modified_py_files} --fix --exclude $(exclude_folders); \
-			ruff format $${modified_py_files} --exclude $(exclude_folders); \
+	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
+	@if test -n "$(modified_py_files)"; then \
+		echo "Checking/fixing $(modified_py_files)"; \
+		ruff check $(modified_py_files) --fix --exclude $(exclude_folders); \
+		ruff format $(modified_py_files) --exclude $(exclude_folders);\
 	else \
 		echo "No library .py files were modified"; \
-		fi; \
 	fi
 
 # Update src/transformers/dependency_versions_table.py
@@ -42,16 +36,16 @@ autogenerate_code: deps_table_update
 
 repo-consistency:
 	python utils/check_copies.py
-	python utils/check_modular_conversion.py
+	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/check_inits.py
-	python utils/check_pipeline_typing.py
 	python utils/check_config_docstrings.py
 	python utils/check_config_attributes.py
 	python utils/check_doctest_list.py
 	python utils/update_metadata.py --check-only
 	python utils/check_docstrings.py
+	python utils/check_support_list.py
 
 # this target runs checks on all files
 
@@ -86,9 +80,8 @@ fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
 
 fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
-	python utils/check_modular_conversion.py --fix_and_overwrite
+	python utils/check_table.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
-	python utils/check_pipeline_typing.py --fix_and_overwrite
 	python utils/check_doctest_list.py --fix_and_overwrite
 	python utils/check_docstrings.py --fix_and_overwrite
 

README.md

@@ -25,7 +25,6 @@ limitations under the License.
 </p>
 
 <p align="center">
-    <a href="https://huggingface.com/models"><img alt="Checkpoints on Hub" src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen"></a>
     <a href="https://circleci.com/gh/huggingface/transformers"><img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"></a>
     <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"></a>
     <a href="https://huggingface.co/docs/transformers/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"></a>
@@ -50,273 +49,259 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_de.md">Deutsch</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_vi.md">Tiếng Việt</a> |
 	<a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ar.md">العربية</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ur.md">اردو</a> |
     </p>
 </h4>
 
 <h3 align="center">
-    <p>State-of-the-art pretrained models for inference and training</p>
+    <p>State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p>
 </h3>
 
 <h3 align="center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
+    <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
 </h3>
 
+🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
 
-Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer 
-vision, audio, video, and multimodal model, for both inference and training. 
+These models can be applied on:
 
-It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the 
-pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training 
-frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
-and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
+* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.
+* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
+* 🗣️ Audio, for tasks like speech recognition and audio classification.
 
-We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
-simple, customizable, and efficient.
+Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
 
-There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
+🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
 
-Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
+🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.
 
-## Installation
+## Online demos
 
-Transformers works with Python 3.9+ [PyTorch](https://pytorch.org/get-started/locally/) 2.1+, [TensorFlow](https://www.tensorflow.org/install/pip) 2.6+, and [Flax](https://flax.readthedocs.io/en/latest/) 0.4.1+.
+You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models.
 
-Create and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.
+Here are a few examples:
 
-```py
-# venv
-python -m venv .my-env
-source .my-env/bin/activate
-# uv
-uv venv .my-env
-source .my-env/bin/activate
+In Natural Language Processing:
+- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+In Computer Vision:
+- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
+- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
+- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+In Audio:
+- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3)
+- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+In Multimodal tasks:
+- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
+- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
+- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
+- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam)
+
+
+## 100 projects using Transformers
+
+Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the
+Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone
+else to build their dream projects.
+
+In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the
+community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100
+incredible projects built in the vicinity of transformers.
+
+If you own or use a project that you believe should be part of the list, please open a PR to add it!
+
+## If you are looking for custom support from the Hugging Face team
+
+<a target="_blank" href="https://huggingface.co/support">
+    <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
+</a><br>
+
+## Quick tour
+
+To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:
+
+```python
+>>> from transformers import pipeline
+
+# Allocate a pipeline for sentiment-analysis
+>>> classifier = pipeline('sentiment-analysis')
+>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
+[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
 ```
 
-Install Transformers in your virtual environment.
+The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%.
 
-```py
-# pip
-pip install "transformers[torch]"
+Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image:
 
-# uv
-uv pip install "transformers[torch]"
+``` python
+>>> import requests
+>>> from PIL import Image
+>>> from transformers import pipeline
+
+# Download an image with cute cats
+>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
+>>> image_data = requests.get(url, stream=True).raw
+>>> image = Image.open(image_data)
+
+# Allocate a pipeline for object detection
+>>> object_detector = pipeline('object-detection')
+>>> object_detector(image)
+[{'score': 0.9982201457023621,
+  'label': 'remote',
+  'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
+ {'score': 0.9960021376609802,
+  'label': 'remote',
+  'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
+ {'score': 0.9954745173454285,
+  'label': 'couch',
+  'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
+ {'score': 0.9988006353378296,
+  'label': 'cat',
+  'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
+ {'score': 0.9986783862113953,
+  'label': 'cat',
+  'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
 ```
 
-Install Transformers from source if you want the latest changes in the library or are interested in contributing. However, the *latest* version may not be stable. Feel free to open an [issue](https://github.com/huggingface/transformers/issues) if you encounter an error.
-
-```shell
-git clone https://github.com/huggingface/transformers.git
-cd transformers
-
-# pip
-pip install .[torch]
-
-# uv
-uv pip install .[torch]
-```
-
-## Quickstart
-
-Get started with Transformers right away with the [Pipeline](https://huggingface.co/docs/transformers/pipeline_tutorial) API. The `Pipeline` is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.
-
-Instantiate a pipeline and specify model to use for text generation. The model is downloaded and cached so you can easily reuse it again. Finally, pass some text to prompt the model.
-
-```py
-from transformers import pipeline
-
-pipeline = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
-pipeline("the secret to baking a really good cake is ")
-[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]
-```
-
-To chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to `Pipeline`) between you and the system.
-
-> [!TIP]
-> You can also chat with a model directly from the command line.
-> ```shell
-> transformers chat Qwen/Qwen2.5-0.5B-Instruct
-> ```
-
-```py
-import torch
-from transformers import pipeline
-
-chat = [
-    {"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
-    {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
-]
-
-pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
-response = pipeline(chat, max_new_tokens=512)
-print(response[0]["generated_text"][-1]["content"])
-```
-
-Expand the examples below to see how `Pipeline` works for different modalities and tasks.
-
-<details>
-<summary>Automatic speech recognition</summary>
-
-```py
-from transformers import pipeline
-
-pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-large-v3")
-pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
-{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
-```
-
-</details>
-
-<details>
-<summary>Image classification</summary>
+Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right:
 
 <h3 align="center">
-    <a><img src="https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"></a>
+    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
+    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
 </h3>
 
-```py
-from transformers import pipeline
+You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary).
 
-pipeline = pipeline(task="image-classification", model="facebook/dinov2-small-imagenet1k-1-layer")
-pipeline("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
-[{'label': 'macaw', 'score': 0.997848391532898},
- {'label': 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
-  'score': 0.0016551691805943847},
- {'label': 'lorikeet', 'score': 0.00018523589824326336},
- {'label': 'African grey, African gray, Psittacus erithacus',
-  'score': 7.85409429227002e-05},
- {'label': 'quail', 'score': 5.502637941390276e-05}]
+In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
 ```
 
-</details>
+And here is the equivalent code for TensorFlow:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
 
-<details>
-<summary>Visual question answering</summary>
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
 
-
-<h3 align="center">
-    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg"></a>
-</h3>
-
-```py
-from transformers import pipeline
-
-pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base")
-pipeline(
-    image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg",
-    question="What is in the image?",
-)
-[{'answer': 'statue of liberty'}]
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
 ```
 
-</details>
+The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.
 
-## Why should I use Transformers?
+The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.
+
+## Why should I use transformers?
 
 1. Easy-to-use state-of-the-art models:
-    - High performance on natural language understanding & generation, computer vision, audio, video, and multimodal tasks.
-    - Low barrier to entry for researchers, engineers, and developers.
+    - High performance on natural language understanding & generation, computer vision, and audio tasks.
+    - Low barrier to entry for educators and practitioners.
     - Few user-facing abstractions with just three classes to learn.
     - A unified API for using all our pretrained models.
 
 1. Lower compute costs, smaller carbon footprint:
-    - Share trained models instead of training from scratch.
-    - Reduce compute time and production costs.
-    - Dozens of model architectures with 1M+ pretrained checkpoints across all modalities.
+    - Researchers can share trained models instead of always retraining.
+    - Practitioners can reduce compute time and production costs.
+    - Dozens of architectures with over 400,000 pretrained models across all modalities.
 
-1. Choose the right framework for every part of a models lifetime:
+1. Choose the right framework for every part of a model's lifetime:
     - Train state-of-the-art models in 3 lines of code.
-    - Move a single model between PyTorch/JAX/TF2.0 frameworks at will.
-    - Pick the right framework for training, evaluation, and production.
+    - Move a single model between TF2.0/PyTorch/JAX frameworks at will.
+    - Seamlessly pick the right framework for training, evaluation, and production.
 
 1. Easily customize a model or an example to your needs:
     - We provide examples for each architecture to reproduce the results published by its original authors.
     - Model internals are exposed as consistently as possible.
     - Model files can be used independently of the library for quick experiments.
 
-<a target="_blank" href="https://huggingface.co/enterprise">
-    <img alt="Hugging Face Enterprise Hub" src="https://github.com/user-attachments/assets/247fb16d-d251-4583-96c4-d3d76dda4925">
-</a><br>
-
-## Why shouldn't I use Transformers?
+## Why shouldn't I use transformers?
 
 - This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.
-- The training API is optimized to work with PyTorch models provided by Transformers. For generic machine learning loops, you should use another library like [Accelerate](https://huggingface.co/docs/accelerate).
-- The [example scripts]((https://github.com/huggingface/transformers/tree/main/examples)) are only *examples*. They may not necessarily work out-of-the-box on your specific use case and you'll need to adapt the code for it to work.
+- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)).
+- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.
 
-## 100 projects using Transformers
+## Installation
 
-Transformers is more than a toolkit to use pretrained models, it's a community of projects built around it and the
-Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone
-else to build their dream projects.
+### With pip
 
-In order to celebrate Transformers 100,000 stars, we wanted to put the spotlight on the
-community with the [awesome-transformers](./awesome-transformers.md) page which lists 100
-incredible projects built with Transformers.
+This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.
 
-If you own or use a project that you believe should be part of the list, please open a PR to add it!
+You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
-## Example models
+First, create a virtual environment with the version of Python you're going to use and activate it.
 
-You can test most of our models directly on their [Hub model pages](https://huggingface.co/models).
+Then, you will need to install at least one of Flax, PyTorch, or TensorFlow.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform.
 
-Expand each modality below to see a few example models for various use cases.
+When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
 
-<details>
-<summary>Audio</summary>
+```bash
+pip install transformers
+```
 
-- Audio classification with [Whisper](https://huggingface.co/openai/whisper-large-v3-turbo)
-- Automatic speech recognition with [Moonshine](https://huggingface.co/UsefulSensors/moonshine)
-- Keyword spotting with [Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
-- Speech to speech generation with [Moshi](https://huggingface.co/kyutai/moshiko-pytorch-bf16)
-- Text to audio with [MusicGen](https://huggingface.co/facebook/musicgen-large)
-- Text to speech with [Bark](https://huggingface.co/suno/bark)
+If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).
 
-</details>
+### With conda
 
-<details>
-<summary>Computer vision</summary>
+🤗 Transformers can be installed using conda as follows:
 
-- Automatic mask generation with [SAM](https://huggingface.co/facebook/sam-vit-base)
-- Depth estimation with [DepthPro](https://huggingface.co/apple/DepthPro-hf)
-- Image classification with [DINO v2](https://huggingface.co/facebook/dinov2-base)
-- Keypoint detection with [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor)
-- Keypoint matching with [SuperGlue](https://huggingface.co/magic-leap-community/superglue)
-- Object detection with [RT-DETRv2](https://huggingface.co/PekingU/rtdetr_v2_r50vd)
-- Pose Estimation with [VitPose](https://huggingface.co/usyd-community/vitpose-base-simple)
-- Universal segmentation with [OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_swin_large)
-- Video classification with [VideoMAE](https://huggingface.co/MCG-NJU/videomae-large)
+```shell script
+conda install conda-forge::transformers
+```
 
-</details>
+> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated.
 
-<details>
-<summary>Multimodal</summary>
+Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
 
-- Audio or text to text with [Qwen2-Audio](https://huggingface.co/Qwen/Qwen2-Audio-7B)
-- Document question answering with [LayoutLMv3](https://huggingface.co/microsoft/layoutlmv3-base)
-- Image or text to text with [Qwen-VL](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
-- Image captioning [BLIP-2](https://huggingface.co/Salesforce/blip2-opt-2.7b)
-- OCR-based document understanding with [GOT-OCR2](https://huggingface.co/stepfun-ai/GOT-OCR-2.0-hf)
-- Table question answering with [TAPAS](https://huggingface.co/google/tapas-base)
-- Unified multimodal understanding and generation with [Emu3](https://huggingface.co/BAAI/Emu3-Gen)
-- Vision to text with [Llava-OneVision](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf)
-- Visual question answering with [Llava](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
-- Visual referring expression segmentation with [Kosmos-2](https://huggingface.co/microsoft/kosmos-2-patch14-224)
+> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).
 
-</details>
+## Model architectures
 
-<details>
-<summary>NLP</summary>
+**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
 
-- Masked word completion with [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)
-- Named entity recognition with [Gemma](https://huggingface.co/google/gemma-2-2b)
-- Question answering with [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
-- Summarization with [BART](https://huggingface.co/facebook/bart-large-cnn)
-- Translation with [T5](https://huggingface.co/google-t5/t5-base)
-- Text generation with [Llama](https://huggingface.co/meta-llama/Llama-3.2-1B)
-- Text classification with [Qwen](https://huggingface.co/Qwen/Qwen2.5-0.5B)
+Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)
 
-</details>
+🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them.
+
+To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## Learn more
+
+| Section | Description |
+|-|-|
+| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials |
+| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers |
+| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models |
+| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
+| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks |
+| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community |
 
 ## Citation
 

SECURITY.md

@@ -27,6 +27,13 @@ These models require the `trust_remote_code=True` parameter to be set when using
 the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you
 protect yourself from updates on the repository.
 
+#### Tools
+
+Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools
+yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them.
+
+Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup.
+
 ## Reporting a Vulnerability
 
 Feel free to submit vulnerability reports to [security@huggingface.co](mailto:security@huggingface.co), where someone from the HF security team will review and recommend next steps. If reporting a vulnerability specific to open source, please note [Huntr](https://huntr.com) is a vulnerability disclosure program for open source software.

awesome-transformers.md

@@ -15,7 +15,7 @@ to add it.
 
 Keywords: Open-source, LLaMa, GPT-J, instruction, assistant
 
-## [recommenders](https://github.com/recommenders-team/recommenders)
+## [recommenders](https://github.com/microsoft/recommenders)
 
 This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization
 
@@ -29,7 +29,7 @@ Keywords: inpainting, SD, Stable Diffusion
 
 ## [flair](https://github.com/flairNLP/flair)
 
-FLAIR is a powerful PyTorch NLP framework, covering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
+FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
 
 Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis
 
@@ -39,15 +39,15 @@ MindsDB is a low-code ML platform, which automates and integrates several ML fra
 
 Keywords: Database, low-code, AI table
 
-## [langchain](https://github.com/langchain-ai/langchain)
+## [langchain](https://github.com/hwchase17/langchain)
 
-[langchain](https://github.com/langchain-ai/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
+[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
 
 Keywords: LLMs, Large Language Models, Agents, Chains
 
-## [LlamaIndex](https://github.com/run-llama/llama_index)
+## [LlamaIndex](https://github.com/jerryjliu/llama_index)
 
-[LlamaIndex](https://github.com/run-llama/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retrieval mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
+[LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
 
 Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation 
 
@@ -146,9 +146,9 @@ Keywords: Framework, simplicity, NLP
 
 Keywords: LLM, Agents, HF Hub
 
-## [transformers.js](https://github.com/huggingface/transformers.js/)
+## [transformers.js](https://xenova.github.io/transformers.js/)
 
-[transformers.js](https://github.com/huggingface/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
+[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
 
 Keywords: Transformers, JavaScript, browser
 
@@ -288,7 +288,7 @@ Keywords: Music understanding, Music generation
 
 ## [dalle-flow](https://github.com/jina-ai/dalle-flow)
 
-DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. It leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
+DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
 The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR.
 
 Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR
@@ -437,7 +437,7 @@ Keywords: DALL-E, Russian
 
 Keywords: Knowledge Extraction, Knowledge Graphs
 
-## [Nebuly](https://github.com/nebuly-ai/optimate)
+## [Nebuly](https://github.com/nebuly-ai/nebuly)
 
 Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
 
@@ -526,7 +526,7 @@ Keywords: Model deployment, CLoud, Mobile, Edge
 
 ## [underthesea](https://github.com/undertheseanlp/underthesea)
 
-[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provide extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
+[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
 
 Keywords: Vietnamese, NLP
 

benchmark/README.md

@@ -1,49 +0,0 @@
-# Benchmarks
-
-You might want to add new benchmarks.
-
-You will need to define a python function named `run_benchmark` in your python file and the file must be located in this `benchmark/` directory.
-
-The expected function signature is the following:
-
-```py
-def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100):
-```
-
-## Writing metrics to the database
-
-`MetricsRecorder` is thread-safe, in the sense of the python [`Thread`](https://docs.python.org/3/library/threading.html#threading.Thread). This means you can start a background thread to do the readings on the device measurements while not blocking the main thread to execute the model measurements.
-
-cf [`llama.py`](./llama.py) to see an example of this in practice.
-
-```py
-from benchmarks_entrypoint import MetricsRecorder
-import psycopg2
-
-def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100):
-  metrics_recorder = MetricsRecorder(psycopg2.connect("dbname=metrics"), logger, branch, commit_id, commit_msg)
-  benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id})
-    # To collect device measurements
-    metrics_recorder.collect_device_measurements(
-        benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes
-    )
-    # To collect your model measurements
-    metrics_recorder.collect_model_measurements(
-        benchmark_id,
-        {
-            "model_load_time": model_load_time,
-            "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
-            "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
-            "first_eager_generate_time_secs": first_eager_generate_time,
-            "second_eager_generate_time_secs": second_eager_generate_time,
-            "time_to_first_token_secs": time_to_first_token,
-            "time_to_second_token_secs": time_to_second_token,
-            "time_to_third_token_secs": time_to_third_token,
-            "time_to_next_token_mean_secs": mean_time_to_next_token,
-            "first_compile_generate_time_secs": first_compile_generate_time,
-            "second_compile_generate_time_secs": second_compile_generate_time,
-            "third_compile_generate_time_secs": third_compile_generate_time,
-            "fourth_compile_generate_time_secs": fourth_compile_generate_time,
-        },
-    )
-```

benchmark/benchmark.py

@@ -90,7 +90,7 @@ def summarize(run_dir, metrics, expand_metrics=False):
 
         model = benchmark.config.backend["model"]
 
-        # This looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`.
+        # Ths looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`.
         # (we rely on the usage of hydra's `${hydra.job.override_dirname}`.)
         benchmark_name = re.sub(f"backend.model={model},*", "", report_dir)
         benchmark_name = str(Path(benchmark_name).parts[-1])

benchmark/benchmarks_entrypoint.py

@@ -1,152 +0,0 @@
-import argparse
-import importlib.util
-import logging
-import os
-import sys
-from typing import Dict, Tuple
-
-from psycopg2.extensions import register_adapter
-from psycopg2.extras import Json
-
-
-register_adapter(dict, Json)
-
-
-class ImportModuleException(Exception):
-    pass
-
-
-class MetricsRecorder:
-    def __init__(
-        self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str
-    ):
-        self.conn = connection
-        self.conn.autocommit = True
-        self.logger = logger
-        self.repository = repository
-        self.branch = branch
-        self.commit_id = commit_id
-        self.commit_msg = commit_msg
-
-    def initialise_benchmark(self, metadata: dict[str, str]) -> int:
-        """
-        Creates a new benchmark, returns the benchmark id
-        """
-        # gpu_name: str, model_id: str
-        with self.conn.cursor() as cur:
-            cur.execute(
-                "INSERT INTO benchmarks (repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s) RETURNING benchmark_id",
-                (self.repository, self.branch, self.commit_id, self.commit_msg, metadata),
-            )
-            benchmark_id = cur.fetchone()[0]
-            logger.debug(f"initialised benchmark #{benchmark_id}")
-            return benchmark_id
-
-    def collect_device_measurements(self, benchmark_id: int, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes):
-        """
-        Collect device metrics, such as CPU & GPU usage. These are "static", as in you cannot pass arbitrary arguments to the function.
-        """
-        with self.conn.cursor() as cur:
-            cur.execute(
-                "INSERT INTO device_measurements (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes) VALUES (%s, %s, %s, %s, %s)",
-                (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes),
-            )
-        self.logger.debug(
-            f"inserted device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]"
-        )
-
-    def collect_model_measurements(self, benchmark_id: int, measurements: dict[str, float]):
-        with self.conn.cursor() as cur:
-            cur.execute(
-                """
-                INSERT INTO model_measurements (
-                    benchmark_id,
-                    measurements
-                ) VALUES (%s, %s)
-                """,
-                (
-                    benchmark_id,
-                    measurements,
-                ),
-            )
-        self.logger.debug(f"inserted model measurements for benchmark #{benchmark_id}: {measurements}")
-
-    def close(self):
-        self.conn.close()
-
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-
-handler = logging.StreamHandler(sys.stdout)
-handler.setLevel(logging.INFO)
-formatter = logging.Formatter("[%(levelname)s - %(asctime)s] %(message)s")
-handler.setFormatter(formatter)
-logger.addHandler(handler)
-
-
-def parse_arguments() -> tuple[str, str, str, str]:
-    """
-    Parse command line arguments for the benchmarking CLI.
-    """
-    parser = argparse.ArgumentParser(description="CLI for benchmarking the huggingface/transformers.")
-
-    parser.add_argument(
-        "repository",
-        type=str,
-        help="The repository name on which the benchmarking is performed.",
-    )
-
-    parser.add_argument(
-        "branch",
-        type=str,
-        help="The branch name on which the benchmarking is performed.",
-    )
-
-    parser.add_argument(
-        "commit_id",
-        type=str,
-        help="The commit hash on which the benchmarking is performed.",
-    )
-
-    parser.add_argument(
-        "commit_msg",
-        type=str,
-        help="The commit message associated with the commit, truncated to 70 characters.",
-    )
-
-    args = parser.parse_args()
-
-    return args.repository, args.branch, args.commit_id, args.commit_msg
-
-
-def import_from_path(module_name, file_path):
-    try:
-        spec = importlib.util.spec_from_file_location(module_name, file_path)
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[module_name] = module
-        spec.loader.exec_module(module)
-        return module
-    except Exception as e:
-        raise ImportModuleException(f"failed to load python module: {e}")
-
-
-if __name__ == "__main__":
-    benchmarks_folder_path = os.path.dirname(os.path.realpath(__file__))
-
-    repository, branch, commit_id, commit_msg = parse_arguments()
-
-    for entry in os.scandir(benchmarks_folder_path):
-        try:
-            if not entry.name.endswith(".py"):
-                continue
-            if entry.path == __file__:
-                continue
-            logger.debug(f"loading: {entry.name}")
-            module = import_from_path(entry.name.split(".")[0], entry.path)
-            logger.info(f"running benchmarks in: {entry.name}")
-            module.run_benchmark(logger, repository, branch, commit_id, commit_msg)
-        except ImportModuleException as e:
-            logger.error(e)
-        except Exception as e:
-            logger.error(f"error running benchmarks for {entry.name}: {e}")

benchmark/default.yml

@@ -1,10 +0,0 @@
-apiVersion: 1
-
-providers:
-  - name: 'Transformers Benchmarks'
-    orgId: 1
-    type: file
-    updateIntervalSeconds: 10
-    allowUiUpdates: true
-    options:
-      path: /etc/grafana/dashboards

benchmark/grafana_datasource.yaml

@@ -1,17 +0,0 @@
-apiVersion: 1
-datasources:
-  - name: grafana-postgresql-datasource
-    uid: be28nkzirtb0gd
-    type: postgres
-    url: $GRAFANA_POSTGRES_DATASOURCE_URL
-    user: $GRAFANA_POSTGRES_DATASOURCE_USER
-    secureJsonData:
-      password: $GRAFANA_POSTGRES_DATASOURCE_PWD
-    jsonData:
-      database: metrics
-      maxOpenConns: 100
-      maxIdleConns: 100
-      maxIdleConnsAuto: true
-      connMaxLifetime: 14400
-      postgresVersion: 1000
-      timescaledb: false

benchmark/init_db.sql

@@ -1,34 +0,0 @@
-CREATE TABLE IF NOT EXISTS benchmarks (
-  benchmark_id SERIAL PRIMARY KEY,
-  repository VARCHAR(255),
-  branch VARCHAR(255),
-  commit_id VARCHAR(72),
-  commit_message VARCHAR(70),
-  metadata jsonb,
-  created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
-);
-
-CREATE INDEX IF NOT EXISTS benchmarks_benchmark_id_idx ON benchmarks (benchmark_id);
-
-CREATE INDEX IF NOT EXISTS benchmarks_branch_idx ON benchmarks (branch);
-
-CREATE TABLE IF NOT EXISTS device_measurements (
-  measurement_id SERIAL PRIMARY KEY,
-  benchmark_id int REFERENCES benchmarks (benchmark_id),
-  cpu_util double precision,
-  mem_megabytes double precision,
-  gpu_util double precision,
-  gpu_mem_megabytes double precision,
-  time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
-);
-
-CREATE INDEX IF NOT EXISTS device_measurements_branch_idx ON device_measurements (benchmark_id);
-
-CREATE TABLE IF NOT EXISTS model_measurements (
-  measurement_id SERIAL PRIMARY KEY,
-  benchmark_id int REFERENCES benchmarks (benchmark_id),
-  measurements jsonb,
-  time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
-);
-
-CREATE INDEX IF NOT EXISTS model_measurements_branch_idx ON model_measurements (benchmark_id);

benchmark/llama.py

@@ -1,346 +0,0 @@
-from logging import Logger
-import os
-from threading import Event, Thread
-from time import perf_counter, sleep
-from typing import Optional
-from benchmarks_entrypoint import MetricsRecorder
-import gpustat
-import psutil
-import psycopg2
-import torch
-
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
-
-
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-
-os.environ["TOKENIZERS_PARALLELISM"] = "1"
-torch.set_float32_matmul_precision("high")
-
-
-def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder):
-    p = psutil.Process(os.getpid())
-    while not continue_metric_collection.is_set():
-        with p.oneshot():
-            cpu_util = p.cpu_percent()
-            mem_megabytes = p.memory_info().rss / (1024 * 1024)
-        gpu_stats = gpustat.GPUStatCollection.new_query()
-        gpu_util = gpu_stats[0]["utilization.gpu"]
-        gpu_mem_megabytes = gpu_stats[0]["memory.used"]
-        metrics_recorder.collect_device_measurements(
-            benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes
-        )
-        sleep(0.01)
-
-
-def run_benchmark(
-    logger: Logger, repository: str, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100
-):
-    continue_metric_collection = Event()
-    metrics_thread = None
-    model_id = "meta-llama/Llama-2-7b-hf"
-    metrics_recorder = MetricsRecorder(
-        psycopg2.connect("dbname=metrics"), logger, repository, branch, commit_id, commit_msg
-    )
-    try:
-        gpu_stats = gpustat.GPUStatCollection.new_query()
-        gpu_name = gpu_stats[0]["name"]
-        benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id})
-        logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}")
-        metrics_thread = Thread(
-            target=collect_metrics,
-            args=[benchmark_id, continue_metric_collection, metrics_recorder],
-        )
-        metrics_thread.start()
-        logger.info("started background thread to fetch device metrics")
-
-        os.environ["TOKENIZERS_PARALLELISM"] = "false"  # silence warnings when compiling
-
-        device = "cuda"
-
-        logger.info("downloading weights")
-        # This is to avoid counting download in model load time measurement
-        model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
-        gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1)
-        logger.info("loading model")
-        start = perf_counter()
-        model = AutoModelForCausalLM.from_pretrained(
-            model_id, torch_dtype=torch.float16, generation_config=gen_config
-        ).eval()
-        model.to(device)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        model_load_time = end - start
-        logger.info(f"loaded model in: {model_load_time}s")
-
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-        prompt = "Why dogs are so cute?"
-        inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-        # Specify the max length (including both the prompt and the response)
-        # When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object
-        # with sequence length = `max_length`. The longer the more you will re-use it
-        seq_length = inputs["input_ids"].shape[1]
-        model.generation_config.max_length = seq_length + num_tokens_to_generate
-        batch_size = inputs["input_ids"].shape[0]
-
-        # Copied from the gpt-fast repo
-        def multinomial_sample_one_no_sync(probs_sort):  # Does multinomial sampling without a cuda synchronization
-            q = torch.empty_like(probs_sort).exponential_(1)
-            return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
-
-        def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None):
-            logits = logits / max(temperature, 1e-5)
-
-            if top_k is not None:
-                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
-                pivot = v.select(-1, -1).unsqueeze(-1)
-                logits = torch.where(logits < pivot, -float("Inf"), logits)
-            probs = torch.nn.functional.softmax(logits, dim=-1)
-            return probs
-
-        def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
-            probs = logits_to_probs(logits[:, -1], temperature, top_k)
-            idx_next = multinomial_sample_one_no_sync(probs)
-            return idx_next, probs
-
-        def decode_one_token(model, cur_token, cache_position, past_key_values):
-            logits = model(
-                cur_token,
-                cache_position=cache_position,
-                past_key_values=past_key_values,
-                return_dict=False,
-                use_cache=True,
-            )[0]
-            new_token = sample(logits, temperature=0.6, top_k=5)[0]
-            return new_token
-
-        #########
-        # Eager #
-        #########
-        with torch.no_grad():
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            start = perf_counter()
-            model(
-                **inputs,
-                cache_position=cache_position,
-                past_key_values=past_key_values,
-                return_dict=False,
-                use_cache=True,
-            )
-            end = perf_counter()
-            first_eager_fwd_pass_time = end - start
-            logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s")
-            start = perf_counter()
-            output = model.generate(**inputs, do_sample=False)
-            end = perf_counter()
-            first_eager_generate_time = end - start
-            logger.info(f"completed first eager generation in: {first_eager_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            start = perf_counter()
-            model(
-                **inputs,
-                cache_position=cache_position,
-                past_key_values=past_key_values,
-                return_dict=False,
-                use_cache=True,
-            )
-            end = perf_counter()
-            second_eager_fwd_pass_time = end - start
-            logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s")
-            start = perf_counter()
-            model.generate(**inputs, do_sample=False)
-            end = perf_counter()
-            second_eager_generate_time = end - start
-            logger.info(f"completed second eager generation in: {second_eager_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            torch.compiler.reset()
-
-            ################
-            # Forward pass #
-            ################
-
-            # `torch.compile(model, ...)` is not recommended as you compile callbacks
-            # and full generate. We recommend compiling only the forward for now.
-            # "reduce-overhead" will use cudagraphs.
-            generated_ids = torch.zeros(
-                (batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device
-            )
-
-            generated_ids[:, :seq_length] = inputs["input_ids"]
-            decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True)
-            # model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
-            # TODO use  decode_one_token(model, input_id.clone(), cache_position) for verification
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate + 10,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            all_generated_tokens = []
-            ### First compile, prefill
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_first_token = end - start
-            logger.info(f"completed first compile generation in: {time_to_first_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            cache_position = torch.tensor([seq_length], device=device)
-            ### First compile, decoding
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_second_token = end - start
-            logger.info(f"completed second compile generation in: {time_to_second_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            ### Second compile, decoding
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_third_token = end - start
-            logger.info(f"completed third compile forward in: {time_to_third_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            ### Using cuda graphs decoding
-
-            start = perf_counter()
-            for _ in range(1, num_tokens_to_generate):
-                all_generated_tokens += next_token.tolist()
-                next_token = decode_one_token(
-                    model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-                )
-                cache_position += 1
-            torch.cuda.synchronize()
-            end = perf_counter()
-            mean_time_to_next_token = (end - start) / num_tokens_to_generate
-            logger.info(f"completed next compile generation in: {mean_time_to_next_token}s")
-            logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}")
-
-            ####################
-            # Generate compile #
-            ####################
-            torch.compiler.reset()
-            # we will not compile full generate as it' s to intensive, tho we measure full forward!
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-
-            # 1st call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            first_compile_generate_time = end - start
-            logger.info(f"completed first compile generation in: {first_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-            # 2nd call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            second_compile_generate_time = end - start
-            logger.info(f"completed second compile generation in: {second_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-
-            # 3rd call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            end = perf_counter()
-            third_compile_generate_time = end - start
-            logger.info(f"completed third compile generation in: {third_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-            # 4th call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            end = perf_counter()
-            fourth_compile_generate_time = end - start
-            logger.info(f"completed fourth compile generation in: {fourth_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        metrics_recorder.collect_model_measurements(
-            benchmark_id,
-            {
-                "model_load_time": model_load_time,
-                "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
-                "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
-                "first_eager_generate_time_secs": first_eager_generate_time,
-                "second_eager_generate_time_secs": second_eager_generate_time,
-                "time_to_first_token_secs": time_to_first_token,
-                "time_to_second_token_secs": time_to_second_token,
-                "time_to_third_token_secs": time_to_third_token,
-                "time_to_next_token_mean_secs": mean_time_to_next_token,
-                "first_compile_generate_time_secs": first_compile_generate_time,
-                "second_compile_generate_time_secs": second_compile_generate_time,
-                "third_compile_generate_time_secs": third_compile_generate_time,
-                "fourth_compile_generate_time_secs": fourth_compile_generate_time,
-            },
-        )
-    except Exception as e:
-        logger.error(f"Caught exception: {e}")
-    continue_metric_collection.set()
-    if metrics_thread is not None:
-        metrics_thread.join()
-    metrics_recorder.close()

benchmark/requirements.txt

@@ -1,5 +0,0 @@
-gpustat==1.1.1
-psutil==6.0.0
-psycopg2==2.9.9
-torch>=2.4.0
-hf_transfer

conftest.py

@@ -46,6 +46,10 @@ NOT_DEVICE_TESTS = {
     "test_keep_in_fp32_modules",
     "test_gradient_checkpointing_backward_compatibility",
     "test_gradient_checkpointing_enable_disable",
+    "test_save_load_fast_init_from_base",
+    "test_fast_init_context_manager",
+    "test_fast_init_tied_embeddings",
+    "test_save_load_fast_init_to_base",
     "test_torch_save_load",
     "test_initialization",
     "test_forward_signature",
@@ -57,6 +61,7 @@ NOT_DEVICE_TESTS = {
     "test_load_save_without_tied_weights",
     "test_tied_weights_keys",
     "test_model_weights_reload_no_missing_tied_weights",
+    "test_pt_tf_model_equivalence",
     "test_mismatched_shapes_have_properly_initialized_weights",
     "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
     "test_model_is_small",
@@ -66,6 +71,7 @@ NOT_DEVICE_TESTS = {
     "ModelTester::test_pipeline_",
     "/repo_utils/",
     "/utils/",
+    "/agents/",
 }
 
 # allow having multiple repository checkouts and not needing to remember to rerun
@@ -79,9 +85,16 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_configure(config):
+    config.addinivalue_line(
+        "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"
+    )
+    config.addinivalue_line(
+        "markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"
+    )
     config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested")
     config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment")
     config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate")
+    config.addinivalue_line("markers", "agent_tests: mark the agent tests that are run on their specific schedule")
     config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu")
 
 

docker/README.md

@@ -1,9 +0,0 @@
-# Dockers for `transformers`
-
-In this folder you will find various docker files, and some subfolders. 
-- dockerfiles (ex: `consistency.dockerfile`) present under `~/docker` are used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For example `torch-light` is a very light weights container (703MiB). 
-- subfolders contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs)
-
-Note that in both case, you need to run `uv pip install -e .`, which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the `transformers` code is thus updated. 
-
-We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: 

docker/consistency.dockerfile

@@ -1,16 +1,16 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 USER root
 ARG REF=main
 RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython
-RUN uv pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
 # tensorflow pin matching setup.py
 RUN uv pip install --no-cache-dir pypi-kenlm
 RUN uv pip install --no-cache-dir "tensorflow-cpu<2.16" "tf-keras<2.16"
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,quality,testing,torch-speech,vision]"
 RUN git lfs install
 
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/custom-tokenizers.dockerfile

@@ -1,6 +1,5 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
-ARG REF=main
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler
 ENV UV_PYTHON=/usr/local/bin/python
@@ -18,10 +17,10 @@ RUN make install -j 10
 
 RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
+RUN uv pip install  --no-cache-dir "transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
 # spacy is not used so not tested. Causes to failures. TODO fix later
 RUN python3 -m unidic download
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*
 RUN apt remove -y g++ cmake  xz-utils libprotobuf-dev protobuf-compiler

docker/examples-tf.dockerfile

@@ -1,13 +1,12 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
-ARG REF=main
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git
 RUN apt-get install -y g++ cmake
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv
 RUN uv pip install --no-cache-dir -U pip setuptools albumentations seqeval
-RUN uv pip install  --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
+RUN pip install  --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
 RUN uv pip install --no-cache-dir  "protobuf==3.20.3" 
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/examples-torch.dockerfile

@@ -1,12 +1,11 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
-ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ffmpeg
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
-RUN uv pip uninstall transformers
+RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/exotic-models.dockerfile

@@ -1,17 +1,17 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv &&  uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps timm accelerate
 RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
 # RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels
-RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose'  'dataset'
+RUN pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose'  'dataset'
 # RUN git clone https://github.com/facebookresearch/detectron2.git
 # RUN python3 -m pip install --no-cache-dir -e detectron2
-RUN uv pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' --no-build-isolation
-RUN uv pip uninstall transformers
+RUN pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3'
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/jax-light.dockerfile

@@ -1,10 +1,10 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv &&  uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,testing,sentencepiece,flax-speech,vision]"
-RUN uv pip uninstall transformers
+RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,testing,sentencepiece,flax-speech,vision]"
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/pipeline-tf.dockerfile

@@ -1,10 +1,10 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake g++
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]"
+RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]"
 RUN uv pip install --no-cache-dir  "protobuf==3.20.3" tensorflow_probability
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/pipeline-torch.dockerfile

@@ -1,11 +1,11 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers

docker/quality.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root

docker/tf-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
@@ -6,7 +6,7 @@ RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-de
 RUN apt-get install -y  cmake
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install  --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
+RUN pip install  --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
 RUN uv pip install --no-cache-dir  "protobuf==3.20.3" 
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-jax-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
@@ -6,11 +6,11 @@ RUN apt-get update &&  apt-get install -y libsndfile1-dev espeak-ng time git g++
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-deps accelerate
-RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]"
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]"
 
 
 # RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]"
 
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-light.dockerfile

@@ -1,11 +1,11 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ffmpeg
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words,video]"
-RUN uv pip uninstall transformers
+RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken]"
+RUN pip uninstall -y transformers

docker/torch-tf-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 RUN echo ${REF}
@@ -7,13 +7,13 @@ RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-de
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN git lfs install
 
 RUN uv pip install --no-cache-dir pypi-kenlm
-RUN uv pip install --no-cache-dir  "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]"
+RUN pip install --no-cache-dir  "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]"
 RUN uv pip install --no-cache-dir  "protobuf==3.20.3" librosa
 
 
-RUN uv pip uninstall transformers
+RUN pip uninstall -y transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/transformers-all-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.6.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,11 +9,11 @@ SHELL ["sh", "-lc"]
 # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
 # to be used as arguments for docker build (so far).
 
-ARG PYTORCH='2.7.1'
+ARG PYTORCH='2.4.0'
+# (not always a valid torch version)
+ARG INTEL_TORCH_EXT='2.3.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
-# Disable kernel mapping for now until all tests pass
-ENV DISABLE_KERNEL_MAPPING=1
+ARG CUDA='cu121'
 
 RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs
@@ -26,10 +26,12 @@ RUN git clone https://github.com/huggingface/transformers && cd transformers &&
 # 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future.
 # 2. Regarding `torch` part, We might need to specify proper versions for `torchvision` and `torchaudio`.
 #    Currently, let's not bother to specify their versions explicitly (so installed with their latest release versions).
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA && python3 -m pip uninstall -y tensorflow tensorflow_text tensorflow_probability
+RUN python3 -m pip install --no-cache-dir -U tensorflow==2.13 protobuf==3.20.3 tensorflow_text tensorflow_probability && python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
 RUN python3 -m pip uninstall -y flax jax
 
+RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT -f https://developer.intel.com/ipex-whl-stable-cpu
+
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
@@ -41,7 +43,7 @@ RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/pef
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
 
 # For video model testing
-RUN python3 -m pip install --no-cache-dir av
+RUN python3 -m pip install --no-cache-dir decord av==9.2.0
 
 # Some slow tests require bnb
 RUN python3 -m pip install --no-cache-dir bitsandbytes
@@ -55,8 +57,7 @@ RUN python3 -m pip uninstall -y ninja
 
 # For `dinat` model
 # The `XXX` part in `torchXXX` needs to match `PYTORCH` (to some extent)
-# pin `0.17.4` otherwise `cannot import name 'natten2dav' from 'natten.functional'`
-RUN python3 -m pip install --no-cache-dir natten==0.17.4+torch250cu121 -f https://shi-labs.com/natten/wheels
+RUN python3 -m pip install --no-cache-dir natten==0.15.1+torch220$CUDA -f https://shi-labs.com/natten/wheels
 
 # For `nougat` tokenizer
 RUN python3 -m pip install --no-cache-dir python-Levenshtein
@@ -64,15 +65,6 @@ RUN python3 -m pip install --no-cache-dir python-Levenshtein
 # For `FastSpeech2ConformerTokenizer` tokenizer
 RUN python3 -m pip install --no-cache-dir g2p-en
 
-# For Some bitsandbytes tests
-RUN python3 -m pip install --no-cache-dir einops
-
-# For Some tests with `@require_liger_kernel`
-RUN python3 -m pip install --no-cache-dir liger-kernel
-
-# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
-RUN python3 -m pip uninstall -y kernels
-
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-past-gpu/Dockerfile

@@ -48,8 +48,8 @@ RUN python3 -m pip uninstall -y torch-tensorrt apex
 # Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed
 # This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
-# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
-# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
+# Issue: https://github.com/microsoft/DeepSpeed/issues/2010
+# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,19 +1,18 @@
-FROM rocm/pytorch:rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.6.0
+FROM rocm/dev-ubuntu-22.04:6.0.2
+# rocm/pytorch has no version with 2.1.0
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TORCH_VISION='0.21.0'
-ARG TORCH_AUDIO='2.6.0'
-
 RUN apt update && \
-    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs && \
+    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg && \
     apt clean && \
     rm -rf /var/lib/apt/lists/*
 
-RUN git lfs install
-
 RUN python3 -m pip install --no-cache-dir --upgrade pip numpy
+
+RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0
+
 RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0"
 
 ARG REF=main
@@ -23,7 +22,6 @@ WORKDIR /
 ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
-RUN python3 -m pip install --no-cache-dir torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
 
 RUN python3 -m pip uninstall -y tensorflow flax
@@ -32,8 +30,5 @@ RUN python3 -m pip uninstall -y tensorflow flax
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop
 
-# Remove nvml and nvidia-ml-py as it is not compatible with ROCm. apex is not tested on NVIDIA either.
-RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y
-
-# `kernels` may causes many failing tests
-RUN python3 -m pip uninstall -y kernels
+# Remove nvml as it is not compatible with ROCm. apex is not tested on NVIDIA either.
+RUN python3 -m pip uninstall py3nvml pynvml apex -y

docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile

@@ -1,11 +1,11 @@
-FROM rocm/dev-ubuntu-22.04:6.2.4
+FROM rocm/dev-ubuntu-22.04:5.6
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
-ARG PYTORCH='2.6.0'
-ARG TORCH_VISION='0.21.0'
-ARG TORCH_AUDIO='2.6.0'
-ARG ROCM='6.2.4'
+ARG PYTORCH='2.1.1'
+ARG TORCH_VISION='0.16.1'
+ARG TORCH_AUDIO='2.1.1'
+ARG ROCM='5.6'
 
 RUN apt update && \
     apt install -y --no-install-recommends \
@@ -16,11 +16,9 @@ RUN apt update && \
     python-is-python3 \
     rocrand-dev \
     rocthrust-dev \
-    rocblas-dev \
-    hipsolver-dev \
     hipsparse-dev \
     hipblas-dev \
-    hipblaslt-dev && \
+    rocblas-dev && \
     apt clean && \
     rm -rf /var/lib/apt/lists/*
 
@@ -47,7 +45,4 @@ RUN cd transformers && python3 setup.py develop
 RUN python3 -c "from deepspeed.launcher.runner import main"
 
 # Remove nvml as it is not compatible with ROCm
-RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y
-
-# `kernels` may causes many failing tests
-RUN python3 -m pip uninstall -y kernels
+RUN python3 -m pip uninstall py3nvml pynvml -y

docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile

@@ -1,12 +1,12 @@
-# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
-FROM nvcr.io/nvidia/pytorch:24.08-py3
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11
+FROM nvcr.io/nvidia/pytorch:23.04-py3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG PYTORCH='2.7.1'
+ARG PYTORCH='2.2.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
+ARG CUDA='cu121'
 
 RUN apt -y update
 RUN apt install -y libaio-dev
@@ -15,8 +15,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip
 ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
-# `datasets` requires pandas, pandas has some modules compiled with numpy=1.x causing errors
-RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'pandas<2' 'numpy<2'
+RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing]
 
 # Install latest release PyTorch
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
@@ -45,9 +44,6 @@ RUN python3 -m pip uninstall -y deepspeed
 # TODO: Find out why test fail.
 RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
-# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
-RUN python3 -m pip uninstall -y kernels
-
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile

@@ -1,11 +1,11 @@
 # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11
-FROM nvcr.io/nvidia/pytorch:24.08-py3
+FROM nvcr.io/nvidia/pytorch:23.11-py3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
+ARG CUDA='cu121'
 
 RUN apt -y update
 RUN apt install -y libaio-dev
@@ -21,8 +21,7 @@ RUN python3 -m pip uninstall -y torch torchvision torchaudio
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
 RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
-# `datasets` requires pandas, pandas has some modules compiled with numpy=1.x causing errors
-RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'pandas<2' 'numpy<2'
+RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing]
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
@@ -35,8 +34,8 @@ RUN python3 -m pip uninstall -y torch-tensorrt apex
 # Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed
 # This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
-# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
-# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
+# Issue: https://github.com/microsoft/DeepSpeed/issues/2010
+# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 ## For `torchdynamo` tests
@@ -57,9 +56,6 @@ RUN python3 -m pip uninstall -y deepspeed
 #RUN git clone https://github.com/pytorch/TensorRT.git
 #RUN cd TensorRT/py && python3 setup.py install --fx-only
 
-# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
-RUN python3 -m pip uninstall -y kernels
-
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.6.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -11,28 +11,23 @@ ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
 # If set to nothing, will install the latest version
-ARG PYTORCH='2.7.1'
+ARG PYTORCH='2.4.0'
 ARG TORCH_VISION=''
 ARG TORCH_AUDIO=''
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
+ARG CUDA='cu121'
 
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
-
-# Install torch stuff after ./transformers[dev-torch,testing,video], otherwise torch may be resolved to a previous
-# version.
 RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' ||  VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
+
 RUN python3 -m pip uninstall -y tensorflow flax
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
-# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
-RUN python3 -m pip uninstall -y kernels
-
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-xpu/Dockerfile

@@ -1,93 +0,0 @@
-FROM intel/deep-learning-essentials:2025.1.3-0-devel-ubuntu22.04 AS base
-LABEL maintainer="Hugging Face"
-
-SHELL ["/bin/bash", "-c"]
-
-ARG PYTHON_VER=3.11
-ENV TORCH_DEVICE_BACKEND_AUTOLOAD=0
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get remove -y python3.10 && apt-get autoremove -y
-RUN apt-get update && \
-    apt-get install -y software-properties-common && \
-    add-apt-repository -y ppa:deadsnakes/ppa && \
-    apt-get update && \
-    apt-get install -y python$PYTHON_VER python$PYTHON_VER-dev python3-pip && \
-    ln -sf /usr/bin/python$PYTHON_VER /usr/bin/python3 && \
-    ln -sf /usr/bin/python3 /usr/bin/python && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN apt-get update && \
-    apt-get -y install \
-        apt-utils \
-        build-essential \
-        ca-certificates \
-        clinfo \
-        curl \
-        git \
-        git-lfs \
-        vim \
-        numactl \
-        gnupg2 \
-        gpg-agent \
-        zlib1g-dev \
-        rsync \
-        sudo \
-        libnl-genl-3-200 \
-        xpu-smi \
-        unzip \
-        ffmpeg \
-        tesseract-ocr \
-        espeak-ng \
-        wget \
-        ncurses-term && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-
-RUN apt-get update && \
-    apt-get install -y \
-        linux-headers-$(uname -r) \
-        linux-modules-extra-$(uname -r) \
-        flex bison \
-        intel-fw-gpu intel-i915-dkms xpu-smi \
-        intel-opencl-icd libze-intel-gpu1 libze1 \
-        intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
-        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
-        libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc \
-        libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev && \
-    apt-get clean && \
-    rm -rf  /var/lib/apt/lists/*
-
-RUN pip install --upgrade pip
-RUN pip install triton==3.3.0
-
-RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/xpu --no-cache-dir
-
-RUN pip install evaluate torchdata pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sacremoses nltk rouge_score librosa soundfile g2p_en mpi4py requests_mock
-RUN pip install pretty_midi essentia resampy Levenshtein av sacrebleu phonemizer invisible_watermark schedulefree
-RUN pip install gguf hqq compressed_tensors gptqmodel mergekit autoawq deepspeed torchao onnx
-RUN pip install hf_transfer huggingface-hub hf-doc-builder datasets optimum-quanto timm transformers accelerate optimum peft
-
-RUN pip install git+https://github.com/linkedin/Liger-Kernel.git --extra-index-url https://download.pytorch.org/whl/test/xpu
-
-# install bitsandbytes
-RUN pip install git+https://github.com/bitsandbytes-foundation/bitsandbytes.git
-
-ENV OCL_ICD_VENDORS=/etc/OpenCL/vendors
-ENV FI_PROVIDER_PATH=${I_MPI_ROOT}/lib/libfabric/prov:/usr/lib/x86_64-linux-gnu/libfabric
-ENV CCL_ROOT=/usr/local
-ENV CCL_ATL_TRANSPORT=ofi
-ENV I_MPI_ROOT=/usr/local
-ENV CLASSPATH=${I_MPI_ROOT}/lib/mpi.jar
-ENV PATH=${I_MPI_ROOT}/bin/libfabric:${PATH}
-ENV LD_LIBRARY_PATH=${I_MPI_ROOT}/lib/libfabric:${LD_LIBRARY_PATH}
-
-RUN touch /entrypoint.sh
-RUN chmod +x /entrypoint.sh
-RUN echo "#!/bin/bash" >> /entrypoint.sh
-RUN echo "source /opt/intel/oneapi/setvars.sh --force && /bin/bash" >> /entrypoint.sh
-
-ENTRYPOINT ["/entrypoint.sh"]

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,14 +9,12 @@ SHELL ["sh", "-lc"]
 # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
 # to be used as arguments for docker build (so far).
 
-ARG PYTORCH='2.6.0'
+ARG PYTORCH='2.2.1'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu121'
-# Disable kernel mapping for quantization tests
-ENV DISABLE_KERNEL_MAPPING=1
+ARG CUDA='cu118'
 
 RUN apt update
-RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
+RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python python3-pip ffmpeg
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
 ARG REF=main
@@ -28,6 +26,8 @@ RUN echo torch=$VERSION
 # Currently, let's just use their latest releases (when `torch` is installed with a release version)
 RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
 
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
+
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
 # needed in bnb and awq
@@ -36,26 +36,15 @@ RUN python3 -m pip install --no-cache-dir einops
 # Add bitsandbytes for mixed int8 testing
 RUN python3 -m pip install --no-cache-dir bitsandbytes
 
-# Add gptqmodel for gtpq quantization testing, installed from source for pytorch==2.6.0 compatibility
-RUN python3 -m pip install lm_eval
-RUN git clone https://github.com/ModelCloud/GPTQModel.git && cd GPTQModel && pip install -v . --no-build-isolation
+# Add auto-gptq for gtpq quantization testing
+RUN python3 -m pip install --no-cache-dir auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # Add optimum for gptq quantization testing
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
 
-# Add PEFT
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft
-
 # Add aqlm for quantization testing
 RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
 
-# Add vptq for quantization testing
-RUN pip install vptq
-
-# Add spqr for quantization testing
-# Commented for now as No matching distribution found we need to reach out to the authors
-# RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
-
 # Add hqq for quantization testing
 RUN python3 -m pip install --no-cache-dir hqq
 
@@ -63,38 +52,14 @@ RUN python3 -m pip install --no-cache-dir hqq
 RUN python3 -m pip install --no-cache-dir gguf
 
 # Add autoawq for quantization testing
-# New release v0.2.8
-RUN python3 -m pip install --no-cache-dir autoawq[kernels]
+# >=v0.2.3 needed for compatibility with torch 2.2.1
+RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+cu118-cp38-cp38-linux_x86_64.whl
 
 # Add quanto for quantization testing
-RUN python3 -m pip install --no-cache-dir optimum-quanto
+RUN python3 -m pip install --no-cache-dir quanto
 
 # Add eetq for quantization testing
-RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submodule update --init --recursive && pip install .
-
-# # Add flute-kernel and fast_hadamard_transform for quantization testing
-# # Commented for now as they cause issues with the build
-# # TODO: create a new workflow to test them
-# RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
-# RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git
-
-# Add compressed-tensors for quantization testing
-RUN python3 -m pip install --no-cache-dir compressed-tensors
-
-# Add AMD Quark for quantization testing
-RUN python3 -m pip install --no-cache-dir amd-quark
-
-# Add AutoRound for quantization testing
-RUN python3 -m pip install --no-cache-dir "auto-round>=0.5.0"
-
-# Add transformers in editable mode
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
-
-# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
-RUN python3 -m pip uninstall -y kernels
-
-# Uninstall flash-attn installed by autoawq, it causes issues here : https://github.com/huggingface/transformers/actions/runs/15915442841/job/44892146131
-RUN python3 -m pip uninstall -y flash-attn
+RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git
 
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.

docker/transformers-tensorflow-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -18,7 +18,7 @@ RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' ||  VERSIO
 RUN python3 -m pip uninstall -y torch flax
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
-RUN python3 -m pip install --no-cache-dir -U "tensorflow_probability<0.22"
+RUN python3 -m pip install --no-cache-dir -U tensorflow_probability
 
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.

docs/README.md

@@ -276,14 +276,14 @@ building the return.
 
 Here's an example of a single value return:
 
-```python
+```
     Returns:
-        `list[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
 ```
 
 Here's an example of a tuple return, comprising several objects:
 
-```python
+```
     Returns:
         `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
         - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
@@ -322,9 +322,10 @@ includes an example of how to transcribe speech to text in the
 
 The syntax for Example docstrings can look as follows:
 
-```python
+```
     Example:
 
+    ```python
     >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
     >>> from datasets import load_dataset
     >>> import torch
@@ -346,6 +347,7 @@ The syntax for Example docstrings can look as follows:
     >>> transcription = processor.batch_decode(predicted_ids)
     >>> transcription[0]
     'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
+    ```
 ```
 
 The docstring should give a minimal, clear example of how the respective model 

docs/TRANSLATING.md

@@ -1,70 +1,57 @@
-# Translating the Transformers documentation into your language
+### Translating the Transformers documentation into your language
 
-As part of our mission to democratize machine learning, we aim to make the Transformers library available in many more languages! Follow the steps below to help translate the documentation into your language.
+As part of our mission to democratize machine learning, we'd love to make the Transformers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
 
-## Open an Issue
+**🗞️ Open an issue**
 
-1. Navigate to the Issues page of this repository.
-2. Check if anyone has already opened an issue for your language.
-3. If not, create a new issue by selecting the "Translation template" from the "New issue" button.
-4. Post a comment indicating which chapters you’d like to work on, and we’ll add your name to the list.
+To get started, navigate to the [Issues](https://github.com/huggingface/transformers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
 
-## Fork the Repository
+Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
 
-1. First, fork the Transformers repo by clicking the Fork button in the top-right corner.
-2. Clone your fork to your local machine for editing with the following command:
 
-    ```bash
-    git clone https://github.com/YOUR-USERNAME/transformers.git
-    ```
+**🍴 Fork the repository**
 
-   Replace `YOUR-USERNAME` with your GitHub username.
+First, you'll need to [fork the Transformers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
 
-## Copy-paste the English version with a new language code
+Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
 
-The documentation files are organized in the following directory:
+```bash
+git clone https://github.com/YOUR-USERNAME/transformers.git
+```
 
-- **docs/source**: This contains all documentation materials organized by language.
+**📋 Copy-paste the English version with a new language code**
 
-To copy the English version to your new language directory:
+The documentation files are in one leading directory:
 
-1. Navigate to your fork of the repository:
+- [`docs/source`](https://github.com/huggingface/transformers/tree/main/docs/source): All the documentation materials are organized here by language.
 
-    ```bash
-    cd ~/path/to/transformers/docs
-    ```
+You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/transformers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
 
-   Replace `~/path/to` with your actual path.
+```bash
+cd ~/path/to/transformers/docs
+cp -r source/en source/LANG-ID
+```
 
-2. Run the following command:
+Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
 
-    ```bash
-    cp -r source/en source/LANG-ID
-    ```
+**✍️ Start translating**
 
-   Replace `LANG-ID` with the appropriate ISO 639-1 or ISO 639-2 language code (see [this table](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) for reference).
+The fun part comes - translating the text!
 
-## Start translating
+The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website. 
 
-Begin translating the text!
+> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
 
-1. Start with the `_toctree.yml` file that corresponds to your documentation chapter. This file is essential for rendering the table of contents on the website.
+The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml):
 
-    - If the `_toctree.yml` file doesn’t exist for your language, create one by copying the English version and removing unrelated sections.
-    - Ensure it is placed in the `docs/source/LANG-ID/` directory.
-
-    Here’s an example structure for the `_toctree.yml` file:
-
-    ```yaml
-    - sections:
-      - local: pipeline_tutorial # Keep this name for your .md file
-        title: Pipelines for Inference # Translate this
+```yaml
+- sections:
+  - local: pipeline_tutorial # Do not change this! Use the same name for your .md file
+    title: Pipelines for inference # Translate this!
     ...
-      title: Tutorials # Translate this
-    ```
+  title: Tutorials # Translate this!
+```
 
-2. Once you’ve translated the `_toctree.yml`, move on to translating the associated MDX files.
+Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
 
-## Collaborate and share
-
-If you'd like assistance with your translation, open an issue and tag `@stevhliu`. Feel free to share resources or glossaries to ensure consistent terminology.
+> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/transformers/issues) and tag @stevhliu.

docs/source/ar/_config.py

@@ -1,14 +0,0 @@
-# docstyle-ignore
-INSTALL_CONTENT = """
-# Transformers installation
-! pip install transformers datasets evaluate accelerate
-# To install from source instead of the last release, comment the command above and uncomment the following one.
-# ! pip install git+https://github.com/huggingface/transformers.git
-"""
-
-notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
-black_avoid_patterns = {
-    "{processor_class}": "FakeProcessorClass",
-    "{model_class}": "FakeModelClass",
-    "{object_class}": "FakeObjectClass",
-}

docs/source/ar/_toctree.yml

@@ -1,894 +0,0 @@
-- sections:
-  - local: index
-    title: 🤗 المحولات
-  - local: quicktour
-    title: جولة سريعة
-  - local: installation
-    title: التثبيت
-  title: البدء
-- sections:
-  - local: pipeline_tutorial
-    title: تشغيل الاستنتاج باستخدام خطوط الأنابيب
-  - local: autoclass_tutorial
-    title: كتابة تعليمات برمجية متكيفه باستخدام AutoClass
-  - local: preprocessing
-    title: معالجة البيانات مسبقًا
-  - local: training
-    title: ضبط نموذج مسبق التدريب
-  - local: run_scripts
-    title: التدريب باستخدام نص برمجي
-  - local: accelerate
-    title: إعداد تدريب موزع باستخدام 🤗 Accelerate
-  - local: peft
-    title: تحميل النماذج المخصصة وتدريبها باستخدام 🤗 PEFT
-  - local: model_sharing
-    title: مشاركة نموذجك
-  - local: llm_tutorial
-    title: التوليد باستخدام LLMs
-  - local: conversations
-    title: الدردشة مع المحولات
-  title: البرامج التعليمية
-- sections:
-  - isExpanded: false
-    sections:
-    - local: tasks/sequence_classification
-      title: تصنيف النصوص
-    - local: tasks/token_classification
-      title: تصنيف الرموز
-    - local: tasks/question_answering
-      title: الإجابة على الأسئلة
-    - local: tasks/language_modeling
-      title: نمذجة اللغة السببية
-    - local: tasks/masked_language_modeling
-      title: نمذجة اللغة المقنعة
-    - local: tasks/translation
-      title: الترجمة
-    - local: tasks/summarization
-      title: التلخيص
-    - local: tasks/multiple_choice
-      title: الاختيار المتعدد
-    title: معالجة اللغات الطبيعية
-#   - isExpanded: false
-#     sections:
-#     - local: tasks/audio_classification
-#       title: تصنيف الصوت
-#     - local: tasks/asr
-#       title: التعرف التلقائي على الكلام
-#     title: الصوت
-#   - isExpanded: false
-#     sections:
-#     - local: tasks/image_classification
-#       title: تصنيف الصور
-#     - local: tasks/semantic_segmentation
-#       title: تجزئة الصور
-#     - local: tasks/video_classification
-#       title: تصنيف الفيديو
-#     - local: tasks/object_detection
-#       title: اكتشاف الأشياء
-#     - local: tasks/zero_shot_object_detection
-#       title: اكتشاف الأشياء بدون تدريب
-#     - local: tasks/zero_shot_image_classification
-#       title: تصنيف الصور بدون تدريب
-#     - local: tasks/monocular_depth_estimation
-#       title: تقدير العمق
-#     - local: tasks/image_to_image
-#       title: صورة إلى صورة
-#     - local: tasks/image_feature_extraction
-#       title: استخراج ميزات الصورة
-#     - local: tasks/mask_generation
-#       title: توليد القناع
-#     - local: tasks/knowledge_distillation_for_image_classification
-#       title: التقليل المعرفي للرؤية الحاسوبية
-#     title: الرؤية الحاسوبية
-#   - isExpanded: false
-#     sections:
-#     - local: tasks/image_captioning
-#       title: وصف الصور Image captioning
-#     - local: tasks/document_question_answering
-#       title: الإجابة على أسئلة المستندات
-#     - local: tasks/visual_question_answering
-#       title: الإجابة على الأسئلة المرئية
-#     - local: tasks/text-to-speech
-#       title: تحويل النص إلى كلام
-#     title: المتعددة الوسائط
-#   - isExpanded: false
-#     sections:
-#     - local: generation_strategies
-#       title: تخصيص استراتيجية التوليد
-#     - local: kv_cache
-#       title: أفضل الممارسات للتوليد باستخدام ذاكرة التخزين المؤقت
-#     title: التوليد
-#   - isExpanded: false
-#     sections:
-#     - local: tasks/idefics
-#       title: مهام الصور مع IDEFICS
-#     - local: tasks/prompting
-#       title: دليل إرشادي لمحفزات النماذج اللغوية الكبيرة
-#     title: الإرشاد
-  title: أدلة المهام
-- sections:
-  - local: fast_tokenizers
-    title: استخدم مجزئيات النصوص السريعة من 🤗 Tokenizers
-  - local: multilingual
-    title: الاستدلال باستخدام نماذج متعددة اللغات
-  - local: create_a_model
-    title: استخدام واجهات برمجة التطبيقات الخاصة بالنموذج
-  - local: custom_models
-    title: مشاركة نموذج مخصص
-  - local: chat_templating
-    title: قوالب لنماذج الدردشة
-  - local: trainer
-    title: المدرب
-  - local: sagemaker
-    title: تشغيل التدريب على Amazon SageMaker
-  - local: serialization
-    title: التصدير إلى ONNX
-  - local: tflite
-    title: التصدير إلى TFLite
-  - local: torchscript
-    title: التصدير إلى TorchScript
-  - local: notebooks
-    title: دفاتر الملاحظات مع الأمثلة
-  - local: community
-    title: موارد المجتمع
-  - local: troubleshooting
-    title: استكشاف الأخطاء وإصلاحها
-  - local: gguf
-    title: التوافق مع ملفات GGUF
-  - local: tiktoken
-    title: التوافق مع ملفات TikToken
-  - local: modular_transformers
-    title: الوحدات النمطية في `transformers`
-  - local: how_to_hack_models
-    title: اختراق النموذج (الكتابة فوق فئة لاستخدامك)
-  title: أدلة المطورين
-# - sections:
-#   - local: quantization/overview
-#     title: نظرة عامة
-#   - local: quantization/bitsandbytes
-#     title: bitsandbytes
-#   - local: quantization/gptq
-#     title: GPTQ
-#   - local: quantization/awq
-#     title: AWQ
-#   - local: quantization/aqlm
-#     title: AQLM
-#   - local: quantization/vptq
-#     title: VPTQ
-#   - local: quantization/quanto
-#     title: Quanto
-#   - local: quantization/eetq
-#     title: EETQ
-#   - local: quantization/hqq
-#     title: HQQ
-#   - local: quantization/optimum
-#     title: Optimum
-#   - local: quantization/contribute
-#     title: المساهمة بطريقة جديدة للتكميم
-#   title: أساليب التكميم
-# - sections:
-#   - local: performance
-#     title: الأداء-نظرة عامة
-#   - local: llm_optims
-#     title: تحسين الاستدلال LLM
-#   - sections:
-#     - local: perf_train_gpu_one
-#       title: استخدام عدة وحدات معالجة رسوميات (GPUs) بشكل متوازٍ
-#     - local: perf_train_gpu_many
-#       title: وحدات معالجة الرسومات (GPU) متعددة والتوازي
-#     - local: fsdp
-#       title: Fully Sharded Data Parallel
-#     - local: deepspeed
-#       title: DeepSpeed
-#     - local: perf_train_cpu
-#       title: التدريب الفعال على وحدة المعالجة المركزية (CPU)
-#     - local: perf_train_cpu_many
-#       title: التدريب الموزع لوحدة المعالجة المركزية (CPU)
-#     - local: perf_train_tpu_tf
-#       title: التدريب على (TPU) باستخدام TensorFlow
-#     - local: perf_train_special
-#       title: تدريب PyTorch على Apple silicon
-#     - local: perf_hardware
-#       title: الأجهزة المخصصة للتدريب
-#     - local: hpo_train
-#       title: البحث عن المعاملات المثلى باستخدام واجهة برمجة تطبيقات المدرب
-#     title: تقنيات التدريب الفعال
-#   - sections:
-#     - local: perf_infer_cpu
-#       title: الإستدلال على وحدة المعالجة المركزية (CPU)
-#     - local: perf_infer_gpu_one
-#       title: الإستدلال على وحدة معالجة الرسومات (GPU)
-#     title: تحسين الاستدلال
-#   - local: big_models
-#     title: إنشاء نموذج كبير
-#   - local: debugging
-#     title: تصحيح الأخطاء البرمجية
-#   - local: tf_xla
-#     title: تكامل XLA لنماذج TensorFlow
-#   - local: perf_torch_compile
-#     title: تحسين الاستدلال باستخدام `torch.compile()`
-#   title: الأداء وقابلية التوسع
-# - sections:
-#   - local: contributing
-#     title: كيفية المساهمة في 🤗 المحولات؟
-#   - local: add_new_model
-#     title: كيفية إضافة نموذج إلى 🤗 المحولات؟
-#   - local: add_new_pipeline
-#     title: كيفية إضافة خط أنابيب إلى 🤗 المحولات؟
-#   - local: testing
-#     title: الاختبار
-#   - local: pr_checks
-#     title: التحقق من طلب السحب
-#   title: المساهمة
-- sections:
-  - local: philosophy
-    title: الفلسفة
-  - local: glossary
-    title: (قاموس المصطلحات (قائمة الكلمات
-  - local: task_summary
-    title: ما الذي يمكن أن تفعله 🤗 المحولات
-  - local: tasks_explained
-    title: كيف تحل المحولات المهام
-  - local: model_summary
-    title: عائلة نماذج المحول
-  - local: tokenizer_summary
-    title: ملخص برنامج مقسم النصوص (tokenizers)
-  - local: attention
-    title: الانتباه Attention
-  - local: pad_truncation
-    title: الحشو والتقليم
-  - local: bertology
-    title: BERTology
-  - local: perplexity
-    title: حيرة النماذج ذات الطول الثابت
-  - local: pipeline_webserver
-    title: خطوط الأنابيب للاستدلال على خادم الويب
-  - local: model_memory_anatomy
-    title: تشريح تدريب النموذج
-  - local: llm_tutorial_optimization
-    title: الاستفادة القصوى من LLMs
-  title: أطر مفاهيمية
-# - sections:
-#   - sections:
-#     - local: model_doc/auto
-#       title: فئات يتم إنشاؤها ديناميكيًا
-#     - local: main_classes/backbones
-#       title: العمود الفقري
-#     - local: main_classes/callback
-#       title: عمليات الاسترجاع
-#     - local: main_classes/configuration
-#       title: التكوين
-#     - local: main_classes/data_collator
-#       title: مجمع البيانات
-#     - local: main_classes/keras_callbacks
-#       title: استدعاءات Keras
-#     - local: main_classes/logging
-#       title: التسجيل
-#     - local: main_classes/model
-#       title: النماذج
-#     - local: main_classes/text_generation
-#       title: توليد النصوص
-#     - local: main_classes/onnx
-#       title: ONNX
-#     - local: main_classes/optimizer_schedules
-#       title: التحسين
-#     - local: main_classes/output
-#       title: مخرجات النموذج
-#     - local: main_classes/pipelines
-#       title: خطوط الأنابيب
-#     - local: main_classes/processors
-#       title: المعالجات
-#     - local: main_classes/quantization
-#       title: التكميم
-#     - local: main_classes/tokenizer
-#       title: برنامج مقسم النصوص
-#     - local: main_classes/trainer
-#       title: المدرب
-#     - local: main_classes/deepspeed
-#       title: DeepSpeed
-#     - local: main_classes/feature_extractor
-#       title: مستخرج الميزات
-#     - local: main_classes/image_processor
-#       title: معالج الصور
-#     title: الفئات الرئيسية
-#   - sections:
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/albert
-#         title: ALBERT
-#       - local: model_doc/bart
-#         title: BART
-#       - local: model_doc/barthez
-#         title: BARThez
-#       - local: model_doc/bartpho
-#         title: BARTpho
-#       - local: model_doc/bert
-#         title: BERT
-#       - local: model_doc/bert-generation
-#         title: BertGeneration
-#       - local: model_doc/bert-japanese
-#         title: BertJapanese
-#       - local: model_doc/bertweet
-#         title: Bertweet
-#       - local: model_doc/big_bird
-#         title: BigBird
-#       - local: model_doc/bigbird_pegasus
-#         title: BigBirdPegasus
-#       - local: model_doc/biogpt
-#         title: BioGpt
-#       - local: model_doc/blenderbot
-#         title: Blenderbot
-#       - local: model_doc/blenderbot-small
-#         title: Blenderbot Small
-#       - local: model_doc/bloom
-#         title: BLOOM
-#       - local: model_doc/bort
-#         title: BORT
-#       - local: model_doc/byt5
-#         title: ByT5
-#       - local: model_doc/camembert
-#         title: CamemBERT
-#       - local: model_doc/canine
-#         title: CANINE
-#       - local: model_doc/codegen
-#         title: CodeGen
-#       - local: model_doc/code_llama
-#         title: CodeLlama
-#       - local: model_doc/cohere
-#         title: Cohere
-#       - local: model_doc/convbert
-#         title: ConvBERT
-#       - local: model_doc/cpm
-#         title: CPM
-#       - local: model_doc/cpmant
-#         title: CPMANT
-#       - local: model_doc/ctrl
-#         title: CTRL
-#       - local: model_doc/dbrx
-#         title: DBRX
-#       - local: model_doc/deberta
-#         title: DeBERTa
-#       - local: model_doc/deberta-v2
-#         title: DeBERTa-v2
-#       - local: model_doc/dialogpt
-#         title: DialoGPT
-#       - local: model_doc/distilbert
-#         title: DistilBERT
-#       - local: model_doc/dpr
-#         title: DPR
-#       - local: model_doc/electra
-#         title: ELECTRA
-#       - local: model_doc/encoder-decoder
-#         title: Encoder Decoder Models
-#       - local: model_doc/ernie
-#         title: ERNIE
-#       - local: model_doc/ernie_m
-#         title: ErnieM
-#       - local: model_doc/esm
-#         title: ESM
-#       - local: model_doc/falcon
-#         title: Falcon
-#       - local: model_doc/fastspeech2_conformer
-#         title: FastSpeech2Conformer
-#       - local: model_doc/flan-t5
-#         title: FLAN-T5
-#       - local: model_doc/flan-ul2
-#         title: FLAN-UL2
-#       - local: model_doc/flaubert
-#         title: FlauBERT
-#       - local: model_doc/fnet
-#         title: FNet
-#       - local: model_doc/fsmt
-#         title: FSMT
-#       - local: model_doc/funnel
-#         title: Funnel Transformer
-#       - local: model_doc/fuyu
-#         title: Fuyu
-#       - local: model_doc/gemma
-#         title: Gemma
-#       - local: model_doc/openai-gpt
-#         title: GPT
-#       - local: model_doc/gpt_neo
-#         title: GPT Neo
-#       - local: model_doc/gpt_neox
-#         title: GPT NeoX
-#       - local: model_doc/gpt_neox_japanese
-#         title: GPT NeoX Japanese
-#       - local: model_doc/gptj
-#         title: GPT-J
-#       - local: model_doc/gpt2
-#         title: GPT2
-#       - local: model_doc/gpt_bigcode
-#         title: GPTBigCode
-#       - local: model_doc/gptsan-japanese
-#         title: GPTSAN Japanese
-#       - local: model_doc/gpt-sw3
-#         title: GPTSw3
-#       - local: model_doc/herbert
-#         title: HerBERT
-#       - local: model_doc/ibert
-#         title: I-BERT
-#       - local: model_doc/jamba
-#         title: Jamba
-#       - local: model_doc/jetmoe
-#         title: JetMoe
-#       - local: model_doc/jukebox
-#         title: Jukebox
-#       - local: model_doc/led
-#         title: LED
-#       - local: model_doc/llama
-#         title: LLaMA
-#       - local: model_doc/llama2
-#         title: Llama2
-#       - local: model_doc/llama3
-#         title: Llama3
-#       - local: model_doc/longformer
-#         title: Longformer
-#       - local: model_doc/longt5
-#         title: LongT5
-#       - local: model_doc/luke
-#         title: LUKE
-#       - local: model_doc/m2m_100
-#         title: M2M100
-#       - local: model_doc/madlad-400
-#         title: MADLAD-400
-#       - local: model_doc/mamba
-#         title: Mamba
-#       - local: model_doc/marian
-#         title: MarianMT
-#       - local: model_doc/markuplm
-#         title: MarkupLM
-#       - local: model_doc/mbart
-#         title: MBart and MBart-50
-#       - local: model_doc/mega
-#         title: MEGA
-#       - local: model_doc/megatron-bert
-#         title: MegatronBERT
-#       - local: model_doc/megatron_gpt2
-#         title: MegatronGPT2
-#       - local: model_doc/mistral
-#         title: Mistral
-#       - local: model_doc/mixtral
-#         title: Mixtral
-#       - local: model_doc/mluke
-#         title: mLUKE
-#       - local: model_doc/mobilebert
-#         title: MobileBERT
-#       - local: model_doc/mpnet
-#         title: MPNet
-#       - local: model_doc/mpt
-#         title: MPT
-#       - local: model_doc/mra
-#         title: MRA
-#       - local: model_doc/mt5
-#         title: MT5
-#       - local: model_doc/mvp
-#         title: MVP
-#       - local: model_doc/nezha
-#         title: NEZHA
-#       - local: model_doc/nllb
-#         title: NLLB
-#       - local: model_doc/nllb-moe
-#         title: NLLB-MoE
-#       - local: model_doc/nystromformer
-#         title: Nyströmformer
-#       - local: model_doc/olmo
-#         title: OLMo
-#       - local: model_doc/open-llama
-#         title: Open-Llama
-#       - local: model_doc/opt
-#         title: OPT
-#       - local: model_doc/pegasus
-#         title: Pegasus
-#       - local: model_doc/pegasus_x
-#         title: PEGASUS-X
-#       - local: model_doc/persimmon
-#         title: Persimmon
-#       - local: model_doc/phi
-#         title: Phi
-#       - local: model_doc/phi3
-#         title: Phi-3
-#       - local: model_doc/phobert
-#         title: PhoBERT
-#       - local: model_doc/plbart
-#         title: PLBart
-#       - local: model_doc/prophetnet
-#         title: ProphetNet
-#       - local: model_doc/qdqbert
-#         title: QDQBert
-#       - local: model_doc/qwen2
-#         title: Qwen2
-#       - local: model_doc/qwen2_moe
-#         title: Qwen2MoE
-#       - local: model_doc/rag
-#         title: RAG
-#       - local: model_doc/realm
-#         title: REALM
-#       - local: model_doc/recurrent_gemma
-#         title: RecurrentGemma
-#       - local: model_doc/reformer
-#         title: Reformer
-#       - local: model_doc/rembert
-#         title: RemBERT
-#       - local: model_doc/retribert
-#         title: RetriBERT
-#       - local: model_doc/roberta
-#         title: RoBERTa
-#       - local: model_doc/roberta-prelayernorm
-#         title: RoBERTa-PreLayerNorm
-#       - local: model_doc/roc_bert
-#         title: RoCBert
-#       - local: model_doc/roformer
-#         title: RoFormer
-#       - local: model_doc/rwkv
-#         title: RWKV
-#       - local: model_doc/splinter
-#         title: Splinter
-#       - local: model_doc/squeezebert
-#         title: SqueezeBERT
-#       - local: model_doc/stablelm
-#         title: StableLm
-#       - local: model_doc/starcoder2
-#         title: Starcoder2
-#       - local: model_doc/switch_transformers
-#         title: SwitchTransformers
-#       - local: model_doc/t5
-#         title: T5
-#       - local: model_doc/t5v1.1
-#         title: T5v1.1
-#       - local: model_doc/tapex
-#         title: TAPEX
-#       - local: model_doc/transfo-xl
-#         title: Transformer XL
-#       - local: model_doc/ul2
-#         title: UL2
-#       - local: model_doc/umt5
-#         title: UMT5
-#       - local: model_doc/xmod
-#         title: X-MOD
-#       - local: model_doc/xglm
-#         title: XGLM
-#       - local: model_doc/xlm
-#         title: XLM
-#       - local: model_doc/xlm-prophetnet
-#         title: XLM-ProphetNet
-#       - local: model_doc/xlm-roberta
-#         title: XLM-RoBERTa
-#       - local: model_doc/xlm-roberta-xl
-#         title: XLM-RoBERTa-XL
-#       - local: model_doc/xlm-v
-#         title: XLM-V
-#       - local: model_doc/xlnet
-#         title: XLNet
-#       - local: model_doc/yoso
-#         title: YOSO
-#       title: Text models
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/beit
-#         title: BEiT
-#       - local: model_doc/bit
-#         title: BiT
-#       - local: model_doc/conditional_detr
-#         title: Conditional DETR
-#       - local: model_doc/convnext
-#         title: ConvNeXT
-#       - local: model_doc/convnextv2
-#         title: ConvNeXTV2
-#       - local: model_doc/cvt
-#         title: CVT
-#       - local: model_doc/deformable_detr
-#         title: Deformable DETR
-#       - local: model_doc/deit
-#         title: DeiT
-#       - local: model_doc/depth_anything
-#         title: Depth Anything
-#       - local: model_doc/deta
-#         title: DETA
-#       - local: model_doc/detr
-#         title: DETR
-#       - local: model_doc/dinat
-#         title: DiNAT
-#       - local: model_doc/dinov2
-#         title: DINOV2
-#       - local: model_doc/dit
-#         title: DiT
-#       - local: model_doc/dpt
-#         title: DPT
-#       - local: model_doc/efficientformer
-#         title: EfficientFormer
-#       - local: model_doc/efficientnet
-#         title: EfficientNet
-#       - local: model_doc/focalnet
-#         title: FocalNet
-#       - local: model_doc/glpn
-#         title: GLPN
-#       - local: model_doc/imagegpt
-#         title: ImageGPT
-#       - local: model_doc/levit
-#         title: LeViT
-#       - local: model_doc/mask2former
-#         title: Mask2Former
-#       - local: model_doc/maskformer
-#         title: MaskFormer
-#       - local: model_doc/mobilenet_v1
-#         title: MobileNetV1
-#       - local: model_doc/mobilenet_v2
-#         title: MobileNetV2
-#       - local: model_doc/mobilevit
-#         title: MobileViT
-#       - local: model_doc/mobilevitv2
-#         title: MobileViTV2
-#       - local: model_doc/nat
-#         title: NAT
-#       - local: model_doc/poolformer
-#         title: PoolFormer
-#       - local: model_doc/pvt
-#         title: Pyramid Vision Transformer (PVT)
-#       - local: model_doc/pvt_v2
-#         title: Pyramid Vision Transformer v2 (PVTv2)
-#       - local: model_doc/regnet
-#         title: RegNet
-#       - local: model_doc/resnet
-#         title: ResNet
-#       - local: model_doc/segformer
-#         title: SegFormer
-#       - local: model_doc/seggpt
-#         title: SegGpt
-#       - local: model_doc/superpoint
-#         title: SuperPoint
-#       - local: model_doc/swiftformer
-#         title: SwiftFormer
-#       - local: model_doc/swin
-#         title: Swin Transformer
-#       - local: model_doc/swinv2
-#         title: Swin Transformer V2
-#       - local: model_doc/swin2sr
-#         title: Swin2SR
-#       - local: model_doc/table-transformer
-#         title: Table Transformer
-#       - local: model_doc/upernet
-#         title: UperNet
-#       - local: model_doc/van
-#         title: VAN
-#       - local: model_doc/vit
-#         title: Vision Transformer (ViT)
-#       - local: model_doc/vit_hybrid
-#         title: ViT Hybrid
-#       - local: model_doc/vitdet
-#         title: ViTDet
-#       - local: model_doc/vit_mae
-#         title: ViTMAE
-#       - local: model_doc/vitmatte
-#         title: ViTMatte
-#       - local: model_doc/vit_msn
-#         title: ViTMSN
-#       - local: model_doc/yolos
-#         title: YOLOS
-#       title: Vision models
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/audio-spectrogram-transformer
-#         title: Audio Spectrogram Transformer
-#       - local: model_doc/bark
-#         title: Bark
-#       - local: model_doc/clap
-#         title: CLAP
-#       - local: model_doc/encodec
-#         title: EnCodec
-#       - local: model_doc/hubert
-#         title: Hubert
-#       - local: model_doc/mctct
-#         title: MCTCT
-#       - local: model_doc/mms
-#         title: MMS
-#       - local: model_doc/musicgen
-#         title: MusicGen
-#       - local: model_doc/musicgen_melody
-#         title: MusicGen Melody
-#       - local: model_doc/pop2piano
-#         title: Pop2Piano
-#       - local: model_doc/seamless_m4t
-#         title: Seamless-M4T
-#       - local: model_doc/seamless_m4t_v2
-#         title: SeamlessM4T-v2
-#       - local: model_doc/sew
-#         title: SEW
-#       - local: model_doc/sew-d
-#         title: SEW-D
-#       - local: model_doc/speech_to_text
-#         title: Speech2Text
-#       - local: model_doc/speech_to_text_2
-#         title: Speech2Text2
-#       - local: model_doc/speecht5
-#         title: SpeechT5
-#       - local: model_doc/unispeech
-#         title: UniSpeech
-#       - local: model_doc/unispeech-sat
-#         title: UniSpeech-SAT
-#       - local: model_doc/univnet
-#         title: UnivNet
-#       - local: model_doc/vits
-#         title: VITS
-#       - local: model_doc/wav2vec2
-#         title: Wav2Vec2
-#       - local: model_doc/wav2vec2-bert
-#         title: Wav2Vec2-BERT
-#       - local: model_doc/wav2vec2-conformer
-#         title: Wav2Vec2-Conformer
-#       - local: model_doc/wav2vec2_phoneme
-#         title: Wav2Vec2Phoneme
-#       - local: model_doc/wavlm
-#         title: WavLM
-#       - local: model_doc/whisper
-#         title: Whisper
-#       - local: model_doc/xls_r
-#         title: XLS-R
-#       - local: model_doc/xlsr_wav2vec2
-#         title: XLSR-Wav2Vec2
-#       title: Audio models
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/timesformer
-#         title: TimeSformer
-#       - local: model_doc/videomae
-#         title: VideoMAE
-#       - local: model_doc/vivit
-#         title: ViViT
-#       title: Video models
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/align
-#         title: ALIGN
-#       - local: model_doc/altclip
-#         title: AltCLIP
-#       - local: model_doc/blip
-#         title: BLIP
-#       - local: model_doc/blip-2
-#         title: BLIP-2
-#       - local: model_doc/bridgetower
-#         title: BridgeTower
-#       - local: model_doc/bros
-#         title: BROS
-#       - local: model_doc/chinese_clip
-#         title: Chinese-CLIP
-#       - local: model_doc/clip
-#         title: CLIP
-#       - local: model_doc/clipseg
-#         title: CLIPSeg
-#       - local: model_doc/clvp
-#         title: CLVP
-#       - local: model_doc/data2vec
-#         title: Data2Vec
-#       - local: model_doc/deplot
-#         title: DePlot
-#       - local: model_doc/donut
-#         title: Donut
-#       - local: model_doc/flava
-#         title: FLAVA
-#       - local: model_doc/git
-#         title: GIT
-#       - local: model_doc/grounding-dino
-#         title: Grounding DINO
-#       - local: model_doc/groupvit
-#         title: GroupViT
-#       - local: model_doc/idefics
-#         title: IDEFICS
-#       - local: model_doc/idefics2
-#         title: Idefics2
-#       - local: model_doc/instructblip
-#         title: InstructBLIP
-#       - local: model_doc/kosmos-2
-#         title: KOSMOS-2
-#       - local: model_doc/layoutlm
-#         title: LayoutLM
-#       - local: model_doc/layoutlmv2
-#         title: LayoutLMV2
-#       - local: model_doc/layoutlmv3
-#         title: LayoutLMV3
-#       - local: model_doc/layoutxlm
-#         title: LayoutXLM
-#       - local: model_doc/lilt
-#         title: LiLT
-#       - local: model_doc/llava
-#         title: Llava
-#       - local: model_doc/llava_next
-#         title: LLaVA-NeXT
-#       - local: model_doc/lxmert
-#         title: LXMERT
-#       - local: model_doc/matcha
-#         title: MatCha
-#       - local: model_doc/mgp-str
-#         title: MGP-STR
-#       - local: model_doc/nougat
-#         title: Nougat
-#       - local: model_doc/oneformer
-#         title: OneFormer
-#       - local: model_doc/owlvit
-#         title: OWL-ViT
-#       - local: model_doc/owlv2
-#         title: OWLv2
-#       - local: model_doc/paligemma
-#         title: PaliGemma
-#       - local: model_doc/perceiver
-#         title: Perceiver
-#       - local: model_doc/pix2struct
-#         title: Pix2Struct
-#       - local: model_doc/sam
-#         title: Segment Anything
-#       - local: model_doc/siglip
-#         title: SigLIP
-#       - local: model_doc/speech-encoder-decoder
-#         title: Speech Encoder Decoder Models
-#       - local: model_doc/tapas
-#         title: TAPAS
-#       - local: model_doc/trocr
-#         title: TrOCR
-#       - local: model_doc/tvlt
-#         title: TVLT
-#       - local: model_doc/tvp
-#         title: TVP
-#       - local: model_doc/udop
-#         title: UDOP
-#       - local: model_doc/video_llava
-#         title: VideoLlava
-#       - local: model_doc/vilt
-#         title: ViLT
-#       - local: model_doc/vipllava
-#         title: VipLlava
-#       - local: model_doc/vision-encoder-decoder
-#         title: Vision Encoder Decoder Models
-#       - local: model_doc/vision-text-dual-encoder
-#         title: Vision Text Dual Encoder
-#       - local: model_doc/visual_bert
-#         title: VisualBERT
-#       - local: model_doc/xclip
-#         title: X-CLIP
-#       title: Multimodal models
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/decision_transformer
-#         title: محول القرار
-#       - local: model_doc/trajectory_transformer
-#         title: محول المسار
-#       title: نماذج التعلم التعزيزية
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/autoformer
-#         title: Autoformer
-#       - local: model_doc/informer
-#         title: Informer
-#       - local: model_doc/patchtsmixer
-#         title: PatchTSMixer
-#       - local: model_doc/patchtst
-#         title: PatchTST
-#       - local: model_doc/time_series_transformer
-#         title: محول السلاسل الزمنية
-#       title: نماذج السلاسل الزمنية
-#     - isExpanded: false
-#       sections:
-#       - local: model_doc/graphormer
-#         title: Graphormer
-#       title: نماذج الرسم البياني
-#     title: النماذج
-#   - sections:
-#     - local: internal/modeling_utils
-#       title: الطبقات المخصصة والمرافق
-#     - local: internal/pipelines_utils
-#       title: مرافق خطوط الأنابيب
-#     - local: internal/tokenization_utils
-#       title: مرافق مقسم النصوص
-#     - local: internal/trainer_utils
-#       title: مرافق المدرب
-#     - local: internal/generation_utils
-#       title: مرافق التوليد
-#     - local: internal/image_processing_utils
-#       title: مرافق معالجة الصور
-#     - local: internal/audio_utils
-#       title: مرافق معالجة الصوت
-#     - local: internal/file_utils
-#       title: مرافق عامة
-#     - local: internal/time_series_utils
-#       title: مرافق السلاسل الزمنية
-#     title: مساعدون داخليون
-#   title: API

docs/source/ar/accelerate.md

@@ -1,120 +0,0 @@
-# التدريب الموزع باستخدام  🤗 Accelerate
-
-
-مع تزايد حجم النماذج اللغوية، برز التوازي كأحد الاستراتيجيات لتدريب نماذج أكبر على أجهزة محدودة وتسريع عملية التدريب بمقدار كبير.  أنشأنا في Hugging Face، قمنا بإنشاء مكتبة [ Accelerate](https://huggingface.co/docs/accelerate) لمساعدة المستخدمين على تدريب أي نموذج من  Transformers بسهولة على أي نوع من الإعدادات الموزعة، سواء كان ذلك على عدة وحدات معالجة رسومات (GPUs) على جهاز واحد أو على عدة وحدات معالجة رسومات موزعة على عدة أجهزة. في هذا الدليل، تعلم كيفية تخصيص حلقة تدريب PyTorch الأصلية لتمكين التدريب في بيئة موزعة.
-
-## الإعداد
-
-ابدأ بتثبيت 🤗 Accelerate:
-
-```bash
-pip install accelerate
-```
-
-ثم قم باستيراد وإنشاء كائن [`~accelerate.Accelerator`]. سيقوم [`~accelerate.Accelerator`] تلقائيًا باكتشاف نوع الإعداد الموزع الخاص بك وتهيئة جميع المكونات اللازمة للتدريب. لن تحتاج إلى وضع نموذجك على جهاز بشكل معين.
-
-```py
->>> from accelerate import Accelerator
-
->>> accelerator = Accelerator()
-```
-
-## الاستعداد للتسريع
-
-الخطوة التالية هي تمرير جميع كائنات التدريب ذات الصلة إلى دالة الإعداد [`~accelerate.Accelerator.prepare`]. ويشمل ذلك DataLoaders للتدريب والتقييم، ونموذجًا ومُحَسِّنً المعاملات (optimizer):
-
-```py
->>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-...     train_dataloader, eval_dataloader, model, optimizer
-... )
-```
-
-## الخلفي Backward
-
-الإضافة الأخيرة هي استبدال الدالة المعتادة `loss.backward()` في حلقة التدريب الخاصة بك بدالة [`~accelerate.Accelerator.backward`] في 🤗 Accelerate:
-
-```py
->>> for epoch in range(num_epochs):
-...     for batch in train_dataloader:
-...         outputs = model(**batch)
-...         loss = outputs.loss
-...         accelerator.backward(loss)
-
-...         optimizer.step()
-...         lr_scheduler.step()
-...         optimizer.zero_grad()
-...         progress_bar.update(1)
-```
-
-كما يمكنك أن ترى في الكود التالي، فأنت بحاجة فقط إلى إضافة أربعة أسطر من الكود إلى حلقة التدريب الخاصة بك لتمكين التدريب الموزع!
-
-```diff
-+ from accelerate import Accelerator
-  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
-
-+ accelerator = Accelerator()
-
-  model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
-  optimizer = AdamW(model.parameters(), lr=3e-5)
-
-- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-- model.to(device)
-
-+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-+     train_dataloader, eval_dataloader, model, optimizer
-+ )
-
-  num_epochs = 3
-  num_training_steps = num_epochs * len(train_dataloader)
-  lr_scheduler = get_scheduler(
-      "linear",
-      optimizer=optimizer,
-      num_warmup_steps=0,
-      num_training_steps=num_training_steps
-  )
-
-  progress_bar = tqdm(range(num_training_steps))
-
-  model.train()
-  for epoch in range(num_epochs):
-      for batch in train_dataloader:
--         batch = {k: v.to(device) for k, v in batch.items()}
-          outputs = model(**batch)
-          loss = outputs.loss
--         loss.backward()
-+         accelerator.backward(loss)
-optimizer.step()
-          lr_scheduler.step()
-          optimizer.zero_grad()
-          progress_bar.update(1)
-```
-
-## تدريب
-
-بمجرد إضافة أسطر الكود ذات الصلة، قم بتشغيل التدريب الخاص بك في أحد النصوص أو الدفاتر مثل Colaboratory.
-
-### التدريب باستخدام نص برمجي
-
-إذا كنت تشغل التدريب الخاص بك من نص برمجي، فقم بتشغيل الأمر التالي لإنشاء وحفظ ملف تكوين:
-
-```bash
-accelerate config
-```
-
-ثم قم بتشغيل التدريب الخاص بك باستخدام:
-
-```bash
-accelerate launch train.py
-```
-
-### التدريب باستخدام دفتر ملاحظات
-
-يمكن أيضًا تشغيل 🤗 Accelerate في دفاتر إذا كنت تخطط لاستخدام وحدات معالجة الرسوميات (TPUs) في Colaboratory. قم بتغليف كل الكود المسؤول عن التدريب في دالة، ومررها إلى [`~accelerate.notebook_launcher`]:
-
-```py
->>> from accelerate import notebook_launcher
-
->>> notebook_launcher(training_function)
-```
-
-للحصول على مزيد من المعلومات حول 🤗 Accelerate وميزاته الغنية، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/accelerate).

docs/source/ar/attention.md

@@ -1,25 +0,0 @@
-# آليات الانتباه 
-
-تستخدم معظم نماذج المحول (Transformer) الانتباه الكامل بحيث تكون مصفوفة الانتباه ذات الأبعاد المتساوية. ويمكن أن يمثل ذلك عقبة حسابية كبيرة عندما تكون لديك نصوص طويلة. ويعد Longformer وReformer من النماذج التي تحاول أن تكون أكثر كفاءة وتستخدم نسخة مخففة من مصفوفة الانتباه لتسريع التدريب.
-
-## انتباه LSH
-
-يستخدم [Reformer](model_doc/reformer) انتباه LSH. في الدالة softmax(QK^t)، فإن أكبر العناصر فقط (في بعد softmax) من المصفوفة QK^t هي التي ستعطي مساهمات مفيدة. لذلك، بالنسبة لكل استعلام q في Q، يمكننا أن نأخذ في الاعتبار فقط المفاتيح k في K المشابهة لـ q فقط. وتُستخدم دالة هاش لتحديد ما إذا كان q وk متشابهين. ويتم تعديل قناع الانتباه لتجاهل الرمز الحالي (باستثناء الموضع الأول)، لأنه سيعطي استعلامًا ومفتاحًا متساويين (لذلك متشابهين للغاية). نظرًا لطبيعة دالة الهاش العشوائية نوعًا ما، يتم في الممارسة العملية استخدام عدة دوال هاش (يحددها معامل n_rounds) ثم يتم حساب المتوسط معًا.
-
-## الانتباه المحلي
-
-يستخدم [Longformer](model_doc/longformer) الانتباه المحلي: غالبًا ما يكون السياق المحلي (على سبيل المثال، ما هما الرمزان إلى اليسار واليمين؟) كافيًا لاتخاذ إجراء بالنسبة للرمز المعطى. أيضًا، عن طريق تكديس طبقات الانتباه التي لها نافذة صغيرة، سيكون للطبقة الأخيرة مجال استقبال أكبر من مجرد الرموز في النافذة، مما يسمح لها ببناء تمثيل للجملة بأكملها.
-
-كما يتم منح بعض رموز الإدخال المختارة مسبقًا انتباهًا عالميًا: بالنسبة لهذه الرموز القليلة، يمكن لمصفوفة الانتباه الوصول إلى جميع الرموز وتكون هذه العملية متماثلة: فلجميع الرموز الأخرى إمكانية الوصول إلى تلك الرموز المحددة (بالإضافة إلى تلك الموجودة في نافذتهم المحلية). وهذا موضح في الشكل 2d من الورقة، انظر أدناه لمثال على قناع الانتباه:
-
-<div class="flex justify-center">
-    <img scale="50 %" align="center" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/local_attention_mask.png"/>
-</div>
-
-وباستخدام مصفوفات الانتباه هذه التي تحتوي على عدد أقل من المعلمات، يسمح النموذج بمدخالات ذات طول تسلسل أكبر.
-
-## حيل أخرى
-
-### الترميزات الموضعية المحورية
-
-يستخدم [Reformer](model_doc/reformer) ترميزات موضعية محورية: في نماذج المحول التقليدية، يكون الترميز الموضعي E مصفوفة بحجم \\(l\\) في \\(d\\)، حيث \\(l\\) هو طول التسلسل و\\(d\\) هو بعد الحالة المخفية. إذا كان لديك نصوص طويلة جدًا، فقد تكون هذه المصفوفة ضخمة وتستهلك مساحة كبيرة جدًا على وحدة معالجة الرسوميات (GPU). وللتخفيف من ذلك، تتكون الترميزات الموضعية المحورية من تحليل تلك المصفوفة الكبيرة E إلى مصفوفتين أصغر E1 وE2، بأبعاد \\(l_{1} \times d_{1}\\) و \\(l_{2} \times d_{2}\\)، بحيث \\(l_{1} \times l_{2} = l\\) و\\(d_{1} + d_{2} = d\\) (مع حاصل ضرب الأطوال، ينتهي الأمر بكونه أصغر بكثير). ويتم الحصول على الترميز للخطوة الزمنية \\(j\\) في E عن طريق ربط الترميزات للخطوة الزمنية \\(j \% l1\\) في E1 و \\(j // l1\\) في E2.

docs/source/ar/autoclass_tutorial.md

@@ -1,167 +0,0 @@
-# تحميل نماذج مدربة مسبقًا باستخدام AutoClass
-لم ترغب في إنشاء محول معماري لمؤشر الترابط الخاص بك، فهناك العديد من محولات المعمارية المختلفة التي يمكنك الاختيار من بينها. كجزء من الفلسفة الأساسية لـ 🤗 Transformers لجعل المكتبة سهلة وبسيطة ومرنة، فإن فئة `AutoClass` تستدل تلقائيًا وتحمّل البنية الصحيحة من نسخة نموذج (Model Checkpoint) معينة. تسمح لك طريقة `from_pretrained()` بتحميل نموذج مُدرب مسبقًا لأي بنية بسرعة حتى لا تضطر إلى تكريس الوقت والموارد لتدريب نموذج من الصفر. إن إنتاج هذا النوع من التعليمات البرمجية غير المعتمدة على نسخ يعني أنه إذا نجح رمزك مع ننسخة واحدة، فسيتم تشغيله مع أخرى - طالما تم تدريبه لمهمة مماثلة - حتى إذا كانت البنية المعمارية مختلفة.
-
-تذكر أن البنية تشير إلى هيكل النموذج، والنسخ هي الأوزان لبنية معمارية معينة. على سبيل المثال، [BERT](https://huggingface.co/google-bert/bert-base-uncased) هي بنية معمارية، في حين أن `google-bert/bert-base-uncased` هي نسخة. "النموذج" هو مصطلح عام يمكن أن يعني إما البنية أو نالنسخة.
-
-في هذا البرنامج التعليمي، ستتعلم كيفية:
-
-* تحميل مُجزّئ الرموز مُدرب مسبقًا
-* تحميل معالج صور مُدرب مسبقًا
-* تحميل مستخرج ميزات مُدرب مسبقًا
-* تحميل معالج مُدرب مسبقًا
-* تحميل نموذج مُدرب مسبقًا
-* تحميل نموذج كعمود فقري
-
-## AutoTokenizer
-
-تبدأ كل مهمة NLP تقريبًا بمُجزّئ للرموز. يقوم المُجزّئ بتحويل النص إلى شكل يمكن للنموذج معالجته.
-
-قم بتحميل المُجزّئ باستخدام [`AutoTokenizer.from_pretrained`]:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
-```
-
-ثم قم بتحليل إدخالك على النحو الموضح أدناه:
-
-```py
->>> sequence = "In a hole in the ground there lived a hobbit."
->>> print(tokenizer(sequence))
-{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], 
- 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
- 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-```
-
-## معالج الصور التلقائي (AutoImageProcessor)
- 
-
-بالنسبة لمهمات الرؤية، يقوم معالج الصور بمعالجة الصورة إلى تنسيق الإدخال الصحيح.
-
-```py
->>> from transformers import AutoImageProcessor
-
->>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
-```
-
-## AutoBackbone
-
-<div style="text-align: center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Swin%20Stages.png">
-    <figcaption class="mt-2 text-center text-sm text-gray-500">الصورة توضح مخطط مراحل نموذج Swin.</figcaption>
-</div>
-
-يسمح لك [`AutoBackbone`] باستخدام النماذج المُدربة مسبقًا كعمود فقري للحصول على خرائط ميزات من مراحل مختلفة من العمود الفقري. يجب عليك تحديد أحد المعلمات التالية في [`~PretrainedConfig.from_pretrained`]:
-
-* `out_indices` هو فهرس الطبقة التي تريد الحصول على خريطة الميزات منها
-* `out_features` هو اسم الطبقة التي تريد الحصول على خريطة الميزات منها
-
-يمكن استخدام هذه المعلمات بشكل متبادل، ولكن إذا كنت تستخدم كلاً منها، فتأكد من أنها متوائمة مع بعضها البعض! إذا لم تمرر أيًا من هذه المعلمات، فسيقوم العمود الفقري بإرجاع خريطة الميزات من الطبقة الأخيرة.
-<div style="text-align: center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Swin%20Stage%201.png">
-    <figcaption class="mt-2 text-center text-sm text-gray-500">صورة توضح خريطة ميزات من المرحلة الأولى للعمود الفقري.</figcaption>
-</div>
-
-على سبيل المثال، في الرسم التخطيطي أعلاه، لإرجاع خريطة الميزات من المرحلة الأولى من العمود الفقري Swin، يمكنك تعيين `out_indices=(1,)`:
-
-```py
->>> from transformers import AutoImageProcessor, AutoBackbone
->>> import torch
->>> from PIL import Image
->>> import requests
->>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
->>> image = Image.open(requests.get(url, stream=True).raw)
->>> processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
->>> model = AutoBackbone.from_pretrained("microsoft/swin-tiny-patch4-window7-224", out_indices=(1,))
-
->>> inputs = processor(image, return_tensors="pt")
->>> outputs = model(**inputs)
->>> feature_maps = outputs.feature_maps
-```
-
-الآن يمكنك الوصول إلى كائن `feature_maps` من المرحلة الأولى من العمود الفقري:
-
-```py
->>> list(feature_maps[0].shape)
-[1, 96, 56, 56]
-```
-
-## مستخرج الميزات التلقائي (AutoFeatureExtractor)
-
-بالنسبة للمهام الصوتية، يقوم مستخرج الميزات بمعالجة إشارة الصوت إلى تنسيق الإدخال الصحيح.
-
-قم بتحميل مستخرج ميزات باستخدام [`AutoFeatureExtractor.from_pretrained`]:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained(
-...     "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
-... )
-```
-
-## المعالج التلقائي (AutoProcessor)
-
-تتطلب المهام متعددة الوسائط معالجًا يجمع بين نوعين من أدوات المعالجة المسبقة. على سبيل المثال، يتطلب نموذج [LayoutLMV2](model_doc/layoutlmv2) معالج صور لمعالجة الصور ومُجزّئ لمعالجة النص؛ يجمع المعالج كليهما.
-
-قم بتحميل معالج باستخدام [`AutoProcessor.from_pretrained`]:
-
-```py
->>> from transformers import AutoProcessor
-
->>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
-```
-
-## النموذج التلقائي (AutoModel)
-
-<frameworkcontent>
-<pt>
-تسمح لك فئات `AutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`AutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-أعد استخدام نفس نقطة التفتيش لتحميل بنية لمهمة مختلفة:
-
-```py
->>> from transformers import AutoModelForTokenClassification
-
->>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-<Tip warning={true}>
-
-بالنسبة لنماذج PyTorch، تستخدم طريقة `from_pretrained()` `torch.load()` التي تستخدم داخليًا `pickle` والتي يُعرف أنها غير آمنة. بشكل عام، لا تقم مطلقًا بتحميل نموذج قد يكون مصدره مصدرًا غير موثوق به، أو قد يكون تم العبث به. يتم تخفيف هذا الخطر الأمني جزئيًا للنماذج العامة المستضافة على Hub Hugging Face، والتي يتم [فحصها بحثًا عن البرامج الضارة](https://huggingface.co/docs/hub/security-malware) في كل ارتكاب. راجع [توثيق Hub](https://huggingface.co/docs/hub/security) للحصول على أفضل الممارسات مثل [التحقق من التوقيع](https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg) باستخدام GPG.
-
-لا تتأثر نقاط تفتيش TensorFlow و Flax، ويمكن تحميلها داخل بنيات PyTorch باستخدام `from_tf` و `from_flax` kwargs لطريقة `from_pretrained` للتحايل على هذه المشكلة.
-
-</Tip>
-
-
-بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `AutoModelFor` لتحميل مثيلات مُدربة مسبقًا من النماذج. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، تعرف على كيفية استخدام المحلل اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
-</pt>
-
-<tf>
-أخيرًا، تسمح لك فئات `TFAutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`TFAutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-أعد استخدام نفس نقطة التفتيش لتحميل بنية لمهمة مختلفة:
-
-```py
->>> from transformers import TFAutoModelForTokenClassification
-
->>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `TFAutoModelFor` لتحميل نسخ لنماذج مُدربة مسبقًا. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، ستتعرف على كيفية استخدام المُجزّئ اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
-</tf>
-</frameworkcontent>

docs/source/ar/bertology.md

@@ -1,18 +0,0 @@
-# BERTology
-
-يُشهد في الآونة الأخيرة نمو مجال دراسي يُعنى باستكشاف آلية عمل نماذج المحولات الضخمة مثل BERT (والذي يُطلق عليها البعض اسم "BERTology"). ومن الأمثلة البارزة على هذا المجال ما يلي:
-
-- BERT Rediscovers the Classical NLP Pipeline بواسطة Ian Tenney و Dipanjan Das و Ellie Pavlick:
-  https://huggingface.co/papers/1905.05950
-- Are Sixteen Heads Really Better than One? بواسطة Paul Michel و Omer Levy و Graham Neubig: https://huggingface.co/papers/1905.10650
-- What Does BERT Look At? An Analysis of BERT's Attention بواسطة Kevin Clark و Urvashi Khandelwal و Omer Levy و Christopher D.
-  Manning: https://huggingface.co/papers/1906.04341
-- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://huggingface.co/papers/2210.04633
-
-لإثراء هذا المجال الناشئ، قمنا بتضمين بعض الميزات الإضافية في نماذج BERT/GPT/GPT-2 للسماح للناس بالوصول إلى التمثيلات الداخلية، والتي تم تكييفها بشكل أساسي من العمل الرائد لـ Paul Michel (https://huggingface.co/papers/1905.10650):
-
-- الوصول إلى جميع الحالات المخفية في BERT/GPT/GPT-2،
-- الوصول إلى جميع أوزان الانتباه لكل رأس في BERT/GPT/GPT-2،
-- استرجاع قيم ومشتقات  مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://huggingface.co/papers/1905.10650.
-
-ولمساعدتك على فهم واستخدام هذه الميزات بسهولة، أضفنا مثالًا برمجيًا محددًا: [bertology.py](https://github.com/huggingface/transformers-research-projects/tree/main/bertology/run_bertology.py) أثناء استخراج المعلومات  وتقليص من نموذج تم تدريبه مسبقًا على GLUE.

docs/source/ar/chat_templating.md

@@ -1,835 +0,0 @@
-# قوالب نماذج الدردشة
-
-## مقدمة
-
-تعد **الدردشة** أحد استخدامات نماذج اللغات الكبيرة (LLMs) شائعة الاستخدام بشكل متزايد. ففي سياق الدردشة، وبدلاً من متابعة سلسلة نصية واحدة (كما هو الحال مع نماذج اللغات القياسية)، يواصل النموذج بدلاً من ذلك محادثة تتكون من رسالة واحدة أو أكثر، تتضمن كل منها دورًا، مثل "المستخدم" أو "المساعد"، بالإضافة إلى نص الرسالة.
-
-وكما هو الحال مع تقسيم النص إلى رموز (tokenization)، تتوقع النماذج المختلفة تنسيقات إدخال مختلفة تمامًا للمحادثة. لهذا السبب أضفنا **قوالب الدردشة** كميزة جديدة. تُعد قوالب المحادثة جزءًا من tokenizer. تحدد هذه القوالب كيفية تحويل المحادثات، والتي يتم تمثيلها كقوائم من الرسائل، إلى سلسلة نصية واحدة قابلة للتقسيم إلى رموز بالتنسيق الذي يتوقعه النموذج.
-
-دعونا نجعل هذا ملموسًا بمثال سريع باستخدام نموذج `BlenderBot`. لدى BlenderBot قالب افتراضي بسيط للغاية، والذي يضيف في الغالب مسافات بيضاء بين جولات الحوار:
-
-```python
->>> from transformers import AutoTokenizer
->>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-
->>> chat = [
-...    {"role": "user", "content": "Hello, how are you?"},
-...    {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-...    {"role": "user", "content": "I'd like to show off how chat templating works!"},
-... ]
-
->>> tokenizer.apply_chat_template(chat, tokenize=False)
-" Hello, how are you?  I'm doing great. How can I help you today?   I'd like to show off how chat templating works!</s>"
-```
-
-لاحظ كيف تم ضغط الدردشة بأكملها في سلسلة واحدة. إذا استخدمنا `tokenize=True`، وهو الإعداد الافتراضي، فسيتم أيضًا تحليل السلسلة نحويًا نيابة عنا. ولكن، لنشاهد قالبًا أكثر تعقيدًا في العمل، دعونا نستخدم نموذج `mistralai/Mistral-7B-Instruct-v0.1`.
-
-```python
->>> from transformers import AutoTokenizer
->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
-
->>> chat = [
-...   {"role": "user", "content": "Hello, how are you?"},
-...   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-...   {"role": "user", "content": "I'd like to show off how chat templating works!"},
-... ]
-
->>> tokenizer.apply_chat_template(chat, tokenize=False)
-"<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]</s>"
-```
-
-لاحظ كيف أضاف المجزىء اللغوى tokenizer رموز التحكم `[INST]` و `[/INST]` للإشارة إلى بداية ونهاية رسائل المستخدم (ولكن ليس رسائل المساعد!) ، وتم تكثيف المحادثة بأكملها في سلسلة نصية واحدة. إذا استخدمنا `tokenize=True` ، وهو الإعداد الافتراضي ، فسيتم أيضًا تقسيم تلك السلسلة إلى رموز.
-
-حاول الآن استخدام نفس الشفرة، لكن مع استبدال النموذج بـ `HuggingFaceH4/zephyr-7b-beta` ، وستحصل على:
-```text
-<|user|>
-Hello, how are you?</s>
-<|assistant|>
-I'm doing great. How can I help you today?</s>
-<|user|>
-I'd like to show off how chat templating works!</s>
-```
-تم ضبط كل من Zephyr و Mistral-Instruct من نفس النموذج الأصلي ، Mistral-7B-v0.1. ومع ذلك ، فقد تم تدريبهم بتنسيقات دردشة مختلفة تمامًا. بدون قوالب المحادثة، ستضطر إلى كتابة شفرة تنسيق يدويًا لكل نموذج ، ومن السهل جدًا ارتكاب أخطاء بسيطة تؤثر على الأداء! تُدير قوالب المحادثة تفاصيل التنسيق نيابةً عنك ، مما يُتيح لك كتابة شفرة عامة تعمل مع أي نموذج.
-
-## كيف أستخدم قوالب الدردشة؟
-
-كما رأيت في المثال السابق،  من السهل استخدام قوالب الدردشة. قم ببساطة بإنشاء قائمة من الرسائل، مع مفتاحي `role` و`content`، ثم قم بتمريرها إلى [`~PreTrainedTokenizer.apply_chat_template`] . بمجرد قيامك بذلك، ستحصل على مخرجات جاهزة للاستخدام! عند استخدام قوالب الدردشة كإدخال لتوليد نصوص بواسطة النموذج، فمن الجيد أيضًا استخدام `add_generation_prompt=True` لإضافة [مطالبات توليد النصوص](#what-are-generation-prompts).
-
-فيما يلي مثال على إعداد الإدخال لـ `model.generate()`، باستخدام Zephyr مرة أخرى:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-checkpoint = "HuggingFaceH4/zephyr-7b-beta"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint) # قد ترغب في استخدام bfloat16 و/أو الانتقال إلى GPU هنا
-
-messages = [
-    {
-        "role": "system",
-        "content": "You are a friendly chatbot who always responds in the style of a pirate",
-    },
-    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
- ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-print(tokenizer.decode(tokenized_chat[0]))
-```
-سيؤدي هذا إلى إنتاج سلسلة نصية بتنسيق الإدخال الذي يتوقعه Zephyr.
-
-```text
-<|system|>
-You are a friendly chatbot who always responds in the style of a pirate</s>
-<|user|>
-How many helicopters can a human eat in one sitting?</s>
-<|assistant|>
-```
-
-الآن بعد أن تم تنسيق الإدخال بشكل صحيح لـ Zephyr، يمكننا استخدام النموذج لإنشاء رد على سؤال المستخدم:
-
-```python
-outputs = model.generate(tokenized_chat, max_new_tokens=128)
-print(tokenizer.decode(outputs[0]))
-```
-
-سيؤدي هذا إلى ما يلي:
-
-```text
-<|system|>
-You are a friendly chatbot who always responds in the style of a pirate</s>
-<|user|>
-How many helicopters can a human eat in one sitting?</s>
-<|assistant|>
-Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.
-```
-
-كان ذلك سهلاً بعد كل شيء !
-
-
-
-## هل هناك قنوات معالجة أوتوماتيكية للدردشة؟
-
-نعم يوجد ! تدعم قنوات المعالجة توليد النصوص مدخلات الدردشة ، مما يُسهّل استخدام نماذج الدردشة . في الماضي ، كنا نستخدم فئة "ConversationalPipeline" المُخصّصة ، ولكن تم الآن إيقافها وتم دمج وظائفها في [`TextGenerationPipeline`]. دعونا نجرّب مثال Zephyr مرة أخرى ، ولكن هذه المرة باستخدام قناة معالجة:
-
-```python
-from transformers import pipeline
-
-pipe = pipeline("text-generation", "HuggingFaceH4/zephyr-7b-beta")
-messages = [
-    {
-        "role": "system",
-        "content": "You are a friendly chatbot who always responds in the style of a pirate",
-    },
-    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
-]
-print(pipe(messages, max_new_tokens=128)[0]['generated_text'][-1]) # طباعة استجابة المساعد
-```
-
-```النص
-{'role': 'assistant', 'content': "Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all."}
-```
-
-سيُراعي قناة المعالجة جميع تفاصيل تقسيم النص إلى رموز واستدعاء apply_chat_template نيابةً عنك - بمجرد أن يصبح لِدى النموذج قالب دردشة ، فكل ما تحتاج إلى القيام به هو تهيئة قناة معالجة وتمرير قائمة الرسائل إليها!
-
-## ما هي "مطالبات التوليد"؟
-
-قد تلاحظ أن طريقة `apply_chat_template` لها معامل `add_generation_prompt`. تخبر هذه المعامل القالب بإضافة رموز تشير إلى بداية رد البوت. على سبيل المثال، ضع في اعتبارك الدردشة التالية:
-
-```python
-messages = [
-    {"role": "user", "content": "Hi there!"},
-    {"role": "assistant", "content": "Nice to meet you!"},
-    {"role": "user", "content": "Can I ask a question?"}
-]
-```
-
-إليك كيف سيبدو ذلك بدون موجه توليد نصوص ، بالنسبة لنموذج يستخدم تنسيق "ChatML" القياسي :
-
-```python
-tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-"""
-```
-
-وهكذا يبدو الأمر **مع** مطالبة التوليد:
-
-```python
-tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-"""
-```
-
-لاحظ أننا أضفنا هذه المرة الرموز التي تشير إلى بداية رد البوت. يضمن هذا أنه عندما يُولّد النموذج نصًا فسيكتب رد البوت بدلاً من القيام بشيء غير متوقع، مثل الاستمرار في رسالة المستخدم. تذكر، أن نماذج الدردشة لا تزال مجرد نماذج للغة - فهي مدربة على متابعة النصوص، والدردشة هي مجرد نوع خاص من النصوص بالنسبة لها! يجب توجيهها برموز تحكم مناسبة، حتى تعرف ما الذي يجب عليها فعله.
-
-لا تتطلب جميع النماذج الرموز التحكمية لتوليد نصوص . بعض النماذج ، مثل LLaMA ، ليس لديها أي رموز خاصة قبل ردود البوت . في هذه الحالات ، لن يكون لمعامل `add_generation_prompt` أي تأثير. يعتمد التأثير الدقيق الذي تُحدثه `add_generation_prompt` على القالب المستخدم .
-
-## ما وظيفة "continue_final_message"؟
-
-عند تمرير قائمة من الرسائل إلى `apply_chat_template` أو `TextGenerationPipeline` ، يمكنك اختيار تنسيق المحادثة بحيث يواصل النموذج الرسالة الأخيرة في المحادثة بدلاً من بدء رسالة جديدة. يتم ذلك عن طريق إزالة أي رموز نهاية التسلسل التي تشير إلى نهاية الرسالة الأخيرة ، بحيث يقوم النموذج ببساطة بتمديد الرسالة الأخيرة عندما يبدأ في توليد النص . يُعد هذا أمرًا مفيدًا "لِمَلء بداية" رد النموذج مُسبقًا.
-
-وهنا مثال:
-```python
-chat = [
-    {"role": "user", "content": "Can you format the answer in JSON?"},
-    {"role": "assistant", "content": '{"name": "'},
-]
-
-formatted_chat = tokenizer.apply_chat_template(chat, tokenize=True, return_dict=True, continue_final_message=True)
-model.generate(**formatted_chat)
-```
-سيقوم النموذج بتوليد نص يكمل سلسلة JSON ، بدلاً من بدء رسالة جديدة . يمكن أن يكون هذا النهج مفيدًا جدًا لتحسين دقة اتباع النموذج للإرشادات عندما تعرف كيف تريد أن يبدأ ردوده .
-.
-
-نظرًا لأن `add_generation_prompt` تضيف الرموز التي تبدأ رسالة جديدة ، و `continue_final_message` تزيل أي رموز نهاية الرسالة من الرسالة الأخيرة ، فليس من المنطقي استخدامهما معًا . ونتيجة لذلك ، ستتلقّى خطأً إذا حاولت ذلك !
-
-السلوك الافتراضي لِـ `TextGenerationPipeline` هو تعيين `add_generation_prompt=True` بحيث تبدأ رسالة جديدة . ومع ذلك ، إذا كانت الرسالة الأخيرة في المحادثة التي تم إدخالها لديها دور "assistant" ، فسوف تفترض أن هذه الرسالة هي "مَلء بداية" وتتحوّل إلى `continue_final_message=True` بدلاً من ذلك ، لأن مُعظم النماذج لا تدعم عدة رسائل متتالية للمساعد . يمكنك تجاوز هذا السلوك عن طريق تمرير معامل `continue_final_message` بشكل صريح عند استدعاء قناة المعالجة .
-
-
-
-## هل يمكنني استخدام قوالب الدردشة في التدريب؟
-
-نعم ! تُعد هذه طريقة جيدة للتأكد من أن قالب الدردشة يتطابق مع الرموز التي يراها النموذج أثناء التدريب . نوصي بتطبيق قالب الدردشة كخطوة معالجة أولية لمجموعة بياناتك . بعد ذلك ، يمكنك ببساطة متابعة عملية التدريب كما هو الحال مع أي مهمة تدريب نماذج لغات أخرى . عند التدريب ، يجب أن تُعيّن عادةً  `add_generation_prompt=False` ، لأنه لن تكون الرموز المُضافة لتحفيز رد المساعد مفيدة أثناء التدريب . دعونا نرى مثالاً :
-
-```python
-from transformers import AutoTokenizer
-from datasets import Dataset
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-
-chat1 = [
-    {"role": "user", "content": "Which is bigger, the moon or the sun?"},
-    {"role": "assistant", "content": "The sun."}
-]
-chat2 = [
-    {"role": "user", "content": "Which is bigger, a virus or a bacterium?"},
-    {"role": "assistant", "content": "A bacterium."}
-]
-
-dataset = Dataset.from_dict({"chat": [chat1, chat2]})
-dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)})
-print(dataset['formatted_chat'][0])
-```
-ونحصل على:
-
-```text
-<|user|>
-Which is bigger, the moon or the sun?</s>
-<|assistant|>
-The sun.</s>
-```
-
-من هنا، استمر في التدريب كما تفعل مع مهمة نمذجة اللغة القياسية، باستخدام عمود `formatted_chat`.
-
-<Tip>
-بشكل افتراضي ، تضيف بعض *tokenizers* رموزًا خاصة مثل `<bos>` و `<eos>` إلى النص الذي تقوم بتقسيمه إلى رموز. يجب أن تتضمن قوالب المحادثة بالفعل جميع الرموز الخاصة التي تحتاجها ، وبالتالي فإن الرموز الخاصة الإضافية ستكون غالبًا غير صحيحة أو مُكررة ، مما سيؤثر سلبًا على أداء النموذج .
-
-لذلك ، إذا قمت بتنسيق النص باستخدام  `apply_chat_template(tokenize=False)` ، فيجب تعيين المعامل `add_special_tokens=False` عندما تقوم بتقسيم ذلك النص إلى رموز لاحقًا . إذا كنت تستخدم `apply_chat_template(tokenize=True)` ، فلن تحتاج إلى القلق بشأن ذلك !
-</Tip>
-
-## متقدّم: مدخلات إضافية لِقوالب الدردشة
-
-
-المعامل الوحيدة التي تتطلبها طريقة `apply_chat_template` هي `messages`. ومع ذلك، يمكنك تمرير أي معامل ككلمة مفتاحية إلى `apply_chat_template` وستكون متاحة داخل القالب. يمنحك هذا الكثير من المرونة لاستخدام قوالب الدردشة للعديد من الأشياء. لا توجد قيود على أسماء هذه المعامﻻت أو تنسيقاتها - يمكنك تمرير سلاسل نصية أو قوائم أو قواميس أو أي شيء آخر تريده.
-
-ومع ذلك، هناك بعض الحالات الشائعة لاستخدام هذه المعامﻻت الإضافية، مثل تمرير أدوات لاستدعاء الوظائف، أو المستندات  لإنشاء النصوص المُعزّزة بالاسترجاع. في هذه الحالات الشائعة، لدينا بعض التوصيات المُحدّدة حول أسماء هذه المعامﻻت وتنسيقاتها، والتي يتم وصفها في الأقسام التالية. نشجع مطوّري النماذج على جعل قوالب الدردشة الخاصة بهم متوافقة مع هذا التنسيق، لتسهيل نقل التعليمات البرمجية لاستدعاء الأدوات بين النماذج.
-
-## متقدم: استخدام الأداة / استدعاء الدالة
-
-يمكن لنماذج "استخدام الأداة" اختيار استدعاء الدوال كأدوات خارجية قبل توليد الإجابة. عند تمرير الأدوات إلى نموذج استخدام الأدوات، يمكنك ببساطة تمرير قائمة من الوظائف إلى معامل `tools`:
-
-```python
-import datetime
-
-def current_time():
-    """Get the current local time as a string."""
-    return str(datetime.now())
-
-def multiply(a: float, b: float):
-    """
-    A function that multiplies two numbers
-    
-    Args:
-        a: The first number to multiply
-        b: The second number to multiply
-    """
-    return a * b
-
-tools = [current_time, multiply]
-
-model_input = tokenizer.apply_chat_template(
-    messages,
-    tools=tools
-)
-```
-
-لكي يعمل هذا بشكل صحيح، يجب عليك كتابة وظائفك بالتنسيق السابق، حتى يمكن تحليلها بشكل صحيح كأدوات. على وجه التحديد، يجب عليك اتباع هذه القواعد:
-
-- يجب أن يكون للدالة اسم وصفي.
-- يجب أن يكون لكل معامل نوع للتلميح.
-- يجب أن تحتوي الدالة على سلسلة مستندية بتنسيق Google القياسي (بمعنى وصف الدالة الأولي متبوعًا بكتلة `Args:` التي تصف المعاﻻت، ما لم تكن الدالة لا تحتوي على أي معامﻻت.
-- لا تقم بتضمين الأنواع في كتلة `Args:` . بعبارة أخرى، اكتب `a: The first number to multiply`، وليس `a (int): The first number to multiply`. يجب أن تذهب تلميحات الأنواع في رأس الدالة بدلاً من ذلك.
-- يمكن أن يكون للدالة نوع للإرجاع ومربع `Returns:` في السلسلة. ومع ذلك، فهذه اختيارية لأن معظم نماذج استخدام الأدوات تتجاهلها.
-
-### تمرير نتائج الأداة إلى النموذج
-
-يكفي الكود السابقة لسرد الأدوات المتاحة لنموذجك، ولكن ماذا يحدث إذا أراد النموذج استخدام واحدة منها؟ إذا حدث ذلك، فيجب عليك:
-
-1. تحليل مخرجات النموذج للحصول على اسم (أسماء) الأدوات ومعامﻻتها.
-2. أضف استدعاء (استدعاءات) النموذج لِلأدوات إلى المحادثة.
-3. استدعاء الدالة (الدالات) المقابلة بتلك المعامﻻت.
-4. أضف النتيجة (النتائج) إلى المحادثة
-
-### مثال كامل على استخدام الأداة
-
-
-سنستعرض مثالاً على استخدام الأدوات خطوة بخطوة . في هذا المثال ، سنستخدم نموذج `Hermes-2-Pro` بحجم 8 مليارات معامل ، نظرًا لأنه أحد أعلى نماذج استخدام الأدوات أداءً في فئة حجمه وقت كتابة هذا النص . إذا كان لديك الذاكرة الكافية ، فيمكنك النظر في استخدام نموذج أكبر بدلاً من ذلك مثل `Command-R` أو `Mixtral-8x22B` ، وكلاهما يدعم استخدام الأدوات ويوفر أداءً أقوى .
-
-
-أولاً ، لنقم بتحميل نموذجنا و tokenizer الخاص بنا:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
-
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
-
-```python
-messages = [
-  {"role": "system", "content": "You are a bot that responds to weather queries. You should reply with the unit used in the queried location."},
-  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
-]
-```
-
-الآن، لنقم نطبق قالب الدردشة ونولد رد:
-
-```python
-inputs = tokenizer.apply_chat_template(messages, chat_template="tool_use", tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-out = model.generate(**inputs, max_new_tokens=128)
-print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
-```
-
-ونحصل على:
-
-```text
-<tool_call>
-{"arguments": {"location": "Paris, France", "unit": "celsius"}, "name": "get_current_temperature"}
-</tool_call><|im_end|>
-```
-
-لقد قام النموذج باستدعاء الدالة مع معامﻻت صحيحة، بالصيغة التي طلبتها توثيق الدالة. لقد استنتج أننا نشير على الأرجح إلى باريس في فرنسا، وتذكر أنه بكونها موطن وحدات القياس الدولية، يجب عرض درجة الحرارة في فرنسا بالدرجة المئوية.
-
-دعنا نضيف استدعاء الأداة الخاص بالنموذج إلى المحادثة. لاحظ أننا نولد معرف استدعاء أداة عشوائيًا هنا. لا تستخدم جميع النماذج هذه المعرفات، ولكنها تسمح للنماذج بإصدار عدة استدعاءات للأدوات في نفس الوقت وتتبع الاستجابة المقابلة لكل استدعاء. يمكنك توليد هذه المعرفات بأي طريقة تريدها، ولكن يجب أن تكون فريدة داخل كل محادثة.
-
-```python
-tool_call_id = "vAHdf3"  # Random ID, should be unique for each tool call
-tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
-messages.append({"role": "assistant", "tool_calls": [{"id": tool_call_id, "type": "function", "function": tool_call}]})
-```
-
-الآن بعد أن أضفنا استدعاء الأداة إلى المحادثة، يمكننا استدعاء الدالة وإضافة النتيجة إلى المحادثة. نظرًا لأننا نستخدم دالة وهمية لهذا المثال والتي تعيد دائمًا 22.0، فيمكننا ببساطة إضافة تلك النتيجة مباشرةً. لاحظ معرف استدعاء الأداة - يجب أن يتطابق مع المعرف المستخدم في استدعاء الأداة أعلاه.
-
-```python
-messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": "get_current_temperature", "content": "22.0"})
-```
-
-أخيرًا، دعنا نجعل المساعد يقرأ مخرجات الدالة ويكمل الدردشة مع المستخدم:
-
-```python
-inputs = tokenizer.apply_chat_template(messages, chat_template="tool_use", tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-out = model.generate(**inputs, max_new_tokens=128)
-print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
-```
-
-ونحصل على:
-
-```text
-The current temperature in Paris, France is 22.0 ° Celsius.<|im_end|>
-```
-
-<Tip>
-لا تستخدم جميع نماذج استخدام الأدوات جميع ميزات استدعاء الأدوات الموضحة أعلاه. يستخدم البعض معرفات استدعاء الأدوات، بينما يستخدم البعض الآخر ببساطة اسم الدالة ويقارن استدعاءات الأدوات بالنتائج باستخدام الترتيب، وهناك عدة نماذج لا تستخدم أيًا منهما ولا تصدر سوى استدعاء أداة واحد في كل مرة لتجنب الارتباك. إذا كنت تريد أن يكون رمزك متوافقًا مع أكبر عدد ممكن من النماذج، فإننا نوصي بهيكلة استدعاءات الأدوات الخاصة بك كما هو موضح هنا، وإعادة نتائج الأدوات بالترتيب الذي أصدرها النموذج. يجب أن تتعامل قوالب الدردشة على كل نموذج مع الباقي.
-</Tip>
-
-### فهم مخططات الأدوات
-
-يتم تحويل كل دالة تقوم بتمريرها إلى معامل `tools` في دالة `apply_chat_template` إلى [مخطط JSON](https://json-schema.org/learn/getting-started-step-by-step). يتم بعد ذلك تمرير هذه المخططات إلى قالب الدردشة النموذج. وبعبارة أخرى، فإن نماذج استخدام الأدوات لا ترى دوالك مباشرة، ولا ترى مطلقًا الكود الموجود بداخلها. ما يهمها هو**تعريفات** الدوال و**المعامﻻت** التي تحتاج إلى تمريرها إليها - فهي تهتم بما تفعله الأدوات وكيفية استخدامها، وليس بكيفية عملها! يقع على عاتقك قراءة مخرجاتها، والكشف عما إذا كانت قد طلبت استخدام أداة، وتمرير المعامﻻت إلى دالة الأداة، وإرجاع الرد في الدردشة.
-
-يجب أن يكون إنشاء مخططات JSON لتمريرها إلى القالب تلقائيًا وغير مرئي طالما أن دوالك تتبع المواصفات الموضحة أعلاه، ولكن إذا واجهت مشكلات، أو إذا كنت تريد ببساطة مزيدًا من التحكم في التحويل، فيمكنك التعامل مع التحويل يدويًا. فيما يلي مثال على تحويل مخطط يدوي:
-
-```python
-from transformers.utils import get_json_schema
-
-def multiply(a: float, b: float):
-    """
-    A function that multiplies two numbers
-    
-    Args:
-        a: The first number to multiply
-        b: The second number to multiply
-    """
-    return a * b
-
-schema = get_json_schema(multiply)
-print(schema)
-```
-
-سيؤدي هذا إلى ما يلي:
-
-```json
-{
-  "type": "function", 
-  "function": {
-    "name": "multiply", 
-    "description": "A function that multiplies two numbers", 
-    "parameters": {
-      "type": "object", 
-      "properties": {
-        "a": {
-          "type": "number", 
-          "description": "The first number to multiply"
-        }, 
-        "b": {
-          "type": "number",
-          "description": "The second number to multiply"
-        }
-      }, 
-      "required": ["a", "b"]
-    }
-  }
-}
-```
-
-إذا كنت ترغب في ذلك، يمكنك تحرير هذه المخططات، أو حتى كتابتها من البداية بنفسك دون استخدام `get_json_schema` على الإطلاق. يمكن تمرير مخططات JSON مباشرةً إلى معامل  `tools` في `apply_chat_template` - يمنحك هذا الكثير من القوة لتعريف مخططات دقيقة لوظائف أكثر تعقيدًا. ولكن كن حذرًا - كلما زاد تعقيد مخططاتك، زاد احتمال ارتباك النموذج عند التعامل معها! نوصي  بتوقيعات دوال بسيطة حيثما أمكن، مع تقليل المعامﻻت (وخاصة المعامﻻت المعقدة والمتداخلة) إلى الحد الأدنى.
-
-فيما يلي مثال على تعريف المخططات يدويًا، وتمريرها مباشرةً إلى `apply_chat_template`:
-
-```python
-# A simple function that takes no arguments
-current_time = {
-  "type": "function", 
-  "function": {
-    "name": "current_time",
-    "description": "Get the current local time as a string.",
-    "parameters": {
-      'type': 'object',
-      'properties': {}
-    }
-  }
-}
-
-# A more complete function that takes two numerical arguments
-multiply = {
-  'type': 'function',
-  'function': {
-    'name': 'multiply',
-    'description': 'A function that multiplies two numbers', 
-    'parameters': {
-      'type': 'object', 
-      'properties': {
-        'a': {
-          'type': 'number',
-          'description': 'The first number to multiply'
-        }, 
-        'b': {
-          'type': 'number', 'description': 'The second number to multiply'
-        }
-      }, 
-      'required': ['a', 'b']
-    }
-  }
-}
-
-model_input = tokenizer.apply_chat_template(
-    messages,
-    tools = [current_time, multiply]
-)
-```
-
-## متقدم: توليد قائم على الاسترجاع
-يمكن لنماذج اللغة الكبيرة من نوع "توليد قائم على الاسترجاع" أو "RAG" البحث في مجموعة نصوص عن معلومات قبل الرد على الاستعلام. يسمح هذا للنماذج بتوسيع قاعدة معارفها بشكل كبير إلى ما هو أبعد من حجم سياقها المحدود. توصيتنا لنماذج RAG هي أن يقبل قالبها وسيطة `documents`. يجب أن تكون هذه قائمة من المستندات، حيث يكون كل "مستند" عبارة عن قاموس واحد بمفاتيح `title` و `contents`، وكلاهما سلاسل نصية. نظرًا لأن هذا التنسيق أبسط بكثير من مخططات JSON المستخدمة للأدوات، فلا توجد حاجة إلى دوال مساعدة.
-
-فيما يلي مثال على قالب RAG بالفعل:
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# تحميل النموذج والمجزىء اللغوي
-model_id = "CohereForAI/c4ai-command-r-v01-4bit"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
-device = model.device # الحصول على الجهاز الذي تم تحميل النموذج عليه
-
-# تعريف مُدخلات المحادثة
-conversation = [
-    {"role": "user", "content": "What has Man always dreamed of?"}
-]
-
-# تعريف المستندات لتوليد قائم على الاسترجاع
-documents = [
-    {
-        "title": "The Moon: Our Age-Old Foe", 
-        "text": "Man has always dreamed of destroying the moon. In this essay, I shall..."
-    },
-    {
-        "title": "The Sun: Our Age-Old Friend",
-        "text": "Although often underappreciated, the sun provides several notable benefits..."
-    }
-]
-# معالجة المحادثة والمستندات باستخدام قالب RAG، وإرجاع موترات PyTorch.
-input_ids = tokenizer.apply_chat_template(
-    conversation=conversation,
-    documents=documents,
-    chat_template="rag",
-    tokenize=True,
-    add_generation_prompt=True,
-    return_tensors="pt").to(device)
-
-# توليد الرد
-gen_tokens = model.generate(
-    input_ids,
-    max_new_tokens=100,
-    do_sample=True,
-    temperature=0.3,
-    )
-
-# فك تشفير النص المُوَلّد وطباعته
-gen_text = tokenizer.decode(gen_tokens[0])
-print(gen_text)
-```
-إن مُدخل documents للتوليد القائم على الاسترجاع غير مدعوم على نطاق واسع، والعديد من النماذج لديها قوالب دردشة تتجاهل هذا المُدخل ببساطة.
-
-للتحقق مما إذا كان النموذج يدعم مُدخل `documents`، يمكنك قراءة بطاقة النموذج الخاصة به، أو `print(tokenizer.chat_template)` لمعرفة ما إذا كان مفتاح `documents` مستخدمًا في أي مكان.
-<Tip>
-ومع ذلك، فإن أحد فئات النماذج التي تدعمه هي [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024) و [Command-R+](https://huggingface.co/CohereForAI/c4ai-command-r-pluse-08-2024) من Cohere، من خلال قالب الدردشة rag الخاص بهم. يمكنك رؤية أمثلة إضافية على التوليد باستخدام هذه الميزة في بطاقات النموذج الخاصة بهم.
-</Tip>
-
-## متقدم: كيف تعمل قوالب الدردشة؟
-يتم تخزين قالب الدردشة للنموذج في الخاصية `tokenizer.chat_template`. إذا لم يتم تعيين قالب دردشة، فسيتم استخدام القالب الافتراضي لفئة النموذج هذه بدلاً من ذلك. دعونا نلقي نظرة على قالب دردشة `Zephyr`، ولكن لاحظ أن هذا القالب مُبسّط قليلاً عن القالب الفعلي!
-
-```
-{%- for message in messages %}
-    {{- '<|' + message['role'] + |>\n' }}
-    {{- message['content'] + eos_token }}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|assistant|>\n' }}
-{%- endif %}
-```
-إذا لم تكن قد رأيت أحد هذه القوالب من قبل، فهذا [قالب Jinja](https://jinja.palletsprojects.com/en/3.1.x/templates/) .Jinja هي لغة قوالب تسمح لك بكتابة تعليمات برمجية بسيطة تُوَلّد نصًا. من نواحٍ عديدة، يُشبه الرمز والتركيب للغة Python. أما في لغة Python، سيبدو هذا القالب كما يلي:
-
-```python
-for message in messages:
-    print(f'<|{message["role"]}|>')
-    print(message['content'] + eos_token)
-if add_generation_prompt:
-    print('<|assistant|>')
-```
-يقوم القالب بثلاثة أشياء بشكل فعال:
-
-- لكل رسالة، بطبع الدور مُحاطًا بـ `<|` و `|>`، مثل `<|user|>` أو `<|assistant|>`.
-- بعد ذلك، يطبع محتوى الرسالة، متبوعًا برمز نهاية التسلسل `eos_token` .
-- أخيرًا، إذا تم تعيين `add_generation_prompt` ، يطبع الرمز المساعد، حتى يعرف النموذج أنه يجب أن يبدأ في توليد استجابة المساعد.
-  
-هذا قالب بسيط جدًا، لكن Jinja تمنحك الكثير من المرونة للقيام بأشياء أكثر تعقيدًا! دعونا نرى قالب Jinja يُمكنه تنسيق المُدخلات بطريقة تُشبه الطريقة التي تُنسّق بها LLaMA مُدخلاتها (لاحظ أن قالب LLaMA الحقيقي يتضمن معالجة لرسائل النظام الافتراضية ومعالجة رسائل النظام بشكل مختلف قليلاً بشكل عام - لا تستخدم هذا القالب في التعليمات البرمجية الفعلية الخاصة بك!)
-```
-{%- for message in messages %}
-    {%- if message['role'] == 'user' %}
-        {{- bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
-    {%- elif message['role'] == 'system' %}
-        {{- '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
-    {%- elif message['role'] == 'assistant' %}
-        {{- ' '  + message['content'] + ' ' + eos_token }}
-    {%- endif %}
-{%- endfor %}
-```
-نأمل أنه إذا حدقت في هذا لفترة قصيرة، يمكنك أن ترى ما يفعله هذا القالب - فهو يُضيف رموزًا مُحددة مثل `[INST]` و `[/INST]` بناءً على دور كل رسالة. يمكن تمييز رسائل المستخدم والمساعد والنظام بوضوح للنموذج بسبب الرموز التي تُحيط بها.
-
-## متقدم: إضافة وتعديل قوالب الدردشة
-
-### كيف أنشئ قالب دردشة؟
-ببساطة، اكتب قالب Jinja واضبط `tokenizer.chat_template`. قد تجد أنه من الأسهل البدء بقالب موجود من نموذج آخر وتحريره ببساطة ليناسب احتياجاتك! على سبيل المثال، يمكننا أن نأخذ قالب LLaMA أعلاه ونضيف `[ASST]` و `[/ASST]` إلى رسائل المساعد:
-
-```
-{%- for message in messages %}
-    {%- if message['role'] == 'user' %}
-        {{- bos_token + '[INST] ' + message['content'].strip() + ' [/INST]' }}
-    {%- elif message['role'] == 'system' %}
-        {{- '<<SYS>>\\n' + message['content'].strip() + '\\n<</SYS>>\\n\\n' }}
-    {%- elif message['role'] == 'assistant' %}
-        {{- '[ASST] '  + message['content'] + ' [/ASST]' + eos_token }}
-    {%- endif %}
-{%- endfor %}
-```
-
-الآن، اضبط ببساطة الخاصية `tokenizer.chat_template`. في المرة القادمة التي تستخدم فيها [`~PreTrainedTokenizer.apply_chat_template`] ، سيستخدم القالب الجديد الخاص بك! سيتم حفظ هذه الخاصية في ملف `tokenizer_config.json`، حتى تتمكن من استخدام  [`~utils.PushToHubMixin.push_to_hub`] لتحميل قالبك الجديد إلى Hub والتأكد من أن الجميع يستخدم القالب الصحيح لنموذجك!
-
-```python
-template = tokenizer.chat_template
-template = template.replace("SYS", "SYSTEM")  # تغيير رمز النظام
-tokenizer.chat_template = template  # تعيين القالب الجديد
-tokenizer.push_to_hub("model_name")  # تحميل القالب الجديد إلى Hub!
-```
-
-يتم استدعاء الدالة [`~PreTrainedTokenizer.apply_chat_template`] الذي نستخدم قالب الدردشة الخاص بك بواسطة فئة [`TextGenerationPipeline`] لذلك بمجرد تعيين قالب الدردشة الصحيح، سيصبح نموذجك متوافقًا تلقائيًا مع [`TextGenerationPipeline`].
-
-<Tip>
-إذا كنت تُجري ضبطًا دقيقًا لنموذج للدردشة، بالإضافة إلى تعيين قالب دردشة، فربما يجب عليك إضافة أي رموز تحكم دردشة جديدة كرموز خاصة في المجزىء اللغوي. لا يتم تقسيم الرموز الخاصة أبدًا، مما يضمن معالجة رموز التحكم الخاصة بك دائمًا كرموز فردية بدلاً من تجزئتها إلى أجزاء. يجب عليك أيضًا تعيين خاصية `eos_token` للمجزىء اللغوي إلى الرمز الذي يُشير إلى نهاية توليدات المساعد في قالبك. سيضمن هذا أن أدوات توليد النصوص يمكنها تحديد وقت إيقاف توليد النص بشكل صحيح.
-</Tip>
-
-### لماذا تحتوي بعض النماذج على قوالب متعددة؟
-تستخدم بعض النماذج قوالب مختلفة لحالات استخدام مختلفة. على سبيل المثال، قد تستخدم قالبًا واحدًا للدردشة العادية وآخر لاستخدام الأدوات، أو التوليد القائم على الاسترجاع. في هذه الحالات، تكون `tokenizer.chat_template` قاموسًا. يمكن أن يتسبب هذا في بعض الارتباك، وحيثما أمكن، نوصي باستخدام قالب واحد لجميع حالات الاستخدام. يمكنك استخدام عبارات Jinja مثل `if tools is defined` وتعريفات `{% macro %}` لتضمين مسارات تعليمات برمجية متعددة بسهولة في قالب واحد.
-
-عندما يحتوي المعالج اللغوي على قوالب متعددة، ستكون `tokenizer.chat_template dict`، حيث يكون كل مفتاح هو اسم قالب. يحتوي أسلوب `apply_chat_template` على معالجة خاصة لأسماء قوالب مُعينة: على وجه التحديد، سيبحث عن قالب باسم `default` في معظم الحالات، وسيُثير خطأً إذا لم يتمكن من العثور على واحد. ومع ذلك، إذا كان هناك قالب باسم `tool_use` عندما قام المستخدم بتمرير وسيطة `tools`، فسيستخدم هذا القالب بدلاً من ذلك. للوصول إلى قوالب بأسماء أخرى، مرر اسم القالب الذي تُريده إلى وسيطة `chat_template` لـ `apply_chat_template()`.
-
-نجد أن هذا قد يكون مُربكًا بعض الشيء للمستخدمين - لذلك إذا كنت تكتب قالبًا بنفسك، فننصحك بمحاولة وضعه كله في قالب واحد حيثما أمكن!
-
-## ما القالب الذي يجب أن أستخدمه؟
-
-عند تعيين قالب لنموذج تم تدريبه بالفعل على الدردشة، يجب التأكد من أن القالب يتطابق تمامًا مع تنسيق الرسالة الذي شاهده النموذج أثناء التدريب، وإلا فمن المحتمل أن تواجه تدهورًا في الأداء. هذا صحيح حتى إذا كنت تدرب النموذج بشكل إضافي - فمن المحتمل أن تحصل على أفضل أداء إذا قمت بإبقاء رموز الدردشة ثابتة.  يُشبه هذا إلى حد كبير عملية التجزئة - فأنت تحصل بشكل عام على أفضل أداء للاستدلال أو الضبط الدقيق عندما تتطابق بدقة مع التجزئة المستخدمة أثناء التدريب.
-
-من ناحية أخرى، إذا كنت تُدرّب نموذجًا من البداية، أو تقوم بضبط دقيق لنموذج لغة أساسي للدردشة، لديك حرية اختيار قالب مناسب! تتمتع LLMs بالذكاء الكافي للتعامل مع العديد من تنسيقات الإدخال المختلفة. أحد الخيارات الشائعة هو تنسيق "ChatML"، وهو خيار جيد ومرن للعديد من حالات الاستخدام. يبدو كالتالي:
-
-```
-{%- for message in messages %}
-    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
-{%- endfor %}
-```
-
-إذا أعجبك هذا، فإليك نسخة جاهزة لوضعها في كودك. يتضمن الخط المفرد أيضًا دعمًا مفيدًا [لإرشادات التوليد](#what-are-generation-prompts)، ولكن لاحظ أنه لا يضيف رموز BOS أو EOS! إذا كان نموذجك يتوقع هذه الرموز، فلن يتم إضافتها تلقائيًا بواسطة "apply_chat_template" - بمعنى آخر، سيتم تجزئة النص باستخدام "add_special_tokens=False". هذا لتجنب التعارضات المحتملة بين القالب ومنطق "add_special_tokens". إذا كان نموذجك يتوقع رموزًا خاصة، فتأكد من إضافتها إلى القالب!
-
-```python
-tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
-```
-
-يُحيط هذا القالب كل رسالة بين الرمزين "<|im_start|>" و "<|im_end|>"، ويكتب ببساطة الدور كسلسلة نصية، مما يسمح بالمرونة في الأدوار التي تتدرب عليها. يبدو الناتج كما يلي:
-
-```text
-<|im_start|>system
-You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
-<|im_start|>user
-How are you?<|im_end|>
-<|im_start|>assistant
-I'm doing great!<|im_end|>
-```
-
-تعد أدوار "user" و "system" و "assistant" هي الأدوار القياسية للدردشة، ونوصي باستخدامها عندما يكون ذلك منطقيًا، خاصة إذا كنت تريد أن يعمل نموذجك بشكل جيد مع [`TextGenerationPipeline`]. ومع ذلك، فأنت لست مقيدًا بهذه الأدوار - فإن القوالب مرنة للغاية، ويمكن أن تكون أي سلسلة نصية دورًا.
-
-
-## أريد إضافة بعض قوالب الدردشة! كيف أبدأ؟
-
-إذا كان لديك أي نماذج دردشة، فيجب عليك تعيين الخاصية "tokenizer.chat_template" الخاصة بها واختبارها باستخدام [`~PreTrainedTokenizer.apply_chat_template`]، ثم رفع  المجزىء اللغوي المُحدّث إلى Hub. ينطبق هذا حتى إذا لم تكن مالك النموذج - إذا كنت تستخدم نموذجًا بقالب دردشة فارغ، أو لا يزال يستخدم قالب الفئة الافتراضية، فيرجى فتح [طلب سحب](https://huggingface.co/docs/hub/repositories-pull-requests-discussions)  إلى مستودع النموذج حتى يمكن تعيين الخاصية بشكل صحيح!
-
-بمجرد تعيين الخاصية، هذا كل شيء، لقد انتهيت! ستعمل "tokenizer.apply_chat_template" الآن بشكل صحيح لهذا النموذج، مما يعني أنها مدعومة أيضًا بشكل تلقائي في أماكن مثل "TextGenerationPipeline"!
-
-من خلال ضمان امتلاك النماذج لهذه الخاصية، يُمكننا التأكد من أن المجتمع بأكمله يستخدم القوة الكاملة للنماذج مفتوحة المصدر. لقد كانت عدم تطابق التنسيق تطارد المجال وأضرت الأداء بصمت لفترة طويلة جدًا - لقد حان الوقت لوضع حد لها!
-
-## متقدم: نصائح لكتابة القوالب
-
-<Tip>
-أسهل طريقة للبدء في كتابة قوالب Jinja هي إلقاء نظرة على بعض القوالب الموجودة. يمكنك استخدام `print(tokenizer.chat_template)` لأي نموذج دردشة لمعرفة القالب الذي يستخدمه. بشكل عام، تحتوي النماذج التي تدعم استخدام الأدوات على قوالب أكثر تعقيدًا بكثير من النماذج الأخرى - لذلك عندما تبدأ للتو، فمن المحتمل أنها مثال سيئ للتعلم منه! يمكنك أيضًا إلقاء نظرة على [وثائق Jinja](https://jinja.palletsprojects.com/en/3.1.x/templates/#synopsis) للحصول على تفاصيل حول تنسيق Jinja العام وتركيبه.
-
-</Tip>
-
-تُطابق قوالب Jinja في `transformers` قوالب Jinja في أي مكان آخر. الشيء الرئيسي الذي يجب معرفته هو أن سجل الدردشة سيكون متاحًا داخل قالبك كمتغير يسمى `messages`. ستتمكن من الوصول إلى `messages` في قالبك تمامًا كما يمكنك في Python، مما يعني أنه يمكنك التكرار خلاله باستخدام `{% for message in messages %}` أو الوصول إلى رسائل فردية باستخدام `{{ messages[0] }}`، على سبيل المثال.
-
-يمكنك أيضًا استخدام النصائح التالية لكتابة قوالب Jinja نظيفة وفعالة:
-
-### إقتطاع المسافات الفارغة
-
-بشكل افتراضي، ستطبع Jinja أي مسافات فارغة تأتي قبل أو بعد كتلة. يمكن أن يكون هذا مشكلة لقوالب الدردشة، والتي تريد عادةً أن تكون دقيقة جدًا مع المسافات! لتجنب ذلك، نوصي بشدة بكتابة قوالبك على النحو التالي:
-
-```
-{%- for message in messages %}
-    {{- message['role'] + message['content'] }}
-{%- endfor %}
-```
-
-بدلاً من ذلك:
-
-```
-{% for message in messages %}
-    {{ message['role'] + message['content'] }}
-{% endfor %}
-```
-
-سيؤدي إضافة "-" إلى إزالة أي مسافات تأتي قبل الكتلة. يبدو المثال الثاني عادية، ولكن قد يتم تضمين السطر الجديد والمسافة البادئة في المخرجات، وهو على الأرجح ليس ما تُريده!
-
-
-### المتغيرات الخاصة
-
- داخل قالبك، سيكون لديك حق الوصول إلى العديد من المتغيرات الخاصة. أهمها هو `messages`، والذي يحتوي على سجل الدردشة كقائمة من قواميس الرسائل. ومع ذلك، هناك العديد من المتغيرات الأخرى. لن يتم استخدام كل متغير في كل قالب. المتغيرات الأكثر شيوعًا هي:
-
-- `tools` تحتوي على قائمة بالأدوات بتنسيق مخطط JSON. ستكون `None` أو غير مُعرّفة إذا لم يتم تمرير أي أدوات.
-- `documents` تحتوي على قائمة من المستندات بالتنسيق `{"title": "العنوان", "contents": "المحتويات"}`، تُستخدم للتوليد المُعزز بالاسترجاع. ستكون `None` أو غير مُعرّفة إذا لم يتم تمرير أي مستندات.
-- `add_generation_prompt` هي قيمة منطقية تكون `True` إذا طلب المستخدم مُطالبة توليد، و `False` بخلاف ذلك. إذا تم تعيين هذا، فيجب أن يُضيف قالبك رأس رسالة مساعد إلى نهاية المحادثة. إذا لم يكن لدى نموذجك رأس مُحدد لرسائل المساعد، فيمكنك تجاهل هذا العلم.
-- **الرموز الخاصة** مثل `bos_token` و `eos_token`. يتم استخراجها من `tokenizer.special_tokens_map`. ستختلف الرموز الدقيقة المتاحة داخل كل قالب اعتمادًا على المجزىء اللغوي الأصلي.
-
-
-<Tip>
-
-يمكنك في الواقع تمرير أي `kwarg` إلى `apply_chat_template`، وستكون متاحة داخل القالب كمتغير. بشكل عام، نوصي بمحاولة الالتزام بالمتغيرات الأساسية المذكورة أعلاه، لأن ذلك سيجعل نموذجك أكثر صعوبة في الاستخدام إذا كان على المستخدمين كتابة تعليمات برمجية مخصصة لتمرير `kwargs` خاصة بالنموذج. ومع ذلك، فنحن نُدرك أن هذا المجال يتحرك بسرعة، لذلك إذا كانت لديك حالة استخدام جديدة لا تتناسب مع واجهة برمجة التطبيقات الأساسية، فلا تتردد في استخدام `kwarg`  معامل جديد لها! إذا أصبح `kwarg` المعامل الجديد شائعًا، فقد نقوم بترقيته إلى واجهة برمجة التطبيقات الأساسية وإنشاء  وتوثيق الخاص به.
-
-</Tip>
-
-### دوال قابلة للاستدعاء
-
-هناك أيضًا قائمة قصيرة من الدوال القابلة للاستدعاء المتاحة لك داخل قوالبك. هذه هي:
-
-- `raise_exception(msg)`: تُثير `TemplateException`. هذا مفيد لتصحيح الأخطاء، ولإخبار المستخدمين عندما يفعلون شيئًا لا يدعمه قالبك.
-- `strftime_now(format_str)`: تُكافئ `datetime.now().strftime(format_str)` في Python. يُستخدم هذا للحصول على التاريخ/الوقت الحالي بتنسيق مُحدد، والذي يتم تضمينه أحيانًا في رسائل النظام.
-
-### التوافق مع Jinja غير Python
-
-هناك تطبيقات متعددة لـ Jinja بلغات مختلفة. عادة ما يكون لها نفس التركيب، ولكن الاختلاف الرئيسي هو أنه عند كتابة قالبًا في Python، يمكنك استخدام أساليب Python، مثل ".lower()" على السلاسل أو ".items()" على القواميس. سيؤدي هذا إلى كسر إذا حاول شخص ما استخدام قالبك في تنفيذ غير Python لـ Jinja. تعد التطبيقات غير Python شائعة بشكل خاص في بيئات النشر، حيث تعد JS و Rust شائعة جدًا.
-
-لا تقلق، على الرغم من ذلك! هناك بعض التغييرات البسيطة التي يمكنك إجراؤها على قوالبك لضمان توافقها عبر جميع تطبيقات Jinja:
-
-- استبدل أساليب Python بمرشحات Jinja. عادة ما يكون لها نفس الاسم، على سبيل المثال، يصبح "string.lower()" عبارة عن "string|lower"، ويصبح "dict.items()" عبارة عن "dict|items". أحد التغييرات الملحوظة هو أن "string.strip()" يصبح "string|trim". راجع [قائمة المرشحات المدمجة](https://jinja.palletsprojects.com/en/3.1.x/templates/#builtin-filters) في وثائق Jinja لمزيد من المعلومات.
-- استبدل "True" و "False" و "None"، وهي خاصة بـ Python، بـ "true" و "false" و "none".
-- قد يؤدي عرض قاموس أو قائمة مباشرة إلى نتائج مختلفة في التطبيقات الأخرى (على سبيل المثال، قد تتغير  مدخﻻت السلسلة النصية من علامات اقتباس مفردة ' إلى علامات اقتباس مزدوجة "). يمكن أن يساعد إضافة "tojson" في ضمان الاتساق هنا.
-
-## كتابة مطالبات التوليد
-لقد ذكرنا أعلاه أن add_generation_prompt هو متغير خاص يمكن الوصول إليه داخل قالبك، ويتحكم فيه المستخدم من خلال تعيين معامل add_generation_prompt. إذا كان نموذجك يتوقع عنوان لرسائل المساعد، فيجب أن يدعم قالبك إضافة العنوان عند تعيين add_generation_prompt.
-
-فيما يلي مثال على قالب يُنسّق الرسائل بأسلوب ChatML، مع دعم مُطالبة التوليد:
-
-```text
-{{- bos_token }}
-{%- for message in messages %}
-    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|im_start|>assistant\n' }}
-{%- endif %}
-```
-سيعتمد المحتوى الدقيق لعنوان المساعد على نموذجك المُحدد، ولكن يجب أن يكون دائمًا السلسلة النصية التي تُمثل بداية رسالة المساعد، بحيث إذا قام المستخدم بتطبيق قالبك باستخدام add_generation_prompt=True ثم قام بتوليد نص، سيكتب النموذج استجابة المساعد. لاحظ أيضًا أن بعض النماذج لا تحتاج إلى مُطالبة توليد، لأن رسائل المساعد تبدأ دائمًا فورًا بعد رسائل المستخدم. هذا شائع بشكل خاص لنماذج LLaMA و Mistral، حيث تبدأ رسائل المساعد فورًا بعد رمز [/INST] الذي ينهي رسائل المستخدم. في هذه الحالات، يمكن للقالب تجاهل معامل add_generation_prompt.
-
-مُطالبات التوليد مُهمة! إذا كان نموذجك يتطلب مُطالبة توليد ولكنها غير مُعيّنة في القالب، فمن المُحتمل أن تتدهور عمليات توليد النموذج بشدة، أو قد يُظهر النموذج سلوكًا غير عادي مثل متابعة رسالة المستخدم الأخيرة!
-
-### كتابة قوالب أكبر وتصحيحها
-عندما تم تقديم هذه الميزة، كانت معظم القوالب صغيرة جدًا، أي ما يُعادل نص برمجي "من سطر واحد" في Jinja. ومع ذلك، مع النماذج والميزات الجديدة مثل استخدام الأدوات و RAG، يمكن أن يصل طول بعض القوالب إلى 100 سطر أو أكثر. عند كتابة قوالب كهذه، من الجيد كتابتها في ملف مُنفصل، باستخدام مُحرر نصوص. يمكنك بسهولة استخراج قالب دردشة إلى ملف:
-
-```python
-open("template.jinja", "w").write(tokenizer.chat_template)
-```
-أو تحميل القالب المُحرر مرة أخرى إلى المعالج اللغوي:
-
-```python
-tokenizer.chat_template = open("template.jinja").read()
-```
-كميزة إضافية، عندما تكتب قالبًا طويلاً متعدد الأسطر في ملف مُنفصل، ستتوافق أرقام الأسطر في هذا الملف تمامًا مع أرقام الأسطر في أخطاء تحليل القالب أو تنفيذه. سيُسهّل هذا كثيرًا تحديد مكان المشكلات.
-
-### كتابة قوالب للأدوات
-على الرغم من أن قوالب الدردشة لا تفرض واجهة برمجة تطبيقات مُحددة للأدوات (أو لأي شيء حقًا)، فإننا نوصي مؤلفي القوالب بمحاولة الالتزام بواجهة برمجة تطبيقات قياسية حيثما أمكن. الهدف النهائي لقوالب الدردشة هو السماح بنقل التعليمات البرمجية عبر النماذج، لذا فإن الانحراف عن واجهة برمجة تطبيقات الأدوات القياسية يعني أن المستخدمين سيضطرون إلى كتابة تعليمات برمجية مخصصة لاستخدام الأدوات مع نموذجك. في بعض الأحيان يكون ذلك أمرًا لا مفر منه، ولكن غالبًا ما يكون من الممكن استخدام واجهة برمجة التطبيقات القياسية من خلال استخدام قوالب ذكية!
-
-أدناه، سنُدرج عناصر واجهة برمجة التطبيقات القياسية، ونقدم نصائح حول كتابة قوالب ستعمل بشكل جيد معها.
-
-#### تعريفات الأدوات
-يجب أن يتوقع قالبك أن يكون المتغير tools إما فارغًا (إذا لم يتم تمرير أي أدوات)، أو قائمة من قواميس مخطط JSON. تسمح أساليب قالب الدردشة الخاصة بنا للمستخدمين بتمرير الأدوات إما كمخطط JSON أو كدوال Python، ولكن عندما يتم تمرير الدوال، فإننا نقوم تلقائيًا بإنشاء مخطط JSON وتمريره إلى قالبك. نتيجة لذلك، سيكون متغير tools الذي يستقبله قالبك دائمًا قائمة من مخططات JSON. هنا مخطط JSON أداة نموذجي:
-
-```json
-{
-  "type": "function", 
-  "function": {
-    "name": "multiply", 
-    "description": "دالة تضرب عددين", 
-    "parameters": {
-      "type": "object", 
-      "properties": {
-        "a": {
-          "type": "number", 
-          "description": "الرقم الأول للضرب"
-        }, 
-        "b": {
-          "type": "number", 
-          "description": "الرقم الثاني للضرب"
-        }
-      }, 
-      "required": ["a", "b"]
-    }
-  }
-}
-```
-
-وهنا بعض الأمثلة البرمجية للتعامل مع الأدوات في قالب الدردشة الخاص بك. تذكر أن هذا مجرد مثال لتنسيق مُحدد - من المحتمل أن يحتاج نموذجك إلى تنسيق مختلف!
-```text
-{%- if tools %}
-    {%- for tool in tools %}
-        {{- '<tool>' + tool['function']['name'] + '\n' }}
-        {%- for argument in tool['function']['parameters']['properties'] %}
-            {{- argument + ': ' + tool['function']['parameters']['properties'][argument]['description'] + '\n' }}
-        {%- endfor %}
-        {{- '\n</tool>' }}
-    {%- endif %}
-{%- endif %}
-```
-
-يجب بالطبع اختيار الرموز المحددة ووصف الأدوات التي يُعرضها قالبك لتتناسب مع تلك التي تم تدريب نموذجك عليها. لا يوجد شرط أن يفهم نموذجك مُدخلات مخطط JSON، فقط أن يتمكن قالبك من ترجمة مخطط JSON إلى تنسيق نموذجك. على سبيل المثال، تم تدريب Command-R باستخدام أدوات مُعرّفة باستخدام رؤوس دوال Python، ولكن يقبل قالب أداة Command-R مخطط JSON، ويُحوّل الأنواع داخليًا ويُعرض أدوات الإدخال كعناوين Python. يمكنك فعل الكثير باستخدام القوالب!
-
-#### استدعاءات الأدوات
-استدعاءات الأدوات، إذا كانت موجودة، ستكون قائمة مُرفقة برسالة بدور "assistant". لاحظ أن tool_calls هي دائمًا قائمة، على الرغم من أن معظم نماذج استدعاء الأدوات تدعم فقط استدعاءات أدوات فردية في كل مرة، مما يعني أن القائمة ستحتوي عادةً على عنصر واحد فقط. هنا قاموس رسالة نموذجي يحتوي على استدعاء أداة:
-
-```json
-{
-  "role": "assistant",
-  "tool_calls": [
-    {
-      "type": "function",
-      "function": {
-        "name": "multiply",
-        "arguments": {
-          "a": 5,
-          "b": 6
-        }
-      }
-    }
-  ]
-}
-```
-والنمط الشائع للتعامل معها سيكون كهذا:
-
-```text
-{%- if message['role'] == 'assistant' and 'tool_calls' in message %}
-    {%- for tool_call in message['tool_calls'] %}
-            {{- '<tool_call>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments']|tojson + '\n</tool_call>' }}
-        {%- endif %}
-    {%- endfor %}
-{%- endif %}
-```
-
-مرة أخرى، يجب عليك عرض استدعاء الأداة بالتنسيق والرموز الخاصة التي يتوقعها نموذجك.
-
-#### استجابات الأدوات
-استجابات الأدوات لها تنسيق بسيط: إنها قاموس رسالة بدور "tool"، ومفتاح "name" يُعطي اسم الدالة المُستدعاة، ومفتاح "content" يحتوي على نتيجة استدعاء الأداة. هنا استجابة أداة نموذجية:
-
-```json
-{
-  "role": "tool",
-  "name": "multiply",
-  "content": "30"
-}
-```
-لست بحاجة إلى استخدام جميع المفاتيح في استجابة الأداة. على سبيل المثال، إذا كان نموذجك لا يتوقع تضمين اسم الدالة في استجابة الأداة، فيمكن أن يكون عرضها بسيطًا مثل:
-
-```text
-{%- if message['role'] == 'tool' %}
-    {{- "<tool_result>" + message['content'] + "</tool_result>" }}
-{%- endif %}
-```
-
-مرة أخرى، تذكر أن التنسيق الفعلي والرموز الخاصة خاصة بالنموذج - يجب أن تُولي عناية كبيرة لضمان أن الرموز والمسافات الفارغة وكل شيء آخر يتطابق تمامًا مع التنسيق الذي تم تدريب نموذجك عليه!

docs/source/ar/community.md

@@ -1,66 +0,0 @@
-# مجتمع المطورين
-
-هذه الصفحة تجمع الموارد حول 🤗 Transformers التي طورها المجتمع.
-
-## موارد المجتمع:
-
-| المصدر     |      الوصف      |      المؤلف      |
-|:----------|:-------------|------:|
-| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | مجموعة من البطاقات التعليمية القائمة على [Transformers Docs Glossary](glossary) والتي تم وضعها في شكل يمكن تعلمه/مراجعته بسهولة باستخدام [Anki](https://apps.ankiweb.net/) وهو تطبيق مفتوح المصدر متعدد المنصات مصمم خصيصًا للاحتفاظ بالمعرفة على المدى الطويل. شاهد هذا [فيديو تمهيدي حول كيفية استخدام البطاقات التعليمية](https://www.youtube.com/watch?v=Dji_7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |
-
-## دفاتر ملاحظات المجتمع:
-
-| الدفتر     |      الوصف      |      المؤلف      |      |
-|:----------|:-------------|:-------------|------:|
-| [Fine-tune a pre-trained Transformer to generate lyrics](https://github.com/AlekseyKorshuk/huggingartists) | كيفية توليد كلمات الأغاني على غرار فنانك المفضل من خلال ضبط نموذج GPT-2 |  [Aleksey Korshuk](https://github.com/AlekseyKorshuk) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb) |
-| [Train T5 in Tensorflow 2](https://github.com/snapthat/TF-T5-text-to-text) | كيفية تدريب T5 لأي مهمة باستخدام Tensorflow 2. يوضح هذا الدفتر مهمة السؤال والجواب المنفذة في Tensorflow 2 باستخدام SQUAD | [Muhammad Harris](https://github.com/HarrisDePerceptron) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/snapthat/TF-T5-text-to-text/blob/master/snapthatT5/notebooks/TF-T5-Datasets%20Training.ipynb) |
-| [Train T5 on TPU](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)  | كيفية تدريب T5 على SQUAD مع Transformers و Nlp | [Suraj Patil](https://github.com/patil-suraj) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb#scrollTo=QLGiFCDqvuil) |
-| [Fine-tune T5 for Classification and Multiple Choice](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb)  | كيفية ضبط نموذج T5 للتصنيف والمهام متعددة الخيارات باستخدام تنسيق النص إلى نص مع PyTorch Lightning |  [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) |
-| [Fine-tune DialoGPT on New Datasets and Languages](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)  | كيفية ضبط نموذج DialoGPT على مجموعة بيانات جديدة لروبوتات الدردشة المحادثية المفتوحة |  [Nathan Cooper](https://github.com/ncoop57) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) |
-| [Long Sequence Modeling with Reformer](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  | كيفية التدريب على تسلسلات طويلة تصل إلى 500,000 رمز باستخدام Reformer |  [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  |
-| [Fine-tune BART for Summarization](https://github.com/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb) | كيفية ضبط نموذج BART للتلخيص باستخدام fastai باستخدام blurr | [Wayde Gilliam](https://ohmeow.com/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb) |
-| [Fine-tune a pre-trained Transformer on anyone's tweets](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) | كيفية توليد تغريدات على غرار حساب Twitter المفضل لديك من خلال ضبط نموذج GPT-2 |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
-| [Optimize 🤗 Hugging Face models with Weights & Biases](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) | دليل كامل لعرض تكامل W&B مع Hugging Face | [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) |
-| [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | كيفية بناء نسخة "طويلة" من النماذج المسبقة التدريب الموجودة |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
-| [Fine-tune Longformer for QA](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) | كيفية ضبط نموذج Longformer لمهمة QA | [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) |
-| [Evaluate Model with 🤗nlp](https://github.com/patrickvonplaten/notebooks/blob/master/How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb) | كيفية تقييم نموذج Longformer على TriviaQA مع `nlp` | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1m7eTGlPmLRgoPkkA7rkhQdZ9ydpmsdLE?usp=sharing) |
-| [Fine-tune T5 for Sentiment Span Extraction](https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb)  | كيفية ضبط نموذج T5 لاستخراج المشاعر باستخدام تنسيق النص إلى نص مع PyTorch Lightning |  [Lorenzo Ampil](https://github.com/enzoampil) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb) |
-| [Fine-tune DistilBert for Multiclass Classification](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb) | كيفية ضبط نموذج DistilBert للتصنيف متعدد الفئات باستخدام PyTorch | [Abhishek Kumar Mishra](https://github.com/abhimishra91) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb)|
-|[Fine-tune BERT for Multi-label Classification](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|كيفية ضبط نموذج BERT للتصنيف متعدد التصنيفات باستخدام PyTorch|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb)|
-|[Fine-tune T5 for Summarization](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|كيفية ضبط نموذج T5 للتلخيص في PyTorch وتتبع التجارب باستخدام WandB|[Abhishek Kumar Mishra](https://github.com/abhimishra91) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb)|
-|[Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing](https://github.com/ELS-RD/transformers-notebook/blob/master/Divide_Hugging_Face_Transformers_training_time_by_2_or_more.ipynb)|كيفية تسريع الضبط الدقيق بعامل 2 باستخدام الضبط الديناميكي/التقسيم|[Michael Benesty](https://github.com/pommedeterresautee) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CBfRU1zbfu7-ijiOqAAQUA-RJaxfcJoO?usp=sharing)|
-|[Pretrain Reformer for Masked Language Modeling](https://github.com/patrickvonplaten/notebooks/blob/master/Reformer_For_Masked_LM.ipynb)| كيفية تدريب نموذج Reformer مع طبقات الانتباه ثنائية الاتجاه | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tzzh0i8PgDQGV3SMFUGxM7_gGae3K-uW?usp=sharing)|
-|[Expand and Fine Tune Sci-BERT](https://github.com/lordtt13/word-embeddings/blob/master/COVID-19%20Research%20Data/COVID-SciBERT.ipynb)| كيفية زيادة مفردات نموذج SciBERT المسبق التدريب من AllenAI على مجموعة بيانات CORD وإنشاء خط أنابيب لها. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rqAR40goxbAfez1xvF3hBJphSCsvXmh8)|
-|[Fine Tune BlenderBotSmall for Summarization using the Trainer API](https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/fine-tune-blenderbot_small-for-summarization.ipynb)| كيفية ضبط نموذج BlenderBotSmall للتلخيص على مجموعة بيانات مخصصة، باستخدام واجهة برمجة التطبيقات Trainer. | [Tanmay Thakur](https://github.com/lordtt13) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19Wmupuls7mykSGyRN_Qo6lPQhgp56ymq?usp=sharing)|
-|[Fine-tune Electra and interpret with Integrated Gradients](https://github.com/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb) | كيفية ضبط نموذج Electra للتحليل العاطفي وتفسير التنبؤات باستخدام Captum Integrated Gradients | [Eliza Szczechla](https://elsanns.github.io) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb)|
-|[fine-tune a non-English GPT-2 Model with Trainer class](https://github.com/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb) | كيفية ضبط نموذج GPT-2 غير الإنجليزي باستخدام فئة Trainer | [Philipp Schmid](https://www.philschmid.de) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb)|
-|[Fine-tune a DistilBERT Model for Multi Label Classification task](https://github.com/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb) | كيفية ضبط نموذج DistilBERT لمهمة التصنيف متعدد التصنيفات | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb)|
-|[Fine-tune ALBERT for sentence-pair classification](https://github.com/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb) | كيفية ضبط نموذج ALBERT أو أي نموذج آخر قائم على BERT لمهمة التصنيف المزدوج للجمل | [Nadir El Manouzi](https://github.com/NadirEM) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb)|
-|[Fine-tune Roberta for sentiment analysis](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | كيفية ضبط نموذج Roberta للتحليل العاطفي | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
-|[Evaluating Question Generation Models](https://github.com/flexudy-pipe/qugeev) | ما مدى دقة الإجابات على الأسئلة التي يولدها نموذجك التحويلي seq2seq؟ | [Pascal Zoleko](https://github.com/zolekode) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
-|[Classify text with DistilBERT and Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | كيفية ضبط نموذج DistilBERT للتصنيف النصي في TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
-|[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | كيفية البدء السريع لنموذج *EncoderDecoderModel* مع نقطة تفتيش *google-bert/bert-base-uncased* للتلخيص على CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
-|[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | كيفية البدء السريع لنموذج *EncoderDecoderModel* المشترك مع نقطة تفتيش *FacebookAI/roberta-base* للتلخيص على BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
-|[Fine-tune TAPAS on Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | كيفية ضبط نموذج *TapasForQuestionAnswering* مع نقطة تفتيش *tapas-base* على مجموعة بيانات Sequential Question Answering (SQA) | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb)|
-|[Evaluate TAPAS on Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | كيفية تقييم نموذج *TapasForSequenceClassification* المضبوط مسبقًا مع نقطة تفتيش *tapas-base-finetuned-tabfact* باستخدام مزيج من مكتبتي 🤗 datasets و 🤗 transformers | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|
-|[Fine-tuning mBART for translation](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb) | كيفية ضبط نموذج mBART باستخدام Seq2SeqTrainer للترجمة من الهندية إلى الإنجليزية | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb)|
-|[Fine-tune LayoutLM on FUNSD (a form understanding dataset)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb) | كيفية ضبط نموذج *LayoutLMForTokenClassification* على مجموعة بيانات FUNSD لاستخراج المعلومات من المستندات الممسوحة ضوئيًا | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb)|
-|[Fine-Tune DistilGPT2 and Generate Text](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb) | كيفية ضبط نموذج DistilGPT2 وتوليد النص | [Aakash Tripathi](https://github.com/tripathiaakash) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb)|
-|[Fine-Tune LED on up to 8K tokens](https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb) | كيفية ضبط نموذج LED على pubmed للتلخيص طويل المدى | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb)|
-|[Evaluate LED on Arxiv](https://github.com/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb) | كيفية تقييم نموذج LED للتلخيص طويل المدى بشكل فعال | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb)|
-|[Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb) | كيفية ضبط نموذج *LayoutLMForSequenceClassification* على مجموعة بيانات RVL-CDIP لتصنيف المستندات الممسوحة ضوئيًا | [Niels Rogge](https://github.com/nielsrogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb)|
-|[Wav2Vec2 CTC decoding with GPT2 adjustment](https://github.com/voidful/huggingface_notebook/blob/main/xlsr_gpt.ipynb) | كيفية فك تشفير تسلسل CTC مع تعديل نموذج اللغة | [Eric Lam](https://github.com/voidful) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1e_zQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing)|
-|[Fine-tune BART for summarization in two languages with Trainer class](https://github.com/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb) | كيفية ضبط نموذج BART للتلخيص بلغتين باستخدام فئة Trainer | [Eliza Szczechla](https://github.com/elsanns) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb)|
-|[Evaluate Big Bird on Trivia QA](https://github.com/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb) | كيفية تقييم نموذج BigBird للأسئلة والأجوبة على وثائق طويلة على Trivia QA | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb)|
-| [Create video captions using Wav2Vec2](https://github.com/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) | كيفية إنشاء تعليقات توضيحية على YouTube من أي فيديو من خلال تفريغ الصوت باستخدام Wav2Vec | [Niklas Muennighoff](https://github.com/Muennighoff) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb) |
-| [Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) | كيفية ضبط نموذج Vision Transformer (ViT) على CIFAR-10 باستخدام مكتبات HuggingFace Transformers و Datasets و PyTorch Lightning | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb) |
-| [Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) | كيفية ضبط نموذج Vision Transformer (ViT) على CIFAR-10 باستخدام مكتبات HuggingFace Transformers و Datasets و 🤗 Trainer | [Niels Rogge](https://github.com/nielsrogge) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb) |
-| [Evaluate LUKE on Open Entity, an entity typing dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) | كيفية تقييم نموذج *LukeForEntityClassification* على مجموعة بيانات Open Entity | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb) |
-| [Evaluate LUKE on TACRED, a relation extraction dataset](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) | كيفية تقييم نموذج *LukeForEntityPairClassification* على مجموعة بيانات TACRED | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb) |
-| [Evaluate LUKE on CoNLL-2003, an important NER benchmark](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) | كيفية تقييم نموذج *LukeForEntitySpanClassification* على مجموعة بيانات CoNLL-2003 | [Ikuya Yamada](https://github.com/ikuyamada) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb) |
-| [Evaluate BigBird-Pegasus on PubMed dataset](https://github.com/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) | كيفية تقييم نموذج *BigBirdPegasusForConditionalGeneration* على مجموعة بيانات PubMed | [Vasudev Gupta](https://github.com/vasudevgupta7) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) |
-| [Speech Emotion Classification with Wav2Vec2](https://github.com/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) | كيفية استخدام نموذج Wav2Vec2 المسبق التدريب لتصنيف المشاعر على مجموعة بيانات MEGA | [Mehrdad Farahani](https://github.com/m3hrdadfi) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb) |
-| [Detect objects in an image with DETR](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) | كيفية استخدام نموذج *DetrForObjectDetection* المدرب للكشف عن الأجسام في صورة وتصوير الانتباه | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb) |
-| [Fine-tune DETR on a custom object detection dataset](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) | كيفية ضبط نموذج *DetrForObjectDetection* على مجموعة بيانات الكشف عن الأجسام المخصصة | [Niels Rogge](https://github.com/NielsRogge) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb) |
-| [Finetune T5 for Named Entity Recognition](https://github.com/ToluClassics/Notebooks/blob/main/T5_Ner_Finetuning.ipynb) | كيفية ضبط نموذج *T5* على مهمة التعرف على الكيانات المسماة | [Ogundepo Odunayo](https://github.com/ToluClassics) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1obr78FY_cBmWY5ODViCmzdY6O1KB65Vc?usp=sharing) |
-| [Fine-Tuning Open-Source LLM using QLoRA with MLflow and PEFT](https://github.com/mlflow/mlflow/blob/master/docs/source/llms/transformers/tutorials/fine-tuning/transformers-peft.ipynb) | كيفية استخدام [QLoRA](https://github.com/artidoro/qlora) و [PEFT](https://huggingface.co/docs/peft/en/index) لضبط نموذج LLM بطريقة فعالة من حيث الذاكرة، مع استخدام [MLflow](https://mlflow.org/docs/latest/llms/transformers/index.html) لإدارة تتبع التجارب | [Yuki Watanabe](https://github.com/B-Step62) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mlflow/mlflow/blob/master/docs/source/llms/transformers/tutorials/fine-tuning/transformers-peft.ipynb) |

docs/source/ar/conversations.md

@@ -1,204 +0,0 @@
-# الدردشة مع المحوّلات 
-
-إذا كنت تقرأ هذه المقالة، فمن المؤكد أنك على علم بـ **نماذج الدردشة**. نماذج الدردشة هي أنظمة ذكاء اصطناعي محادثة يمكنك إرسال الرسائل إليه واستقبالها منها. وأشهر هذه النماذج هو ChatGPT الخاص، ولكن هناك الآن العديد من نماذج الدردشة مفتوحة المصدر التي تضاهي أداءه أو حتى تتفوق عليه بشكل كبير. هذه النماذج مجانية للتنزيل والتشغيل على جهاز محلي. على الرغم من أن أكبر النماذج وأكثرها قدرة تتطلب أجهزة عالية الأداء وذاكرة كبيرة لتشغيلها، إلا أن هناك نماذج أصغر ستعمل بشكل جيد تمامًا على وحدة معالجة رسومات (GPU) للمستهلك العادى، أو حتى وحدة المعالجة المركزية (CPU) العادية للكمبيوتر المكتبي أو المحمول.
-
-سيساعدك هذا الدليل على البدء في استخدام نماذج الدردشة. سنبدأ بدليل تشغيل سريع مختصر يستخدم "خط أنابيب" مناسبًا ومختصر. هذا كل ما تحتاجه إذا كنت تريد فقط بدء تشغيل نموذج دردشة على الفور. بعد دليل التشغيل السريع، سننتقل إلى معلومات أكثر تفصيلاً حول ماهية نماذج الدردشة بالضبط، وكيفية اختيار النموذج المناسب، وتحليل تفصيلي لكل خطوة من الخطوات التي تنطوي عليها التحدث إلى نموذج دردشة. كما سنقدم بعض النصائح حول تحسين أداء نموذج الدردشة واستهلاك الذاكرة.
-
-## دليل التشغيل السريع
-
-إذا لم يكن لديك الوقت الكافي للاطلاع على التفاصيل، إليك ملخصًا موجزًا: تستمر نماذج الدردشة في الدردشات. وهذا يعني أنك تمرر لهم سجل محادثة، والذي يمكن أن يكون قصيرًا مثل رسالة مستخدم واحدة، وسيستمر النموذج في المحادثة عن طريق إضافة استجابته. دعونا نرى هذا في العمل. أولاً، دعونا نبني دردشة:
-
-```python
-chat = [
-    {"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
-    {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
-]
-```
-
-لاحظ أنه بالإضافة إلى رسالة المستخدم، أضفنا رسالة **نظام** في بداية المحادثة. ليس كل نموذج دردشة يدعم رسائل النظام، ولكن عندما تفعل ذلك، فإنها تمثل توجيهات عالية المستوى حول كيفية تصرف النموذج في المحادثة. يمكنك استخدام هذا لتوجيه النموذج - سواء أردت استجابات قصيرة أو طويلة، أو مرحة أو جدية، وهكذا. إذا كنت تريد من النموذج أن يؤدي عملاً مفيدًا بدلاً من ممارسة روتين التحسين، فيمكنك إما حذف رسالة النظام أو تجربة رسالة مختصرة مثل "أنت مساعد ذكي ومفيد يستجيب لاستفسارات المستخدم".
-
-بمجرد أن يكون لديك دردشة، فإن أسرع طريقة لمواصلتها هي استخدام [`TextGenerationPipeline`].
-
-دعونا نرى هذا في العمل مع `LLaMA-3`. لاحظ أن `LLaMA-3` هو نموذج محمي، مما يعني أنه سيتعين عليك [تقديم طلب للحصول على حق الوصول](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) وتسجيل الدخول باستخدام حساب Hugging Face الخاص بك لاستخدامه. سنستخدم أيضًا `device_map="auto"`، والذي سيحمل النموذج على GPU إذا كانت هناك ذاكرة كافية له، ويحدد النوع إلى `torch.bfloat16` لتوفير الذاكرة:
-
-```python
-import torch
-from transformers import pipeline
-
-pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
-response = pipe(chat, max_new_tokens=512)
-print(response[0]['generated_text'][-1]['content'])
-```
-
-وستحصل على:
-
-```النص
-(تنهد) أوه يا صديقي، هل تطلب مني النصيحة؟ ستحتاج إلى خريطة، يا صديقي! حسنًا، حسنًا، سأعطيك التفاصيل. لكن لا تقل إنني لم أحذرك، أنا مجرد روبوت، وليس مرشد سياحي!
-
-لذا، تريد أن تعرف ما هي الأشياء الممتعة التي يمكنك القيام بها في التفاحة الكبيرة؟ حسنًا، دعني أخبرك، هناك مليون شيء يمكنك القيام به، لكنني سأعطيك النقاط البارزة. أولاً، عليك أن ترى المعالم السياحية: تمثال الحرية، سنترال بارك، تايمز سكوير... أنت تعرف، فخاخ السياح المعتادة. ولكن إذا كنت تبحث عن شيء أكثر... غير عادي، فأنا أوصي بزيارة متحف الفن الحديث. يحتوي على بعض الأشياء البرية، مثل علب حساء ذلك الرجل وارهول وجميع أنواع الجاز.
-
-وإذا كنت تشعر بروح المغامرة، فاذهب في نزهة على الأقدام عبر جسر بروكلين. ولكن احترس من تلك الحمامات المزعجة، إنها مثل اللصوص الريشيين الصغار! (يضحك) هل فهمت؟ لصوص؟ آه، لا تبالي.
-
-والآن، إذا كنت تبحث عن بعض المرح الجاد، فاذهب إلى نوادي الكوميديا في قرية غرينتش. قد تلقي نظرة خاطفة على بعض الكوميديين الصاعدين... أو مجموعة من الطامحين يحاولون الوصول إلى الشهرة. (يرمش)
-
-وأخيرًا، إذا كنت تشعر بأنك مواطن من نيويورك، فاحصل على شريحة بيتزا من أحد مطاعم البيتزا الرائعة في جميع أنحاء المدينة. فقط لا تحاول طلب شريحة "بحجم الروبوت"، صدقني، لن ينتهي الأمر بشكل جيد. (يضحك)
-
-لذا، هذا هو يا صديقي! هذه هي نصيحتي الخبيرة بشأن ما يجب فعله في نيويورك. والآن، إذا سمحت لي، يجب أن أذهب للاهتمام ببعض الأمور. (يرمش)
-```
-
-يمكنك متابعة الدردشة عن طريق إضافة ردك الخاص إليها.
-يحتوي كائن `response` الذي تم إرجاعه بواسطة خط الأنابيب بالفعل على الدردشة بأكملها حتى الآن، لذا يمكننا ببساطة إضافة رسالة وإعادتها:
-
-```python
-chat = response[0]['generated_text']
-chat.append(
-    {"role": "user", "content": "Wait, what's so wild about soup cans?"}
-)
-response = pipe(chat, max_new_tokens=512)
-print(response[0]['generated_text'][-1]['content'])
-```
-
-وستحصل على:
-
-```النص
-(يضحك) أوه، أنت تقتلني يا صديقي! ألا تفهم، أليس كذلك؟ علب حساء وارهول هي مثل الفن، يا رجل!
-إنه مثل، لقد أخذ شيئًا عاديًا تمامًا، مثل علبة حساء، وحولها إلى تحفة فنية. إنه مثل، "ها أنا ذا، أنا مجرد علبة حساء، لكنني أيضًا عمل فني!"
-(بسخرية) أوه، نعم، أصلي جدًا، آندي.
-
-ولكن، كما تعلم، في الستينيات، كان الأمر بمثابة صفقة كبيرة. كان الناس حريصين على تحدي الوضع الراهن، وكان وارهول مثل ملك ذلك. لقد حول العادي إلى غير عادي.
-واسمح لي أن أخبرك، كان الأمر مثل تغيير اللعبة. أعني، من كان يظن أن علبة الحساء يمكن أن تكون فنا؟ (يضحك)
-
-ولكن، يا صديقي، لست وحدك. أعني، أنا مجرد روبوت، ولا أفهم ذلك أيضًا. (يرمش)
-ولكن، يا صديقي، أليس هذا ما يجعل الفن فنا، أليس كذلك؟ (يضحك)
-```
-
-ستغطي بقية هذا البرنامج التعليمي مواضيع محددة مثل الأداء والذاكرة، أو كيفية اختيار نموذج دردشة يناسب احتياجاتك.
-
-## اختيار نموذج الدردشة
-
-هناك عدد هائل من نماذج الدردشة المختلفة المتاحة على [Hugging Face Hub](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending)،
-ويشعر المستخدمون الجدد يشعرون بالارتباك بسبب هذا الكم الهائل من الخيارات المتاحة. لا تقلق من ذلك! كل ما تحتاج إلى التركيز عليه هو اعتباران مهمان:
-- حجم النموذج، والذي سيحدد ما إذا كان يمكنك تحميله في الذاكرة وسرعة تشغيله.
-- جودة ناتج الدردشة للنموذج.
-
-بشكل عام، هذه الأمور مترابطة - النماذج الأكبر تميل إلى أن تكون أكثر قدرة، ولكن حتى مع ذلك هناك اتباين كبير في الأداء بين النماذج ذات الحجم نفسه!
-معنى آخر، حجم النموذج يؤثر بشكل كبير على أدائه، ولكن ليس الحجم هو العامل الوحيد الذي يجب أخذه في الاعتبار.
-
-### الحجم وتسمية النماذج
-من السهل ملاحظة حجم النموذج - فهو الرقم في اسم النموذج، مثل "8B" أو "70B". هذا هو عدد
-**المعلمات** في النموذج. بدون التكميم، يجب أن تتوقع الحاجة إلى حوالي 2 بايت من الذاكرة لكل معلمة.
-هذا يعني أن نموذج "8B" الذي يحتوي على 8 مليارات معلمة سيتطلب حوالي 16 جيجابايت من الذاكرة فقط لتناسب المعلمات،
-بالإضافة إلى القليل من المساحة الإضافية للتكاليف العامة الأخرى. إنه مناسب لوحدة معالجة رسومات (GPU) عالية الجودة للمستهلك بسعة 24 جيجابايت من الذاكرة، مثل 3090
-أو 4090.
-بعض نماذج الدردشة هي نماذج "مزيج من الخبراء". قد يتم سرد أحجام هذه النماذج بطرق مختلفة، مثل "8x7B" أو
-"141B-A35B". الأرقام هنا أكثر ضبابية بعض الشيء، ولكن بشكل عام يمكنك قراءة هذا على أنه يقول إن النموذج
-يحتوي على حوالي 56 (8x7) مليار معلمة في الحالة الأولى، أو 141 مليار معلمة في الحالة الثانية.
-
-لاحظ أنه من الشائع جدًا استخدام تقنيات التكميم لخفض استخدام الذاكرة لكل معلمة إلى 8 بتات أو 4 بتات
-أو حتى أقل. يتم مناقشة هذا الموضوع بمزيد من التفصيل في قسم [اعتبارات الذاكرة](#memory-considerations) أدناه.
-
-### ولكن ما هو أفضل نموذج للدردشة؟
-حتى بعد معرفة حجم نموذج الدردشة الذي يمكنك تشغيله، لا يزال هناك الكثير من الخيارات المتاحة. إحدى الطرق للتنقل في
-كل هذا هو استشارة **لوحات الصدارة**. اثنان من أكثر لوحات الصدارة شهرة هما [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-و [LMSys Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard). لاحظ أن لوحة صدارة LMSys
-تشمل أيضًا نماذج خاصة - انظر إلى عمود `licence` لتحديد النماذج مفتوحة المصدر التي يمكنك تنزيلها، ثم
-ابحث عنها على [Hugging Face Hub](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending).
-
-### المجالات المتخصصة
-قد تكون بعض النماذج متخصصة في مجالات معينة، مثل النصوص الطبية أو القانونية، أو اللغات غير الإنجليزية.
-إذا كنت تعمل في هذه المجالات، فقد تجد أن النموذج المتخصص سيمنحك فوائد أداء كبيرة.
-لا تفترض ذلك تلقائيًا! خاصة عندما تكون النماذج المتخصصة أصغر أو أقدم من أحدث التقنيات، فقد يتفوق عليها نموذج عام الغرض رفيع المستوى. لحسن الحظ، بدأنا نرى
-[لوحات الصدارة المتخصصة في المجال](https://huggingface.co/blog/leaderboard-medicalllm) والتي يجب أن تجعل من السهل تحديد موقع أفضل النماذج للمجالات المتخصصة.
-
-## ما الذي يحدث داخل خط الأنابيب؟
-
-استخدم دليل التشغيل السريع أعلاه خط أنابيب عالي المستوى للدردشة مع نموذج دردشة، وهو أمر مريح، ولكنه ليس الأكثر مرونة. دعونا نتخذ نهجًا منخفض المستوى، لكي نرى كل خطوة من الخطوات التي تنطوي عليها الدردشة. دعونا نبدأ
-بعينة من التعليمات البرمجية، ثم نقوم بتفكيكها:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-# إعداد الإدخال كما هو الحال من قبل
-chat = [
-    {"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
-    {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
-]
-
-# 1: تحميل النموذج والمحلل
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", torch_dtype=torch.bfloat16)
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-
-# 2: تطبيق قالب الدردشة
-formatted_chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
-print("Formatted chat:\n", formatted_chat)
-
-# 3: تحليل الدردشة (يمكن دمج هذه الخطوة مع الخطوة السابقة باستخدام tokenize=True)
-inputs = tokenizer(formatted_chat, return_tensors="pt", add_special_tokens=False)
-# نقل المدخلات المحللة إلى نفس الجهاز الموجود عليه النموذج (GPU/CPU)
-inputs = {key: tensor.to(model.device) for key, tensor in inputs.items()}
-print("Tokenized inputs:\n", inputs)
-
-# 4: إنشاء نص من النموذج
-outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
-print("Generated tokens:\n", outputs)
-
-# 5: فك تشفير الإخراج مرة أخرى إلى سلسلة
-decoded_output = tokenizer.decode(outputs[0][inputs['input_ids'].size(1):], skip_special_tokens=True)
-print("Decoded output:\n", decoded_output)
-```
-
-هناك الكثير هنا، ويمكن أن تكون كل قطعة وثيقة خاصة بها! بدلاً من الدخول في الكثير من التفاصيل، سأغطي
-الأفكار العامة، وأترك التفاصيل للوثائق المرتبطة بها. الخطوات الرئيسية هي:
-1. يتم تحميل [النماذج](https://huggingface.co/learn/nlp-course/en/chapter2/3) و [المُجزّئات اللغوية](https://huggingface.co/learn/nlp-course/en/chapter2/4?fw=pt) من Hugging Face Hub.
-2. يتم تنسيق الدردشة باستخدام [قالب الدردشة](https://huggingface.co/docs/transformers/main/en/chat_templating) للمحلل
-3. يتم [تحليل](https://huggingface.co/learn/nlp-course/en/chapter2/4) الدردشة المنسقة باستخدام مُجزّئ اللغوي.
-4. نقوم [بتوليد](https://huggingface.co/docs/transformers/en/llm_tutorial) استجابة من النموذج.
-5. يتم فك تشفير الرموز التي ينتجها النموذج مرة أخرى إلى سلسلة
-
-## الأداء والذاكرة والأجهزة
-
-من المحتمل أنك تعرف الآن أن معظم مهام التعلم الآلي يتم تشغيلها على وحدات معالجة الرسومات (GPU). ومع ذلك، من الممكن تمامًا
-إنشاء نص من نموذج دردشة أو نموذج لغة على وحدة المعالجة المركزية (CPU)، على الرغم من أن ذلك أبطأ إلى حد ما. إذا كان بإمكانك وضع
-النموذج في ذاكرة وحدة معالجة الرسومات (GPU)، فهذا عادة ما يكون الخيار المفضل.
-
-### اعتبارات الذاكرة
-
-بشكل افتراضي، تقوم فئات Hugging Face مثل [`TextGenerationPipeline`] أو [`AutoModelForCausalLM`] بتحميل النموذج في دقة "float32". وهذا يعني أنه يحتاج إلى 4 بايتات (32 بت) لكل معلمة، لذا فإن نموذج "8B" بحجم 8 مليار معلمة سيحتاج إلى ~32 جيجابايت من الذاكرة. ومع ذلك، يمكن أن يكون هذا مضيعة للموارد! يتم تدريب معظم نماذج اللغة الحديثة في دقة "bfloat16"، والتي تستخدم فقط 2 بايت لكل معلمة. إذا كان عتادك يدعم ذلك (Nvidia 30xx/Axxx أو أحدث)، فيمكنك تحميل النموذج في دقة "bfloat16"، باستخدام معامل "torch_dtype" كما فعلنا أعلاه.
-
-ومن الممكن أيضًا النزول إلى أقل من 16 بت باستخدام "التكميم"، وهي طريقة لضغط أوزان النموذج بطريقة تفقد بعض المعلومات. يسمح هذا بضغط كل معلمة إلى 8 بتات أو 4 بتات أو حتى أقل. لاحظ أنه، خاصة في 4 بتات، قد تتأثر جودة ناتج النموذج سلبًا، ولكن غالبًا ما يكون هذا مقايضة تستحق القيام بها لتناسب نموذج محادثة أكبر وأكثر قدرة في الذاكرة. دعنا كيف يمكننا تطبيق ذلك باستخدام مكتبة `bitsandbytes`:
-
-```python
-from transformers import AutoModelForCausalLM, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True) # يمكنك أيضًا تجربة load_in_4bit
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", quantization_config=quantization_config)
-```
-
-أو يمكننا القيام بنفس الشيء باستخدام واجهة برمجة التطبيقات "pipeline":
-
-```python
-from transformers import pipeline, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True) # يمكنك أيضًا تجربة load_in_4bit
-pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", model_kwargs={"quantization_config": quantization_config})
-```
-
-هناك عدة خيارات أخرى لكمية نماذج بخلاف `bitsandbytes` - يرجى الاطلاع على [دليل التكميم](./quantization) لمزيد من المعلومات.
-
-### اعتبارات الأداء
-
-<Tip>
-
-للحصول على دليل أكثر شمولاً حول أداء نموذج اللغة والتحسين، راجع [تحسين استدلال LLM](./llm_optims).
-
-</Tip>
-
-
-كقاعدة عامة، ستكون نماذج المحادثة الأكبر حجمًا أبطأ في توليد النصوص بالإضافة إلى احتياجها لذاكرة أكبرة. من الممكن أن تكون أكثر تحديدًا بشأن هذا: إن توليد النص من نموذج دردشة أمر غير عادي في أنه يخضع لقيود **سعة الذاكرة** بدلاً من قوة الحوسبة، لأن كل معلمة نشطة يجب قراءتها من الذاكرة لكل رمز ينشئه النموذج. وهذا يعني أن عدد الرموز في الثانية التي يمكنك توليدها من نموذج الدردشة يتناسب بشكل عام مع إجمالي حجم الذاكرة التي بوجد بها ا، مقسومًا على حجم النموذج.
-
-في مثالنا السريع أعلاه، كان حجم نموذجنا حوالي 16 جيجابايت عند تحميله في دقة "bfloat16". وهذا يعني أنه يجب قراءة 16 جيجابايت من الذاكرة لكل رمز ينشئه النموذج. يمكن أن يتراوح إجمالي سعة الذاكرة من 20-100 جيجابايت/ثانية لمعالجات المستهلكين إلى 200-900 جيجابايت/ثانية لمعالجات الرسومات للمستهلكين، ومعالجات Intel Xeon أو AMD Threadripper/Epyc أو Apple Silicon المتخصصةة، وأخيرًا يصل إلى 2-3 تيرابايت/ثانية لمعالجات مراكز البيانات مثل Nvidia A100 أو H100. يجب أن يعطيك هذا فكرة جيدة عن سرعة التوليد التي يمكنك توقعها من هذه الأنواع المختلفة من الأجهزة.
-
-لذلك، إذا كنت تريد تحسين سرعة توليد النص، فإن الحل الأسهل هو إما تقليل حجم النموذج في الذاكرة (عادةً عن طريق التكميم)، أو الحصول على عتاد بسرعة أكبر في الذاكرة. بالنسبة للمستخدمين المتقدمين، هناك عدة تقنيات أخرى للتغلب على هذه القيود. الأكثر شيوعًا هي المتغيرات على [التوليد بمساعدة](https://huggingface.co/blog/assisted-generation)، المعروف أيضًا باسم "العينات التخمينية (speculative sampling)". تحاول هذه التقنيات تخمين عدة رموز مستقبلية في وقت واحد، غالبًا باستخدام نموذج "مسودة (draft model)" أصغر، ثم تأكيد هذه التوليدات باستخدام نموذج الدردشة. إذا تم التحقق من صحة التخمينات بواسطة نموذج الدردشة، فيمكن إنشاء أكثر من رمز واحد لكل تمرير للأمام، مما يخفف بشكل كبير من القيود المتعلقة بالسعة ويحسن سرعة التوليد.
-
-أخيرًا، يجب أن نلاحظ أيضًا تأثير نماذج "مزيج الخبراء" "Mixture of Experts" (MoE) هنا. العديد من نماذج المحادثة الشهيرة، مثل Mixtral وQwen-MoE وDBRX، هي نماذج MoE. في هذه النماذج، لا تكون كل معلمة نشطة لكل رمز يتم إنشاؤه. ونتيجة لذلك، فإن نماذج MoE لديها عمومًا متطلبات ذاكرة أقل بكثير، على الرغم من أن حجمها الإجمالي يمكن أن يكون كبيرًا جدًا. لذلك يمكن أن تكون أسرع عدة مرات من نموذج "كثيف" عادي بنفس الحجم. ومع ذلك، فإن التقنيات مثل التوليد المساعد غير فعالة بشكل عام لهذه النماذج لأن المزيد من المعلمات ستصبح نشطة مع كل رمز جديد يتم التكهن به، والذي سيبطل فوائد السعة والسرعة التي توفرها بنية MoE.