Fix mask generation auto mapping for Sam/SamHQ

.circleci/config.yml

@@ -184,7 +184,6 @@ jobs:
             - 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

.circleci/create_circleci_config.py

@@ -109,9 +109,7 @@ class CircleCIJob:
                 self.docker_image[0]["image"] = f"{self.docker_image[0]['image']}:dev"
             print(f"Using {self.docker_image} docker image")
         if self.install_steps is None:
-            self.install_steps = ["uv pip install ."]
-        # Use a custom patched pytest to force exit the process at the end, to avoid `Too long with no output (exceeded 10m0s): context deadline exceeded`
-        self.install_steps.append("uv pip install git+https://github.com/ydshieh/pytest.git@8.4.1-ydshieh")
+            self.install_steps = ["uv venv && uv pip install ."]
         if self.pytest_options is None:
             self.pytest_options = {}
         if isinstance(self.tests_to_run, str):
@@ -215,7 +213,7 @@ generate_job = CircleCIJob(
     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 pip install ."],
+    install_steps=["uv venv && uv pip install ."],
     marker="generate",
     parallelism=6,
 )
@@ -232,6 +230,22 @@ processor_job = CircleCIJob(
     parallelism=8,
 )
 
+tf_job = CircleCIJob(
+    "tf",
+    docker_image=[{"image":"huggingface/transformers-tf-light"}],
+    parallelism=6,
+)
+
+
+flax_job = CircleCIJob(
+    "flax",
+    docker_image=[{"image":"huggingface/transformers-jax-light"}],
+    parallelism=6,
+    pytest_num_workers=16,
+    resource_class="2xlarge",
+)
+
+
 pipelines_torch_job = CircleCIJob(
     "pipelines_torch",
     additional_env={"RUN_PIPELINE_TESTS": True},
@@ -240,6 +254,16 @@ pipelines_torch_job = CircleCIJob(
     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},
@@ -252,16 +276,25 @@ examples_torch_job = CircleCIJob(
     additional_env={"OMP_NUM_THREADS": 8},
     docker_image=[{"image":"huggingface/transformers-examples-torch"}],
     # TODO @ArthurZucker remove this once docker is easier to build
-    install_steps=["uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
+    install_steps=["uv venv && uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
     pytest_num_workers=4,
 )
 
+
+examples_tensorflow_job = CircleCIJob(
+    "examples_tensorflow",
+    additional_env={"OMP_NUM_THREADS": 8},
+    docker_image=[{"image":"huggingface/transformers-examples-tf"}],
+    pytest_num_workers=2,
+)
+
+
 hub_job = CircleCIJob(
     "hub",
     additional_env={"HUGGINGFACE_CO_STAGING": True},
     docker_image=[{"image":"huggingface/transformers-torch-light"}],
     install_steps=[
-        'uv pip install .',
+        'uv venv && uv pip install .',
         'git config --global user.email "ci@dummy.com"',
         'git config --global user.name "ci"',
     ],
@@ -275,6 +308,7 @@ onnx_job = CircleCIJob(
     "onnx",
     docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
     install_steps=[
+        "uv venv",
         "uv pip install .[testing,sentencepiece,onnxruntime,vision,rjieba]",
     ],
     pytest_options={"k onnx": None},
@@ -304,7 +338,7 @@ non_model_job = CircleCIJob(
     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 pip install .[serving]"],
+    install_steps=["uv venv && uv pip install ."],
     marker="not generate",
     parallelism=6,
 )
@@ -322,7 +356,7 @@ doc_test_job = CircleCIJob(
     additional_env={"TRANSFORMERS_VERBOSITY": "error", "DATASETS_VERBOSITY": "error", "SKIP_CUDA_DOCTEST": "1"},
     install_steps=[
         # Add an empty file to keep the test step running correctly even no file is selected to be tested.
-        "uv pip install .",
+        "uv venv && pip install .",
         "touch dummy.py",
         command,
         "cat pr_documentation_tests_temp.txt",
@@ -334,7 +368,7 @@ 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
+REGULAR_TESTS = [torch_job, flax_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]
 REPO_UTIL_TESTS = [repo_utils_job]

.github/PULL_REQUEST_TEMPLATE.md

@@ -51,7 +51,7 @@ Library:
 - pipelines: @Rocketknight1
 - tensorflow: @gante and @Rocketknight1
 - tokenizers: @ArthurZucker
-- trainer: @zach-huggingface, @SunMarc and @qgallouedec
+- trainer: @zach-huggingface and @SunMarc
 - chat templates: @Rocketknight1
 
 Integrations:

.github/workflows/benchmark.yml

@@ -48,7 +48,7 @@ jobs:
 
       - name: Run database init script
         run: |
-          psql -f benchmark/utils/init_db.sql
+          psql -f benchmark/init_db.sql
         env:
           PGDATABASE: metrics
           PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}

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

@@ -19,7 +19,7 @@ concurrency:
 
 jobs:
   latest-docker:
-    name: "Latest PyTorch [dev]"
+    name: "Latest PyTorch + TensorFlow [dev]"
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -267,6 +267,44 @@ 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:
+      group: aws-general-8-plus
+    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:

.github/workflows/build_pr_documentation.yml

@@ -2,6 +2,15 @@ name: Build PR Documentation
 
 on:
   pull_request:
+  workflow_call:
+    inputs:
+      pr_number:
+        type: string
+        required: true
+      commit_sha:
+        type: string
+        required: true
+
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
@@ -9,9 +18,9 @@ concurrency:
 
 jobs:
   build:
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@6e2eb04a2604817c97be03786efa494fe3acae90
     with:
-      commit_sha: ${{ github.event.pull_request.head.sha }}
-      pr_number: ${{ github.event.number }}
+      commit_sha: ${{ inputs.commit_sha || github.event.pull_request.head.sha }}
+      pr_number: ${{ inputs.pr_number || github.event.number }}
       package: transformers
       languages: en

.github/workflows/check_failed_tests.yml

@@ -21,9 +21,6 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
 
 
 env:
@@ -44,7 +41,7 @@ jobs:
   check_new_failures:
     name: " "
     runs-on:
-      group: aws-g5-4xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -90,7 +87,7 @@ jobs:
       - name: Update clone
         working-directory: /transformers
         if: ${{ env.process == 'true' }}
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Get target commit
         working-directory: /transformers/utils

.github/workflows/collated-reports.yml

@@ -1,43 +0,0 @@
-name: CI collated reports
-
-on:
-  workflow_call:
-    inputs:
-      job:
-        required: true
-        type: string
-      report_repo_id:
-        required: true
-        type: string
-      machine_type:
-        required: true
-        type: string
-      gpu_name:
-        description: Name of the GPU used for the job. Its enough that the value contains the name of the GPU, e.g. "noise-h100-more-noise". Case insensitive.
-        required: true
-        type: string
-
-jobs:
-  collated_reports:
-    name: Collated reports
-    runs-on: ubuntu-22.04
-    if: always()
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/download-artifact@v4
-
-      - name: Collated reports
-        shell: bash
-        env:
-          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-          CI_SHA: ${{ github.sha }}
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-        run: |
-          pip install huggingface_hub
-          python3 utils/collated_reports.py                  \
-            --path .                                         \
-            --machine-type ${{ inputs.machine_type }}        \
-            --commit-hash ${{ env.CI_SHA }}                  \
-            --job ${{ inputs.job }}                          \
-            --report-repo-id ${{ inputs.report_repo_id }}    \
-            --gpu-name ${{ inputs.gpu_name }}

.github/workflows/doctest_job.yml

@@ -28,10 +28,10 @@ jobs:
       matrix:
         split_keys: ${{ fromJson(inputs.split_keys) }}
     runs-on: 
-      group: aws-g5-4xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       - name: Update clone
         working-directory: /transformers

.github/workflows/doctests.yml

@@ -15,10 +15,10 @@ jobs:
   setup:
     name: Setup
     runs-on: 
-      group: aws-g5-4xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     outputs:
       job_splits: ${{ steps.set-matrix.outputs.job_splits }}
       split_keys: ${{ steps.set-matrix.outputs.split_keys }}

.github/workflows/get-pr-info.yml

@@ -1,157 +0,0 @@
-name: Get PR commit SHA
-on:
-  workflow_call:
-    inputs:
-      pr_number:
-        required: true
-        type: string
-    outputs:
-      PR_HEAD_REPO_FULL_NAME:
-        description: "The full name of the repository from which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_FULL_NAME }}
-      PR_BASE_REPO_FULL_NAME:
-        description: "The full name of the repository to which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_FULL_NAME }}
-      PR_HEAD_REPO_OWNER:
-        description: "The owner of the repository from which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}
-      PR_BASE_REPO_OWNER:
-        description: "The owner of the repository to which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_OWNER }}
-      PR_HEAD_REPO_NAME:
-        description: "The name of the repository from which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}
-      PR_BASE_REPO_NAME:
-        description: "The name of the repository to which the pull request is created"
-        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_NAME }}
-      PR_HEAD_REF:
-        description: "The branch name of the pull request in the head repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REF }}
-      PR_BASE_REF:
-        description: "The branch name in the base repository (to merge into)"
-        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REF }}
-      PR_HEAD_SHA:
-        description: "The head sha of the pull request branch in the head repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_SHA }}
-      PR_BASE_SHA:
-        description: "The head sha of the target branch in the base repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_BASE_SHA }}
-      PR_MERGE_COMMIT_SHA:
-        description: "The sha of the merge commit for the pull request (created by GitHub) in the base repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_SHA }}
-      PR_HEAD_COMMIT_DATE:
-        description: "The date of the head sha of the pull request branch in the head repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_COMMIT_DATE }}
-      PR_MERGE_COMMIT_DATE:
-        description: "The date of the merge commit for the pull request (created by GitHub) in the base repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
-      PR_HEAD_COMMIT_TIMESTAMP:
-        description: "The timestamp of the head sha of the pull request branch in the head repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_COMMIT_TIMESTAMP }}
-      PR_MERGE_COMMIT_TIMESTAMP:
-        description: "The timestamp of the merge commit for the pull request (created by GitHub) in the base repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
-      PR:
-        description: "The PR"
-        value: ${{ jobs.get-pr-info.outputs.PR }}
-      PR_FILES:
-        description: "The files touched in the PR"
-        value: ${{ jobs.get-pr-info.outputs.PR_FILES }}
-
-
-jobs:
-  get-pr-info:
-    runs-on: ubuntu-22.04
-    name: Get PR commit SHA better
-    outputs:
-      PR_HEAD_REPO_FULL_NAME: ${{ steps.pr_info.outputs.head_repo_full_name }}
-      PR_BASE_REPO_FULL_NAME: ${{ steps.pr_info.outputs.base_repo_full_name }}
-      PR_HEAD_REPO_OWNER: ${{ steps.pr_info.outputs.head_repo_owner }}
-      PR_BASE_REPO_OWNER: ${{ steps.pr_info.outputs.base_repo_owner }}
-      PR_HEAD_REPO_NAME: ${{ steps.pr_info.outputs.head_repo_name }}
-      PR_BASE_REPO_NAME: ${{ steps.pr_info.outputs.base_repo_name }}
-      PR_HEAD_REF: ${{ steps.pr_info.outputs.head_ref }}
-      PR_BASE_REF: ${{ steps.pr_info.outputs.base_ref }}
-      PR_HEAD_SHA: ${{ steps.pr_info.outputs.head_sha }}
-      PR_BASE_SHA: ${{ steps.pr_info.outputs.base_sha }}
-      PR_MERGE_COMMIT_SHA: ${{ steps.pr_info.outputs.merge_commit_sha }}
-      PR_HEAD_COMMIT_DATE: ${{ steps.pr_info.outputs.head_commit_date }}
-      PR_MERGE_COMMIT_DATE: ${{ steps.pr_info.outputs.merge_commit_date }}
-      PR_HEAD_COMMIT_TIMESTAMP: ${{ steps.get_timestamps.outputs.head_commit_timestamp }}
-      PR_MERGE_COMMIT_TIMESTAMP: ${{ steps.get_timestamps.outputs.merge_commit_timestamp }}
-      PR: ${{ steps.pr_info.outputs.pr }}
-      PR_FILES: ${{ steps.pr_info.outputs.files }}
-    if: ${{ inputs.pr_number != '' }}
-    steps:
-      - name: Extract PR details
-        id: pr_info
-        uses: actions/github-script@v6
-        with:
-          script: |            
-            const { data: pr } = await github.rest.pulls.get({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              pull_number: ${{ inputs.pr_number }}
-            });
-
-            const { data: head_commit }  = await github.rest.repos.getCommit({
-              owner: pr.head.repo.owner.login,
-              repo: pr.head.repo.name,
-              ref: pr.head.ref
-            });
-
-            const { data: merge_commit }  = await github.rest.repos.getCommit({
-              owner: pr.base.repo.owner.login,
-              repo: pr.base.repo.name,
-              ref: pr.merge_commit_sha,
-            });
-
-            const { data: files } = await github.rest.pulls.listFiles({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              pull_number: ${{ inputs.pr_number }}
-            });
-
-            core.setOutput('head_repo_full_name', pr.head.repo.full_name);
-            core.setOutput('base_repo_full_name', pr.base.repo.full_name);
-            core.setOutput('head_repo_owner', pr.head.repo.owner.login);
-            core.setOutput('base_repo_owner', pr.base.repo.owner.login);
-            core.setOutput('head_repo_name', pr.head.repo.name);
-            core.setOutput('base_repo_name', pr.base.repo.name);
-            core.setOutput('head_ref', pr.head.ref);
-            core.setOutput('base_ref', pr.base.ref);
-            core.setOutput('head_sha', pr.head.sha);
-            core.setOutput('base_sha', pr.base.sha);
-            core.setOutput('merge_commit_sha', pr.merge_commit_sha);
-            core.setOutput('pr', pr);
-
-            core.setOutput('head_commit_date', head_commit.commit.committer.date);
-            core.setOutput('merge_commit_date', merge_commit.commit.committer.date);
-            
-            core.setOutput('files', files);            
-            
-            console.log('PR head commit:', {
-              head_commit: head_commit,
-              commit: head_commit.commit,
-              date: head_commit.commit.committer.date
-            });
-
-            console.log('PR merge commit:', {
-              merge_commit: merge_commit,
-              commit: merge_commit.commit,
-              date: merge_commit.commit.committer.date
-            });
-
-      - name: Convert dates to timestamps
-        id: get_timestamps
-        run: |
-          head_commit_date=${{ steps.pr_info.outputs.head_commit_date }}
-          merge_commit_date=${{ steps.pr_info.outputs.merge_commit_date }}
-          echo $head_commit_date
-          echo $merge_commit_date
-          head_commit_timestamp=$(date -d "$head_commit_date" +%s)
-          merge_commit_timestamp=$(date -d "$merge_commit_date" +%s)
-          echo $head_commit_timestamp
-          echo $merge_commit_timestamp
-          echo "head_commit_timestamp=$head_commit_timestamp" >> $GITHUB_OUTPUT
-          echo "merge_commit_timestamp=$merge_commit_timestamp" >> $GITHUB_OUTPUT

.github/workflows/get-pr-number.yml

@@ -1,36 +0,0 @@
-name: Get PR number
-on:
-  workflow_call:
-    outputs:
-      PR_NUMBER:
-        description: "The extracted PR number"
-        value: ${{ jobs.get-pr-number.outputs.PR_NUMBER }}
-
-jobs:
-  get-pr-number:
-    runs-on: ubuntu-22.04
-    name: Get PR number
-    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 }}" >> $GITHUB_ENV
-          elif [[ "${{ github.event.pull_request.number }}" != "" ]]; then
-            echo "PR_NUMBER=${{ github.event.pull_request.number }}" >> $GITHUB_ENV
-          elif [[ "${{ github.event.pull_request }}" != "" ]]; then
-            echo "PR_NUMBER=${{ github.event.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"

.github/workflows/model_jobs.yml

@@ -12,15 +12,12 @@ on:
       slice_id:
         required: true
         type: number
-      runner_map:
-        required: false
+      runner:
+        required: true
         type: string
       docker:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
       report_name_prefix:
         required: false
         default: run_models_gpu
@@ -48,7 +45,7 @@ jobs:
       matrix:
         folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
     runs-on:
-      group: ${{ fromJson(inputs.runner_map)[matrix.folders][inputs.machine_type] }}
+      group: '${{ inputs.machine_type }}'
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -73,7 +70,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -110,9 +107,9 @@ jobs:
         run: |
           echo "${{ inputs.machine_type }}"
 
-          if [ "${{ inputs.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ inputs.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ inputs.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ inputs.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ inputs.machine_type }}

.github/workflows/model_jobs_amd.yml

@@ -0,0 +1,128 @@
+name: model jobs
+
+on:
+  workflow_call:
+    inputs:
+      folder_slices:
+        required: true
+        type: string
+      machine_type:
+        required: true
+        type: string
+      slice_id:
+        required: true
+        type: number
+      runner:
+        required: true
+        type: string
+      docker:
+        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:
+  run_models_gpu:
+    name: " "
+    strategy:
+      max-parallel: 1  # For now, not to parallelize. Can change later if it works well.
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
+    runs-on: ['${{ inputs.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}']
+    container:
+      image: ${{ inputs.docker }}
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Echo input and matrix info
+        shell: bash
+        run: |
+          echo "${{ inputs.folder_slices }}"
+          echo "${{ matrix.folders }}"
+          echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}"
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ 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: Update / Install some packages (for Past CI)
+        if: ${{ contains(inputs.docker, '-past-') }}
+        working-directory: /transformers
+        run: |
+          python3 -m pip install -U datasets
+
+      - name: Update / Install some packages (for Past CI)
+        if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }}
+        working-directory: /transformers
+        run: |
+          python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: python3 -m pytest -rsfE -v --make-reports=${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}  -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: Run test
+        shell: bash
+        run: |
+          mkdir -p /transformers/reports/${{ 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: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          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/pr-style-bot.yml

@@ -6,13 +6,29 @@ on:
     types: [created]
 
 permissions:
+  contents: write
   pull-requests: write
 
 jobs:
   style:
-    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
+    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@639ee721e149a281fe726a50a2cc1354b48bc463
     with:
       python_quality_dependencies: "[quality]"
       style_command_type: "default"
     secrets:
-      bot_token: ${{ secrets.HF_STYLE_BOT_ACTION }}
+      bot_token: ${{ secrets.GITHUB_TOKEN }}
+
+  check-outputs:
+    runs-on: ubuntu-latest
+    needs: style
+    steps:
+      - run: echo ${{ needs.style.outputs.pr_number }}
+      - run: echo ${{ needs.style.outputs.new_commit_sha }}
+
+  trigger:
+    needs: style
+    if: needs.style.outputs.new_commit_sha != ''
+    uses: "./.github/workflows/build_pr_documentation.yml"
+    with:
+      pr_number: ${{ needs.style.outputs.pr_number }}
+      commit_sha: ${{ needs.style.outputs.new_commit_sha }}

.github/workflows/pr_build_doc_with_comment.yml

@@ -1,134 +0,0 @@
-name: PR - build doc via comment
-on:
-  issue_comment:
-    types:
-      - created
-    branches-ignore:
-      - main
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.issue.number }}-${{ startsWith(github.event.comment.body, 'build-doc') }}
-  cancel-in-progress: true
-permissions: {}
-
-
-jobs:
-  get-pr-number:
-    name: Get PR number
-    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", "stevhliu", "ebezzam"]'), github.actor) && (startsWith(github.event.comment.body, 'build-doc')) }}
-    uses: ./.github/workflows/get-pr-number.yml
-
-  get-pr-info:
-    name: Get PR commit SHA
-    needs: get-pr-number
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    uses: ./.github/workflows/get-pr-info.yml
-    with:
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-
-  verity_pr_commit:
-    name: Verity PR commit corresponds to a specific event by comparing timestamps
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    runs-on: ubuntu-22.04
-    needs: get-pr-info
-    env:
-      COMMENT_DATE: ${{ github.event.comment.created_at }}
-      PR_MERGE_COMMIT_DATE: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
-      PR_MERGE_COMMIT_TIMESTAMP: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
-    steps:
-      - run: |
-          COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
-          echo "COMMENT_DATE: $COMMENT_DATE"
-          echo "PR_MERGE_COMMIT_DATE: $PR_MERGE_COMMIT_DATE"
-          echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
-          echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_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
-
-  create_run:
-    name: Create run
-    needs: [get-pr-number, get-pr-info]
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != '' }}
-    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/${{ needs.get-pr-info.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Custom doc building job" -f "context=custom-doc-build"
-
-  reply_to_comment:
-    name: Reply to the comment
-    if: ${{ needs.create_run.result == 'success' }}
-    needs: [get-pr-number, create_run]
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Reply to the comment
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GITHUB_RUN_URL: https://github.com/${{ 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 }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f "body=[Building docs for all languages...](${{ env.GITHUB_RUN_URL }})"
-
-  build-doc:
-    name: Build doc
-    needs: [get-pr-number, get-pr-info]
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != '' }}
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-      package: transformers
-      languages: ar de en es fr hi it ko pt tr zh ja te
-
-  update_run_status:
-    name: Update Check Run Status
-    needs: [ get-pr-info, create_run, build-doc ]
-    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: ${{ contains(fromJSON('["skipped", "success"]'), needs.create_run.result) }}
-    steps:
-      - name: Get `build-doc` job status
-        run: |
-          echo "${{ needs.build-doc.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.build-doc.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/${{ needs.get-pr-info.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=${{ env.STATUS }}" -f "description=Custom doc building job" -f "context=custom-doc-build"

.github/workflows/pr_run_slow_ci.yml

@@ -1,177 +0,0 @@
-name: PR slow CI
-on:
-  pull_request_target:
-    types: [opened, synchronize, reopened]
-
-jobs:
-  get-pr-number:
-    name: Get PR number
-    uses: ./.github/workflows/get-pr-number.yml
-
-  get-pr-info:
-    name: Get PR commit SHA
-    needs: get-pr-number
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    uses: ./.github/workflows/get-pr-info.yml
-    with:
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-
-  get-jobs:
-    name: Get test files to run
-    runs-on: ubuntu-22.04
-    needs: [get-pr-number, get-pr-info]
-    outputs:
-      jobs: ${{ steps.get_jobs.outputs.jobs_to_run }}
-    steps:
-      - name: Get repository content
-        id: repo_content
-        uses: actions/github-script@v6
-        with:
-          script: |
-            const { data: tests_dir } = await github.rest.repos.getContent({
-              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
-              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
-              path: 'tests',
-              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
-            });
-
-            const { data: tests_models_dir } = await github.rest.repos.getContent({
-              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
-              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
-              path: 'tests/models',
-              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
-            });
-
-            const { data: tests_quantization_dir } = await github.rest.repos.getContent({
-              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
-              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
-              path: 'tests/quantization',
-              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
-            });
-
-            core.setOutput('tests_dir', tests_dir);
-            core.setOutput('tests_models_dir', tests_models_dir);
-            core.setOutput('tests_quantization_dir', tests_quantization_dir);
-
-      # This checkout to the main branch
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: "0"
-
-      - name: Write pr_files file
-        run: |
-          cat > pr_files.txt << 'EOF'
-          ${{ needs.get-pr-info.outputs.PR_FILES }}
-          EOF
-
-      - name: Write tests_dir file
-        run: |
-          cat > tests_dir.txt << 'EOF'
-          ${{ steps.repo_content.outputs.tests_dir }}
-          EOF
-
-      - name: Write tests_models_dir file
-        run: |
-          cat > tests_models_dir.txt << 'EOF'
-          ${{ steps.repo_content.outputs.tests_models_dir }}
-          EOF
-
-      - name: Write tests_quantization_dir file
-        run: |
-          cat > tests_quantization_dir.txt << 'EOF'
-          ${{ steps.repo_content.outputs.tests_quantization_dir }}
-          EOF
-
-      - name: Run script to get jobs to run
-        id: get_jobs
-        run: |
-          python utils/get_pr_run_slow_jobs.py | tee output.txt
-          echo "jobs_to_run: $(tail -n 1 output.txt)"
-          echo "jobs_to_run=$(tail -n 1 output.txt)" >> $GITHUB_OUTPUT
-
-  send_comment:
-    # Will delete the previous comment and send a new one if:
-    #   - either the content is changed
-    #   - or the previous comment is 30 minutes or more old
-    name: Send a comment to suggest jobs to run
-    if: ${{ needs.get-jobs.outputs.jobs != '' }}
-    needs: [get-pr-number, get-jobs]
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check and update comment if needed
-        uses: actions/github-script@v7
-        env:
-          BODY: "\n\nrun-slow: ${{ needs.get-jobs.outputs.jobs }}"
-        with:
-          script: |
-            const prNumber = ${{ needs.get-pr-number.outputs.PR_NUMBER }};
-            const commentPrefix = "**[For maintainers]** Suggested jobs to run (before merge)";
-            const thirtyMinutesAgo = new Date(Date.now() - 30 * 60 * 1000); // 30 minutes ago
-            const newBody = `${commentPrefix}${process.env.BODY}`;
-            
-            // Get all comments on the PR
-            const { data: comments } = await github.rest.issues.listComments({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: prNumber
-            });
-            
-            // Find existing comments that start with our prefix
-            const existingComments = comments.filter(comment => 
-              comment.user.login === 'github-actions[bot]' && 
-              comment.body.startsWith(commentPrefix)
-            );
-            
-            let shouldCreateNewComment = true;
-            let commentsToDelete = [];
-            
-            if (existingComments.length > 0) {
-              // Get the most recent comment
-              const mostRecentComment = existingComments
-                .sort((a, b) => new Date(b.created_at) - new Date(a.created_at))[0];
-              
-              const commentDate = new Date(mostRecentComment.created_at);
-              const isOld = commentDate < thirtyMinutesAgo;
-              const isDifferentContent = mostRecentComment.body !== newBody;
-              
-              console.log(`Most recent comment created: ${mostRecentComment.created_at}`);
-              console.log(`Is older than 30 minutes: ${isOld}`);
-              console.log(`Has different content: ${isDifferentContent}`);
-              
-              if (isOld || isDifferentContent) {
-                // Delete all existing comments and create new one
-                commentsToDelete = existingComments;
-                console.log(`Will delete ${commentsToDelete.length} existing comment(s) and create new one`);
-              } else {
-                // Content is same and comment is recent, skip
-                shouldCreateNewComment = false;
-                console.log('Comment is recent and content unchanged, skipping update');
-              }
-            } else {
-              console.log('No existing comments found, will create new one');
-            }
-            
-            // Delete old comments if needed
-            for (const comment of commentsToDelete) {
-              console.log(`Deleting comment #${comment.id} (created: ${comment.created_at})`);
-              await github.rest.issues.deleteComment({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                comment_id: comment.id
-              });
-            }
-            
-            // Create new comment if needed
-            if (shouldCreateNewComment) {
-              await github.rest.issues.createComment({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: prNumber,
-                body: newBody
-              });
-              console.log('✅ New comment created');
-            } else {
-              console.log('ℹ️ No comment update needed');
-            }

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

@@ -4,6 +4,17 @@ on:
   push:
     branches: [ main ]
 
+env:
+  OUTPUT_SLACK_CHANNEL_ID: "C06L2SGMEEA"
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  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`.
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+
 jobs:
   get_modified_models:
     name: "Get all modified files"
@@ -14,144 +25,111 @@ jobs:
       - name: Check out code
         uses: actions/checkout@v4
 
-      - name: Get changed files using `actions/github-script`
-        id: get-changed-files
-        uses: actions/github-script@v7
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
         with:
-          script: |
-            let files = [];
-            
-            // Only handle push events
-            if (context.eventName === 'push') {
-              const afterSha = context.payload.after;
-              const branchName = context.payload.ref.replace('refs/heads/', '');
-              
-              let baseSha;
-              
-              if (branchName === 'main') {
-                console.log('Push to main branch, comparing to parent commit');
-                // Get the parent commit of the pushed commit
-                const { data: commit } = await github.rest.repos.getCommit({
-                  owner: context.repo.owner,
-                  repo: context.repo.repo,
-                  ref: afterSha
-                });
-                baseSha = commit.parents[0]?.sha;
-                if (!baseSha) {
-                  throw new Error('No parent commit found for the pushed commit');
-                }
-              } else {
-                console.log(`Push to branch ${branchName}, comparing to main`);
-                baseSha = 'main';
-              }
-              
-              const { data: comparison } = await github.rest.repos.compareCommits({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                base: baseSha,
-                head: afterSha
-              });
-              
-              // Include added, modified, and renamed files
-              files = comparison.files
-                .filter(file => file.status === 'added' || file.status === 'modified' || file.status === 'renamed')
-                .map(file => file.filename);
-            }
-            
-            // Include all files under src/transformers/ (not just models subdirectory)
-            const filteredFiles = files.filter(file => 
-              file.startsWith('src/transformers/')
-            );
-            
-            core.setOutput('changed_files', filteredFiles.join(' '));
-            core.setOutput('any_changed', filteredFiles.length > 0 ? 'true' : 'false');
+          files: src/transformers/models/**
 
-      - name: Parse changed files with Python
-        if: steps.get-changed-files.outputs.any_changed == 'true'
-        env:
-          CHANGED_FILES: ${{ steps.get-changed-files.outputs.changed_files }}
+      - name: Run step if only the files listed above change
+        if: steps.changed-files.outputs.any_changed == 'true'
         id: set-matrix
+        env:
+          ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
         run: |
-          python3 - << 'EOF'
-          import os
-          import sys
-          import json
-          
-          # Add the utils directory to Python path
-          sys.path.insert(0, 'utils')
-          
-          # Import the important models list
-          from important_files import IMPORTANT_MODELS
-          
-          print(f"Important models: {IMPORTANT_MODELS}")
-          
-          # Get the changed files from the previous step
-          changed_files_str = os.environ.get('CHANGED_FILES', '')
-          changed_files = changed_files_str.split() if changed_files_str else []
-          
-          # Filter to only Python files
-          python_files = [f for f in changed_files if f.endswith('.py')]
-          print(f"Python files changed: {python_files}")
-          
-          result_models = set()
-          
-          # Specific files that trigger all models
-          transformers_utils_files = [
-              'modeling_utils.py',
-              'modeling_rope_utils.py', 
-              'modeling_flash_attention_utils.py',
-              'modeling_attn_mask_utils.py',
-              'cache_utils.py',
-              'masking_utils.py',
-              'pytorch_utils.py'
-          ]
-          
-          # Single loop through all Python files
-          for file in python_files:
-              # Check for files under src/transformers/models/
-              if file.startswith('src/transformers/models/'):
-                  remaining_path = file[len('src/transformers/models/'):]
-                  if '/' in remaining_path:
-                      model_dir = remaining_path.split('/')[0]
-                      if model_dir in IMPORTANT_MODELS:
-                          result_models.add(model_dir)
-                          print(f"Added model directory: {model_dir}")
-              
-              # Check for specific files under src/transformers/ or src/transformers/generation/ files
-              elif file.startswith('src/transformers/generation/') or \
-                   (file.startswith('src/transformers/') and os.path.basename(file) in transformers_utils_files):
-                  print(f"Found core file: {file} - including all important models")
-                  result_models.update(IMPORTANT_MODELS)
-                  break  # No need to continue once we include all models
-          
-          # Convert to sorted list and create matrix
-          result_list = sorted(list(result_models))
-          print(f"Final model list: {result_list}")
-          
-          if result_list:
-              matrix_json = json.dumps(result_list)
-              print(f"matrix={matrix_json}")
-              
-              # Write to GITHUB_OUTPUT
-              with open(os.environ['GITHUB_OUTPUT'], 'a') as f:
-                  f.write(f"matrix={matrix_json}\n")
-          else:
-              print("matrix=[]")
-              with open(os.environ['GITHUB_OUTPUT'], 'a') as f:
-                  f.write("matrix=[]\n")
-          EOF
-
-  model-ci:
-    name: Model CI
-    uses: ./.github/workflows/self-scheduled.yml
+            model_arrays=()
+            for file in $ALL_CHANGED_FILES; do
+                model_path="${file#*models/}"
+                model_path="models/${model_path%%/*}"
+                if grep -qFx "$model_path" utils/important_models.txt; then
+                    # Append the file to the matrix string
+                    model_arrays+=("$model_path")
+                fi
+            done
+            matrix_string=$(printf '"%s", ' "${model_arrays[@]}" | sed 's/, $//')
+            echo "matrix=[$matrix_string]" >> $GITHUB_OUTPUT
+  test_modified_files:
     needs: get_modified_models
-    if: needs.get_modified_models.outputs.matrix != '' && needs.get_modified_models.outputs.matrix != '[]'
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#transformers-ci-push"
-      docker: huggingface/transformers-all-latest-gpu
-      ci_event: push
-      report_repo_id: hf-internal-testing/transformers_ci_push
-      commit_sha: ${{ github.sha }}
-      models: ${{ needs.get_modified_models.outputs.matrix }}
-    secrets: inherit
+    name: Slow & FA2 tests
+    runs-on:
+      group: aws-g5-4xlarge-cache
+    container:
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    if: ${{ needs.get_modified_models.outputs.matrix != '[]' && needs.get_modified_models.outputs.matrix != '' && fromJson(needs.get_modified_models.outputs.matrix)[0] != null }}
+    strategy:
+      fail-fast: false
+      matrix:
+        model-name: ${{ fromJson(needs.get_modified_models.outputs.matrix) }}
+
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Install locally transformers & other libs
+        run: |
+          apt install sudo
+          sudo -H pip install --upgrade pip
+          sudo -H pip uninstall -y transformers
+          sudo -H pip install -U -e ".[testing]"
+          MAX_JOBS=4 pip install flash-attn --no-build-isolation
+          pip install bitsandbytes
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Show installed libraries and their versions
+        run: pip freeze
+
+      - name: Run FA2 tests
+        id: run_fa2_tests
+        run:
+          pytest -rsfE -m "flash_attn_test" --make-reports=${{ matrix.model-name }}_fa2_tests/ tests/${{ matrix.model-name }}/test_modeling_*
+
+      - name: "Test suite reports artifacts: ${{ matrix.model-name }}_fa2_tests"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.model-name }}_fa2_tests
+          path: /transformers/reports/${{ matrix.model-name }}_fa2_tests
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }}
+          title: 🤗 Results of the FA2 tests - ${{ matrix.model-name }}
+          status: ${{ steps.run_fa2_tests.conclusion}}
+          slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+
+      - name: Run integration tests
+        id: run_integration_tests
+        if: always()
+        run:
+          pytest -rsfE -k "IntegrationTest"  --make-reports=tests_integration_${{ matrix.model-name }} tests/${{ matrix.model-name }}/test_modeling_*
+
+      - name: "Test suite reports artifacts: tests_integration_${{ matrix.model-name }}"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: tests_integration_${{ matrix.model-name }}
+          path: /transformers/reports/tests_integration_${{ matrix.model-name }}
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }}
+          title: 🤗 Results of the Integration tests - ${{ matrix.model-name }}
+          status: ${{ steps.run_integration_tests.conclusion}}
+          slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+
+      - name: Tailscale # In order to be able to SSH when a test fails
+        if: ${{ runner.debug == '1'}}
+        uses: huggingface/tailscale-action@v1
+        with:
+          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
+          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+          waitForSSH: true

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

@@ -29,7 +29,7 @@ jobs:
     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", "stevhliu", "ebezzam"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
+    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "vasqu"]'), 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:
@@ -185,7 +185,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
        group: '${{ matrix.machine_type }}'
     container:
@@ -239,9 +239,9 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -292,7 +292,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -338,9 +338,9 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}

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

@@ -1,54 +1,43 @@
-name: Nvidia CI with nightly torch
+name: Self-hosted runner (nightly-ci)
+
 
 on:
   repository_dispatch:
-  # triggered when the daily scheduled Nvidia CI is completed.
-  # This way, we can compare the results more easily.
-  workflow_run:
-    workflows: ["Nvidia CI"]
-    branches: ["main"]
-    types: [completed]
+  schedule:
+    - cron: "17 2 * * *"
   push:
     branches:
-      - run_ci_with_nightly_torch*
-
-# Used for `push` to easily modify the target workflow runs to compare against
-env:
-    prev_workflow_run_id: ""
-    other_workflow_run_id: ""
-
+      - run_nightly_ci*
 
 jobs:
-  build_nightly_torch_ci_images:
-    name: Build CI Docker Images with nightly torch
+  build_nightly_ci_images:
+    name: Build Nightly CI Docker Images
+    if: (github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_nightly_ci'))
     uses: ./.github/workflows/build-nightly-ci-docker-images.yml
     secrets: inherit
 
-  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
-    needs: build_nightly_torch_ci_images
+    needs: [build_nightly_ci_images]
     uses: ./.github/workflows/self-scheduled.yml
     with:
       job: run_models_gpu
       slack_report_channel: "#transformers-ci-past-future"
+      runner: ci
       docker: huggingface/transformers-all-latest-torch-nightly-gpu
       ci_event: Nightly CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci_with_torch_nightly
-      commit_sha: ${{ github.event.workflow_run.head_sha || github.sha }}
+    secrets: inherit
+
+  deepspeed-ci:
+    name: DeepSpeed CI
+    needs: [build_nightly_ci_images]
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_torch_cuda_extensions_gpu
+      slack_report_channel: "#transformers-ci-past-future"
+      runner: ci
+      # test deepspeed nightly build with the latest release torch
+      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
+      ci_event: Nightly CI
+      working-directory-prefix: /workspace
     secrets: inherit

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

@@ -0,0 +1,25 @@
+name: Self-hosted runner (AMD mi300 CI caller)
+
+on:
+  #workflow_run:
+  #  workflows: ["Self-hosted runner (push-caller)"]
+  #  branches: ["main"]
+  #  types: [completed]
+  push:
+    branches:
+      - run_amd_push_ci_caller*
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+
+jobs:
+  run_amd_ci:
+    name: AMD mi300
+    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-push-amd.yml
+    with:
+      gpu_flavor: mi300
+    secrets: inherit

.github/workflows/self-push.yml

@@ -31,12 +31,12 @@ jobs:
     name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     outputs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
       test_map: ${{ steps.set-matrix.outputs.test_map }}
@@ -131,12 +131,12 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g5-4xlarge-cache]
+        machine_type: [aws-g4dn-2xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     env:
       # For the meaning of these environment variables, see the job `Setup`
       CI_BRANCH_PUSH: ${{ github.event.ref }}
@@ -169,9 +169,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -244,7 +244,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -282,9 +282,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -357,12 +357,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache]
+        machine_type: [aws-g4dn-2xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     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/
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     env:
       # For the meaning of these environment variables, see the job `Setup`
       CI_BRANCH_PUSH: ${{ github.event.ref }}
@@ -395,9 +395,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -467,7 +467,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -505,9 +505,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}

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

@@ -1,8 +1,8 @@
-name: Self-hosted runner scale set (AMD mi355 scheduled CI caller)
+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 : amd-mi355-ci-1gpu
-#              2gpu : amd-mi355-ci-2gpu
+# For example, 1gpu scale set: amd-mi300-ci-1gpu
+#              2gpu scale set: amd-mi300-ci-2gpu
 
 on:
   workflow_run:
@@ -20,9 +20,9 @@ jobs:
     with:
       job: run_models_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi355-ci
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
+      ci_event: Scheduled CI (AMD) - mi300
       report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
@@ -32,9 +32,9 @@ jobs:
     with:
       job: run_pipelines_torch_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi355-ci
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
+      ci_event: Scheduled CI (AMD) - mi300
       report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
@@ -44,9 +44,9 @@ jobs:
     with:
       job: run_examples_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi355-ci
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
+      ci_event: Scheduled CI (AMD) - mi300
       report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
@@ -56,8 +56,8 @@ jobs:
     with:
       job: run_torch_cuda_extensions_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi355-ci
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
+      ci_event: Scheduled CI (AMD) - mi300
       report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit

.github/workflows/self-scheduled-amd-mi325-caller.yml

@@ -1,67 +0,0 @@
-name: Self-hosted runner scale set (AMD mi325 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-mi325-ci-1gpu
-#              2gpu scale set: amd-mi325-ci-2gpu
-
-on:
-  workflow_run:
-    workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
-    branches: ["main"]
-    types: [completed]
-  push:
-    branches:
-      - 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
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#amd-hf-ci"
-      runner_scale_set: amd-mi325-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi325
-      report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
-    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-mi325-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi325
-      report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
-    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-mi325-ci
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi325
-      report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
-    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-mi325-ci
-      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi325
-      report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
-    secrets: inherit

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

@@ -1,4 +1,5 @@
-name: Nvidia CI
+name: Self-hosted runner (scheduled)
+
 
 on:
   repository_dispatch:
@@ -6,7 +7,7 @@ on:
     - cron: "17 2 * * *"
   push:
     branches:
-      - run_nvidia_ci*
+      - run_scheduled_ci*
   workflow_dispatch:
     inputs:
       prev_workflow_run_id:
@@ -21,7 +22,7 @@ on:
         default: ""
 
 
-# Used for `push` to easily modify the target workflow runs to compare against
+# Used for `push` to easily modiffy the target workflow runs to compare against
 env:
     prev_workflow_run_id: ""
     other_workflow_run_id: ""
@@ -50,10 +51,10 @@ jobs:
     with:
       job: run_models_gpu
       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
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   torch-pipeline:
@@ -62,10 +63,22 @@ jobs:
     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
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
+    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
+      report_repo_id: hf-internal-testing/transformers_daily_ci
     secrets: inherit
 
   example-ci:
@@ -74,10 +87,10 @@ jobs:
     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
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   trainer-fsdp-ci:
@@ -86,10 +99,10 @@ jobs:
     with:
       job: run_trainer_and_fsdp_gpu
       slack_report_channel: "#transformers-ci-daily-training"
+      runner: daily-ci
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   deepspeed-ci:
@@ -98,11 +111,11 @@ jobs:
     with:
       job: run_torch_cuda_extensions_gpu
       slack_report_channel: "#transformers-ci-daily-training"
+      runner: daily-ci
       docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
       ci_event: Daily CI
       working-directory-prefix: /workspace
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   quantization-ci:
@@ -111,8 +124,8 @@ jobs:
     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
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit

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

@@ -1,342 +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 }}
-    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_torch_gpu:
-    if: ${{ inputs.job == 'run_pipelines_torch_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_torch_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_torch_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_pipelines_torch_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_torch_cuda_extensions_gpu:
-    if: ${{ inputs.job == 'run_torch_cuda_extensions_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_torch_cuda_extensions_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_torch_cuda_extensions_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
-
-  send_results:
-    name: Slack Report
-    needs:
-      [
-        setup,
-        run_models_gpu,
-        run_examples_gpu,
-        run_torch_cuda_extensions_gpu,
-        run_pipelines_torch_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_torch_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_torch_cuda_extensions_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

@@ -1,4 +1,4 @@
-name: Nvidia CI (job definitions)
+name: Self-hosted runner (scheduled)
 
 # Note that each job's dependencies go into a corresponding docker file.
 #
@@ -15,6 +15,9 @@ on:
       slack_report_channel:
         required: true
         type: string
+      runner:
+        required: true
+        type: string
       docker:
         required: true
         type: string
@@ -28,13 +31,7 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
-      models:
-        default: ""
-        required: false
-        type: string
+
 
 env:
   HF_HOME: /mnt/cache
@@ -52,26 +49,25 @@ env:
 
 jobs:
   setup:
-    name: Setup
     if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu", "run_quantization_torch_gpu"]'), inputs.job)
+    name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-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/
+      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
         working-directory: /transformers
         run: |
-          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+          git fetch && git checkout ${{ github.sha }}
 
       - name: Cleanup
         working-directory: /transformers
@@ -90,9 +86,8 @@ jobs:
         working-directory: /transformers/tests
         run: |
           if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
-            echo "folder_slices=$(python3 ../utils/split_model_tests.py --models '${{ inputs.models }}' --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
+            echo "folder_slices=$(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
@@ -116,16 +111,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [single-gpu, multi-gpu]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
         slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
     uses: ./.github/workflows/model_jobs.yml
     with:
       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 }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
     secrets: inherit
 
   run_trainer_and_fsdp_gpu:
@@ -135,16 +129,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-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 }}
-      runner_map: ${{ needs.setup.outputs.runner_map }}
+      runner: ${{ inputs.runner }}
       docker: ${{ inputs.docker }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
       report_name_prefix: run_trainer_and_fsdp_gpu
     secrets: inherit
 
@@ -154,7 +147,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -163,7 +156,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -188,9 +181,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -216,22 +209,23 @@ jobs:
           name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
           path: /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
 
-  run_examples_gpu:
-    if: ${{ inputs.job == 'run_examples_gpu' }}
-    name: Examples directory
+  run_pipelines_tf_gpu:
+    if: ${{ inputs.job == 'run_pipelines_tf_gpu' }}
+    name: TensorFlow pipelines
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
-      image: huggingface/transformers-all-latest-gpu
+      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 ${{ inputs.commit_sha || github.sha }}
+        run: |
+          git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -256,9 +250,77 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-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_tf_gpu_test_reports tests/pipelines
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: |
+          cat /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports
+          path: /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports
+
+  run_examples_gpu:
+    if: ${{ inputs.job == 'run_examples_gpu' }}
+    name: Examples directory
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [aws-g4dn-4xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Update clone
+        working-directory: /transformers
+        run: git fetch && git checkout ${{ github.sha }}
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -291,7 +353,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -300,7 +362,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
@@ -353,9 +415,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -390,7 +452,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -408,7 +470,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -433,9 +495,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -472,7 +534,6 @@ jobs:
         uses: actions/checkout@v4
         with:
           fetch-depth: 2
-          ref: ${{ inputs.commit_sha || github.sha }}
 
       - name: Install transformers
         run: pip install transformers
@@ -510,12 +571,13 @@ jobs:
       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,
       run_extract_warnings
     ]
-    if: always() && !cancelled()
+    if: ${{ always() }}
     uses: ./.github/workflows/slack-report.yml
     with:
       job: ${{ inputs.job }}
@@ -527,7 +589,6 @@ jobs:
       quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }}
       ci_event: ${{ inputs.ci_event }}
       report_repo_id: ${{ inputs.report_repo_id }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
 
     secrets: inherit
 
@@ -538,7 +599,7 @@ jobs:
     uses: ./.github/workflows/check_failed_tests.yml
     with:
       docker: ${{ inputs.docker }}
-      start_sha: ${{ inputs.commit_sha || github.sha }}
+      start_sha: ${{ github.sha }}
       job: ${{ inputs.job }}
       slack_report_channel: ${{ inputs.slack_report_channel }}
       ci_event: ${{ inputs.ci_event }}

.github/workflows/slack-report.yml

@@ -24,10 +24,6 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
-
 
 env:
   TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
@@ -36,7 +32,7 @@ jobs:
   send_results:
     name: Send results to webhook
     runs-on: ubuntu-22.04
-    if: always() && !cancelled()
+    if: always()
     steps:
       - name: Preliminary job status
         shell: bash
@@ -45,10 +41,6 @@ jobs:
           echo "Setup status: ${{ inputs.setup_status }}"
 
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 2
-          ref: ${{ inputs.commit_sha || github.sha }}
-
       - uses: actions/download-artifact@v4
 
       - name: Prepare some setup values
@@ -75,9 +67,7 @@ jobs:
           SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }}
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           CI_EVENT: ${{ inputs.ci_event }}
-          # This `CI_TITLE` would be empty for `schedule` or `workflow_run` events.
-          CI_TITLE: ${{ github.event.head_commit.message }}
-          CI_SHA: ${{ inputs.commit_sha || github.sha }}
+          CI_SHA: ${{ github.sha }}
           CI_TEST_JOB: ${{ inputs.job }}
           SETUP_STATUS: ${{ inputs.setup_status }}
           REPORT_REPO_ID: ${{ inputs.report_repo_id }}
@@ -93,7 +83,7 @@ jobs:
             python utils/notification_service.py "${{ inputs.quantization_matrix }}"
           else
             python utils/notification_service.py "${{ inputs.folder_slices }}"
-          fi
+          fi          
 
       # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
       - name: Failure table artifacts

.gitignore

@@ -167,6 +167,3 @@ tags
 
 # ruff
 .ruff_cache
-
-# modular conversion
-*.modular_backup

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

@@ -68,7 +68,8 @@ already reported** (use the search bar on GitHub under Issues). Your issue shoul
 
 Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:
 
-* Your **OS type and version** and **Python**, and **PyTorch** versions when applicable.
+* Your **OS type and version** and **Python**, **PyTorch** and
+  **TensorFlow** versions when applicable.
 * A short, self-contained, code snippet that allows us to reproduce the bug in
   less than 30s.
 * The *full* traceback if an exception is raised.
@@ -164,7 +165,8 @@ You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main
    mode with the `-e` flag.
 
    Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
-   failure with this command. If that's the case make sure to install Pytorch then do:
+   failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
+   (PyTorch, TensorFlow and/or Flax) then do:
 
    ```bash
    pip install -e ".[quality]"

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; \
+	$(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 \
-		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); \
-		else \
-			echo "No library .py files were modified"; \
-		fi; \
+		echo "No library .py files were modified"; \
 	fi
 
 # Update src/transformers/dependency_versions_table.py
@@ -46,13 +40,11 @@ repo-consistency:
 	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/add_dates.py
 
 # this target runs checks on all files
 
@@ -89,7 +81,6 @@ fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
 	python utils/check_modular_conversion.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

@@ -44,7 +44,7 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ja.md">日本語</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_hd.md">हिन्दी</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ru.md">Русский</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Português</a> |
+        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Рortuguês</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_te.md">తెలుగు</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_fr.md">Français</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_de.md">Deutsch</a> |
@@ -59,22 +59,12 @@ limitations under the License.
 </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 is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities.
 
-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. 
-
-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`.
-
-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.
-
-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.
+There are over 500K+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
 
 Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
 
@@ -147,7 +137,7 @@ chat = [
     {"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", dtype=torch.bfloat16, device_map="auto")
+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"])
 ```
@@ -242,7 +232,7 @@ pipeline(
 
 - 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 [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.
 
 ## 100 projects using Transformers
 
@@ -280,8 +270,8 @@ Expand each modality below to see a few example models for various use cases.
 - 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 [SuperPoint](https://huggingface.co/magic-leap-community/superpoint)
-- Keypoint matching with [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor)
+- 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)

SECURITY.md

@@ -14,7 +14,7 @@ Models uploaded on the Hugging Face Hub come in different formats. We heavily re
 models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized
 by the transformers library), as developed specifically to prevent arbitrary code execution on your system.
 
-To avoid loading models from unsafe formats (e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
+To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
 
 ### Remote code
 

awesome-transformers.md

@@ -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
@@ -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/.gitignore

@@ -1 +0,0 @@
-benchmark_results/

benchmark/benches/llama.py

@@ -1,345 +0,0 @@
-# Copyright 2025 The HuggingFace 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.
-from logging import Logger
-import os
-from threading import Event, Thread
-from time import perf_counter, sleep
-from typing import Optional
-import sys
-
-# Add the parent directory to Python path to import benchmarks_entrypoint
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from benchmarks_entrypoint import MetricsRecorder
-
-import gpustat
-import psutil
-import psycopg2
-
-# Optional heavy ML dependencies - only required when actually running the benchmark
-try:
-    import torch
-    from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
-    TRANSFORMERS_AVAILABLE = True
-except ImportError:
-    TRANSFORMERS_AVAILABLE = False
-    torch = None
-    AutoModelForCausalLM = None
-    AutoTokenizer = None
-    GenerationConfig = None
-    StaticCache = None
-
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-os.environ["TOKENIZERS_PARALLELISM"] = "1"
-
-# Only set torch precision if torch is available
-if TRANSFORMERS_AVAILABLE:
-    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, metrics_recorder=None, num_tokens_to_generate=100
-):
-    # Check if required ML dependencies are available
-    if not TRANSFORMERS_AVAILABLE:
-        logger.error("Transformers and torch are required to run the LLaMA benchmark. Please install them with:")
-        logger.error("pip install torch transformers")
-        logger.error("Skipping LLaMA benchmark due to missing dependencies.")
-        return
-    
-    continue_metric_collection = Event()
-    metrics_thread = None
-    model_id = "meta-llama/Llama-2-7b-hf"
-    
-    # If no metrics_recorder is provided, create one for backward compatibility
-    if metrics_recorder is None:
-        try:
-            metrics_recorder = MetricsRecorder(
-                psycopg2.connect("dbname=metrics"), logger, repository, branch, commit_id, commit_msg, True
-            )
-            should_close_recorder = True
-        except Exception as e:
-            logger.error(f"Failed to create metrics recorder: {e}")
-            return
-    else:
-        should_close_recorder = False
-    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, 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, 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[0, -1], temperature, top_k)
-            idx_next = multinomial_sample_one_no_sync(probs)
-            return idx_next, probs
-
-        # First eager forward pass
-        logger.info("running first eager forward pass")
-        start = perf_counter()
-        outputs = model(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        first_eager_fwd_pass_time = end - start
-        logger.info(f"completed first eager forward pass in: {first_eager_fwd_pass_time}s")
-
-        # Second eager forward pass (should be faster)
-        logger.info("running second eager forward pass")
-        start = perf_counter()
-        outputs = model(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        second_eager_fwd_pass_time = end - start
-        logger.info(f"completed second eager forward pass in: {second_eager_fwd_pass_time}s")
-
-        # First eager generation
-        logger.info("running first eager generation")
-        start = perf_counter()
-        output = model.generate(**inputs)
-        torch.cuda.synchronize()
-        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())}")
-
-        # Second eager generation (should be faster)
-        logger.info("running second eager generation")
-        start = perf_counter()
-        output = model.generate(**inputs)
-        torch.cuda.synchronize()
-        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())}")
-
-        logger.info("running generation timing loop")
-
-        input_pos = torch.arange(0, seq_length, device=device)
-        inputs = inputs["input_ids"]
-
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(inputs, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_first_token = end - start
-
-        input_pos = torch.tensor([seq_length], device=device, dtype=torch.int)
-        next_token = next_token.clone()
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(next_token, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_second_token = end - start
-
-        input_pos = torch.tensor([seq_length + 1], device=device, dtype=torch.int)
-        next_token = next_token.clone()
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(next_token, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_third_token = end - start
-
-        logger.info("running longer generation timing loop")
-
-        total_time = 0
-        for i in range(20):
-            input_pos = torch.tensor([seq_length + 2 + i], device=device, dtype=torch.int)
-            next_token = next_token.clone()
-            start = perf_counter()
-            with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-                logits = model(next_token, position_ids=input_pos).logits
-            next_token, probs = sample(logits, temperature=0.6, top_k=5)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            total_time += end - start
-
-        mean_time_to_next_token = total_time / 20
-
-        logger.info("running compilation benchmarks")
-
-        # Now compile the model
-        model = torch.compile(model, mode="max-autotune", fullgraph=True)
-
-        # StaticCache for generation
-        with torch.device(device):
-            model.setup_caches(max_batch_size=batch_size, max_seq_len=seq_length + num_tokens_to_generate)
-
-        input_pos = torch.arange(0, seq_length, device=device)
-        inputs = tokenizer(prompt, return_tensors="pt").to(device)["input_ids"]
-
-        logger.info("compiling model")
-
-        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16, generation_config=gen_config)
-        model.to(device)
-        model = torch.compile(model, mode="max-autotune", fullgraph=True)
-
-        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)
-        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)
-        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()
-    
-    # Only close the recorder if we created it locally
-    if should_close_recorder:
-        metrics_recorder.close() 

benchmark/benchmarks_entrypoint.py

@@ -1,35 +1,15 @@
-# Copyright 2025 The HuggingFace 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 argparse
 import importlib.util
 import logging
 import os
 import sys
-import json
-import uuid
-from datetime import datetime
-from typing import Dict, Tuple, Optional, List
+from typing import Dict, Tuple
 
-import pandas as pd
+from psycopg2.extensions import register_adapter
+from psycopg2.extras import Json
 
-try:
-    from psycopg2.extensions import register_adapter
-    from psycopg2.extras import Json
-    register_adapter(dict, Json)
-    PSYCOPG2_AVAILABLE = True
-except ImportError:
-    PSYCOPG2_AVAILABLE = False
+
+register_adapter(dict, Json)
 
 
 class ImportModuleException(Exception):
@@ -38,239 +18,61 @@ class ImportModuleException(Exception):
 
 class MetricsRecorder:
     def __init__(
-        self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str, 
-        collect_csv_data: bool = True
+        self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str
     ):
         self.conn = connection
-        self.use_database = connection is not None
-        if self.use_database:
-            self.conn.autocommit = True
+        self.conn.autocommit = True
         self.logger = logger
         self.repository = repository
         self.branch = branch
         self.commit_id = commit_id
         self.commit_msg = commit_msg
-        self.collect_csv_data = collect_csv_data
-        
-        # For CSV export - store all data in pandas DataFrames (only if CSV collection is enabled)
-        if self.collect_csv_data:
-            # Initialize empty DataFrames with proper schemas
-            self.benchmarks_df = pd.DataFrame(columns=[
-                'benchmark_id', 'repository', 'branch', 'commit_id', 'commit_message', 
-                'metadata', 'created_at'
-            ])
-            self.device_measurements_df = pd.DataFrame(columns=[
-                'benchmark_id', 'cpu_util', 'mem_megabytes', 'gpu_util', 
-                'gpu_mem_megabytes', 'time'
-            ])
-            self.model_measurements_df = pd.DataFrame(columns=[
-                'benchmark_id', 'time', 'model_load_time', 'first_eager_forward_pass_time_secs',
-                'second_eager_forward_pass_time_secs', 'first_eager_generate_time_secs',
-                'second_eager_generate_time_secs', 'time_to_first_token_secs',
-                'time_to_second_token_secs', 'time_to_third_token_secs',
-                'time_to_next_token_mean_secs', 'first_compile_generate_time_secs',
-                'second_compile_generate_time_secs', 'third_compile_generate_time_secs',
-                'fourth_compile_generate_time_secs'
-            ])
-        else:
-            self.benchmarks_df = None
-            self.device_measurements_df = None
-            self.model_measurements_df = None
 
-    def initialise_benchmark(self, metadata: dict[str, str]) -> str:
+    def initialise_benchmark(self, metadata: Dict[str, str]) -> int:
         """
-        Creates a new benchmark, returns the benchmark id (UUID)
+        Creates a new benchmark, returns the benchmark id
         """
-        # Generate a unique UUID for this benchmark
-        benchmark_id = str(uuid.uuid4())
-        
-        if self.use_database:
-            with self.conn.cursor() as cur:
-                cur.execute(
-                    "INSERT INTO benchmarks (benchmark_id, repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s, %s)",
-                    (benchmark_id, self.repository, self.branch, self.commit_id, self.commit_msg, metadata),
-                )
-                self.logger.debug(f"initialised benchmark #{benchmark_id}")
-        
-        # Store benchmark data for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame
-            new_row = pd.DataFrame([{
-                'benchmark_id': benchmark_id,
-                'repository': self.repository,
-                'branch': self.branch,
-                'commit_id': self.commit_id,
-                'commit_message': self.commit_msg,
-                'metadata': json.dumps(metadata),
-                'created_at': datetime.utcnow().isoformat()
-            }])
-            self.benchmarks_df = pd.concat([self.benchmarks_df, new_row], ignore_index=True)
-            
-        mode_info = []
-        if self.use_database:
-            mode_info.append("database")
-        if self.collect_csv_data:
-            mode_info.append("CSV")
-        mode_str = " + ".join(mode_info) if mode_info else "no storage"
-        
-        self.logger.debug(f"initialised benchmark #{benchmark_id} ({mode_str} mode)")
-        return 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: str, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes):
+    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.
         """
-        # Store device measurements for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame
-            new_row = pd.DataFrame([{
-                'benchmark_id': benchmark_id,
-                'cpu_util': cpu_util,
-                'mem_megabytes': mem_megabytes,
-                'gpu_util': gpu_util,
-                'gpu_mem_megabytes': gpu_mem_megabytes,
-                'time': datetime.utcnow().isoformat()
-            }])
-            self.device_measurements_df = pd.concat([self.device_measurements_df, new_row], ignore_index=True)
-        
-        # Store in database if available
-        if self.use_database:
-            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),
-                )
-            
+        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"collected device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]"
+            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: str, measurements: dict[str, float]):
-        # Store model measurements for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame with flattened measurements
-            row_data = {
-                'benchmark_id': benchmark_id,
-                'time': datetime.utcnow().isoformat()
-            }
-            # Flatten the measurements dict into the row
-            row_data.update(measurements)
-            
-            new_row = pd.DataFrame([row_data])
-            self.model_measurements_df = pd.concat([self.model_measurements_df, new_row], ignore_index=True)
-        
-        # Store in database if available
-        if self.use_database:
-            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"collected model measurements for benchmark #{benchmark_id}: {measurements}")
-
-    def export_to_csv(self, output_dir: str = "benchmark_results"):
-        """
-        Export all collected data to CSV files using pandas DataFrames
-        """
-        if not self.collect_csv_data:
-            self.logger.warning("CSV data collection is disabled - no CSV files will be generated")
-            return
-            
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-            self.logger.info(f"Created output directory: {output_dir}")
-            
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        files_created = []
-        
-        # Export using pandas DataFrames
-        self._export_pandas_data(output_dir, timestamp, files_created)
-        
-        self.logger.info(f"CSV export complete! Created {len(files_created)} files in {output_dir}")
-    
-    def _export_pandas_data(self, output_dir: str, timestamp: str, files_created: list):
-        """
-        Export CSV files using pandas DataFrames
-        """
-        # Export benchmarks
-        benchmarks_file = os.path.join(output_dir, f"benchmarks_{timestamp}.csv")
-        self.benchmarks_df.to_csv(benchmarks_file, index=False)
-        files_created.append(benchmarks_file)
-        self.logger.info(f"Exported {len(self.benchmarks_df)} benchmark records to {benchmarks_file}")
-        
-        # Export device measurements  
-        device_file = os.path.join(output_dir, f"device_measurements_{timestamp}.csv")
-        self.device_measurements_df.to_csv(device_file, index=False)
-        files_created.append(device_file)
-        self.logger.info(f"Exported {len(self.device_measurements_df)} device measurement records to {device_file}")
-        
-        # Export model measurements (already flattened)
-        model_file = os.path.join(output_dir, f"model_measurements_{timestamp}.csv")
-        self.model_measurements_df.to_csv(model_file, index=False)
-        files_created.append(model_file)
-        self.logger.info(f"Exported {len(self.model_measurements_df)} model measurement records to {model_file}")
-        
-        # Create comprehensive summary using pandas operations
-        summary_file = os.path.join(output_dir, f"benchmark_summary_{timestamp}.csv")
-        self._create_summary(summary_file)
-        files_created.append(summary_file)
-    
-    def _create_summary(self, summary_file: str):
-        """
-        Create a comprehensive summary CSV using pandas operations
-        """
-        if len(self.benchmarks_df) == 0:
-            # Create empty summary file
-            summary_df = pd.DataFrame()
-            summary_df.to_csv(summary_file, index=False)
-            self.logger.info(f"Created empty benchmark summary at {summary_file}")
-            return
-        
-        # Start with benchmarks as the base
-        summary_df = self.benchmarks_df.copy()
-        
-        # Add model measurements (join on benchmark_id)
-        if len(self.model_measurements_df) > 0:
-            # Drop 'time' column from model measurements to avoid conflicts
-            model_df = self.model_measurements_df.drop(columns=['time'], errors='ignore')
-            summary_df = summary_df.merge(model_df, on='benchmark_id', how='left')
-        
-        # Calculate device measurement aggregates using pandas groupby
-        if len(self.device_measurements_df) > 0:
-            device_agg = self.device_measurements_df.groupby('benchmark_id').agg({
-                'cpu_util': ['mean', 'max', 'std', 'count'],
-                'mem_megabytes': ['mean', 'max', 'std'],
-                'gpu_util': ['mean', 'max', 'std'],
-                'gpu_mem_megabytes': ['mean', 'max', 'std']
-            }).round(3)
-            
-            # Flatten column names
-            device_agg.columns = [f"{col[0]}_{col[1]}" for col in device_agg.columns]
-            device_agg = device_agg.reset_index()
-            
-            # Rename count column to be more descriptive
-            if 'cpu_util_count' in device_agg.columns:
-                device_agg = device_agg.rename(columns={'cpu_util_count': 'device_measurement_count'})
-            
-            # Merge with summary
-            summary_df = summary_df.merge(device_agg, on='benchmark_id', how='left')
-        
-        # Export the comprehensive summary
-        summary_df.to_csv(summary_file, index=False)
-        self.logger.info(f"Created comprehensive benchmark summary with {len(summary_df)} records at {summary_file}")
+    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):
-        if self.use_database and self.conn:
-            self.conn.close()
+        self.conn.close()
 
 
 logger = logging.getLogger(__name__)
@@ -283,7 +85,7 @@ handler.setFormatter(formatter)
 logger.addHandler(handler)
 
 
-def parse_arguments() -> tuple[str, str, str, str, bool, str]:
+def parse_arguments() -> Tuple[str, str, str, str]:
     """
     Parse command line arguments for the benchmarking CLI.
     """
@@ -312,27 +114,10 @@ def parse_arguments() -> tuple[str, str, str, str, bool, str]:
         type=str,
         help="The commit message associated with the commit, truncated to 70 characters.",
     )
-    
-    parser.add_argument(
-        "--csv",
-        action="store_true",
-        default=False,
-        help="Enable CSV output files generation."
-    )
-    
-    parser.add_argument(
-        "--csv-output-dir",
-        type=str,
-        default="benchmark_results",
-        help="Directory for CSV output files (default: benchmark_results)."
-    )
 
     args = parser.parse_args()
-    
-    # CSV is disabled by default, only enabled when --csv is used
-    generate_csv = args.csv
 
-    return args.repository, args.branch, args.commit_id, args.commit_msg, generate_csv, args.csv_output_dir
+    return args.repository, args.branch, args.commit_id, args.commit_msg
 
 
 def import_from_path(module_name, file_path):
@@ -346,124 +131,22 @@ def import_from_path(module_name, file_path):
         raise ImportModuleException(f"failed to load python module: {e}")
 
 
-def create_database_connection():
-    """
-    Try to create a database connection. Returns None if connection fails.
-    """
-    if not PSYCOPG2_AVAILABLE:
-        logger.warning("psycopg2 not available - running in CSV-only mode")
-        return None
-        
-    try:
-        import psycopg2
-        conn = psycopg2.connect("dbname=metrics")
-        logger.info("Successfully connected to database")
-        return conn
-    except Exception as e:
-        logger.warning(f"Failed to connect to database: {e}. Running in CSV-only mode")
-        return None
-
-
-def create_global_metrics_recorder(repository: str, branch: str, commit_id: str, commit_msg: str, 
-                                   generate_csv: bool = False) -> MetricsRecorder:
-    """
-    Create a global metrics recorder that will be used across all benchmarks.
-    """
-    connection = create_database_connection()
-    recorder = MetricsRecorder(connection, logger, repository, branch, commit_id, commit_msg, generate_csv)
-    
-    # Log the storage mode
-    storage_modes = []
-    if connection is not None:
-        storage_modes.append("database")
-    if generate_csv:
-        storage_modes.append("CSV")
-    
-    if not storage_modes:
-        logger.warning("Running benchmarks with NO data storage (no database connection, CSV disabled)")
-        logger.warning("Use --csv flag to enable CSV output when database is unavailable")
-    else:
-        logger.info(f"Running benchmarks with: {' + '.join(storage_modes)} storage")
-    
-    return recorder
-
-
 if __name__ == "__main__":
     benchmarks_folder_path = os.path.dirname(os.path.realpath(__file__))
-    benches_folder_path = os.path.join(benchmarks_folder_path, "benches")
 
-    repository, branch, commit_id, commit_msg, generate_csv, csv_output_dir = parse_arguments()
-    
-    # Create a global metrics recorder
-    global_metrics_recorder = create_global_metrics_recorder(repository, branch, commit_id, commit_msg, generate_csv)
-    
-    successful_benchmarks = 0
-    failed_benchmarks = 0
-    
-    # Automatically discover all benchmark modules in benches/ folder
-    benchmark_modules = []
-    
-    if os.path.exists(benches_folder_path):
-        logger.debug(f"Scanning for benchmarks in: {benches_folder_path}")
-        for entry in os.scandir(benches_folder_path):
+    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.name.startswith("__"):  # Skip __init__.py, __pycache__, etc.
+            if entry.path == __file__:
                 continue
-                
-            # Check if the file has a run_benchmark function
-            try:
-                logger.debug(f"checking if benches/{entry.name} has run_benchmark function")
-                module = import_from_path(entry.name.split(".")[0], entry.path)
-                if hasattr(module, 'run_benchmark'):
-                    benchmark_modules.append(entry.name)
-                    logger.debug(f"discovered benchmark: {entry.name}")
-                else:
-                    logger.debug(f"skipping {entry.name} - no run_benchmark function found")
-            except Exception as e:
-                logger.debug(f"failed to check benches/{entry.name}: {e}")
-    else:
-        logger.warning(f"Benches directory not found: {benches_folder_path}")
-
-    if benchmark_modules:
-        logger.info(f"Discovered {len(benchmark_modules)} benchmark(s): {benchmark_modules}")
-    else:
-        logger.warning("No benchmark modules found in benches/ directory")
-
-    for module_name in benchmark_modules:
-        module_path = os.path.join(benches_folder_path, module_name)
-        try:
-            logger.debug(f"loading: {module_name}")
-            module = import_from_path(module_name.split(".")[0], module_path)
-            logger.info(f"running benchmarks in: {module_name}")
-            
-            # Check if the module has an updated run_benchmark function that accepts metrics_recorder
-            try:
-                # Try the new signature first
-                module.run_benchmark(logger, repository, branch, commit_id, commit_msg, global_metrics_recorder)
-            except TypeError:
-                # Fall back to the old signature for backward compatibility
-                logger.warning(f"Module {module_name} using old run_benchmark signature - database connection will be created per module")
-                module.run_benchmark(logger, repository, branch, commit_id, commit_msg)
-            
-            successful_benchmarks += 1
+            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)
-            failed_benchmarks += 1
         except Exception as e:
-            logger.error(f"error running benchmarks for {module_name}: {e}")
-            failed_benchmarks += 1
-
-    # Export CSV results at the end (if enabled)
-    try:
-        if generate_csv:
-            global_metrics_recorder.export_to_csv(csv_output_dir)
-            logger.info(f"CSV reports have been generated and saved to the {csv_output_dir} directory")
-        else:
-            logger.info("CSV generation disabled - no CSV files created (use --csv to enable)")
-        
-        logger.info(f"Benchmark run completed. Successful: {successful_benchmarks}, Failed: {failed_benchmarks}")
-    except Exception as e:
-        logger.error(f"Failed to export CSV results: {e}")
-    finally:
-        global_metrics_recorder.close()
+            logger.error(f"error running benchmarks for {entry.name}: {e}")

benchmark/config/generation.yaml

@@ -19,7 +19,7 @@ backend:
   model: meta-llama/Llama-2-7b-hf
   cache_implementation: static
   torch_compile: true
-  dtype: float16
+  torch_dtype: float16
   torch_compile_config:
     backend: inductor
     mode: reduce-overhead

benchmark/init_db.sql

@@ -0,0 +1,34 @@
+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

@@ -0,0 +1,346 @@
+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

@@ -2,5 +2,4 @@ gpustat==1.1.1
 psutil==6.0.0
 psycopg2==2.9.9
 torch>=2.4.0
-hf_transfer
-pandas>=1.5.0
+hf_transfer

conftest.py

@@ -23,17 +23,12 @@ from os.path import abspath, dirname, join
 import _pytest
 import pytest
 
-from transformers.testing_utils import (
-    HfDoctestModule,
-    HfDocTestParser,
-    is_torch_available,
-    patch_torch_compile_force_graph,
-)
+from transformers.testing_utils import HfDoctestModule, HfDocTestParser
 
 
 NOT_DEVICE_TESTS = {
     "test_tokenization",
-    "test_tokenization_mistral_common",
+    "test_processor",
     "test_processing",
     "test_beam_constraints",
     "test_configuration_utils",
@@ -88,8 +83,6 @@ def pytest_configure(config):
     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", "not_device_test: mark the tests always running on cpu")
-    config.addinivalue_line("markers", "torch_compile_test: mark test which tests torch compile functionality")
-    config.addinivalue_line("markers", "torch_export_test: mark test which tests torch export functionality")
 
 
 def pytest_collection_modifyitems(items):
@@ -134,14 +127,3 @@ class CustomOutputChecker(OutputChecker):
 doctest.OutputChecker = CustomOutputChecker
 _pytest.doctest.DoctestModule = HfDoctestModule
 doctest.DocTestParser = HfDocTestParser
-
-if is_torch_available():
-    import torch
-
-    # The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
-    # We set it to `False` for CI. See https://github.com/pytorch/pytorch/issues/157274#issuecomment-3090791615
-    torch.backends.cudnn.allow_tf32 = False
-
-    # patch `torch.compile`: if `TORCH_COMPILE_FORCE_FULLGRAPH=1` (or values considered as true, e.g. yes, y, etc.),
-    # the patched version will always run with `fullgraph=True`.
-    patch_torch_compile_force_graph()

docker/consistency.dockerfile

@@ -4,7 +4,7 @@ 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 pip install --no-cache-dir -U pip setuptools GitPython
+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
 # tensorflow pin matching setup.py
 RUN uv pip install --no-cache-dir pypi-kenlm

docker/custom-tokenizers.dockerfile

@@ -4,7 +4,7 @@ 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
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 
 RUN wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz
 RUN tar xvf jumanpp-2.0.0-rc3.tar.xz
@@ -20,7 +20,7 @@ RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pyt
 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
 # spacy is not used so not tested. Causes to failures. TODO fix later
-RUN uv run python -m unidic download
+RUN python3 -m unidic download
 RUN uv pip uninstall transformers
 
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/examples-tf.dockerfile

@@ -5,7 +5,7 @@ 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
+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 uv pip install --no-cache-dir  "protobuf==3.20.3"

docker/examples-torch.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-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 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 --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 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

docker/exotic-models.dockerfile

@@ -4,10 +4,10 @@ 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 pip install --no-cache-dir -U pip setuptools
+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 uv pip install --no-cache-dir  --no-deps timm accelerate
-RUN uv pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
+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 git clone https://github.com/facebookresearch/detectron2.git

docker/jax-light.dockerfile

@@ -4,7 +4,7 @@ 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 pip install --no-cache-dir -U pip setuptools
+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 apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/pipeline-tf.dockerfile

@@ -4,7 +4,7 @@ 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 pip install --no-cache-dir -U pip setuptools
+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 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

@@ -2,10 +2,10 @@ FROM python:3.9-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 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 --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 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

docker/quality.dockerfile

@@ -2,8 +2,8 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update && apt-get install -y time git
+RUN apt-get update && apt-get install -y time git 
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip install uv
+RUN pip install uv &&  uv venv
 RUN uv pip install --no-cache-dir -U pip setuptools GitPython "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ruff]" urllib3
 RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/*

docker/tf-light.dockerfile

@@ -5,7 +5,7 @@ USER root
 RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ pkg-config openssh-client git
 RUN apt-get install -y  cmake
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+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 uv pip install --no-cache-dir  "protobuf==3.20.3"
 RUN uv pip uninstall transformers

docker/torch-jax-light.dockerfile

@@ -4,7 +4,7 @@ ARG REF=main
 USER root
 RUN apt-get update &&  apt-get install -y 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 pip install --no-cache-dir -U pip setuptools
+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]"

docker/torch-light.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-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 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 --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 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

docker/torch-tf-light.dockerfile

@@ -5,7 +5,7 @@ RUN echo ${REF}
 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
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+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 git lfs install

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-ubuntu22.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,9 +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='2.8.0'
+ARG PYTORCH='2.6.0'
+# (not always a valid torch version)
+ARG INTEL_TORCH_EXT='2.3.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
+ARG CUDA='cu121'
 # Disable kernel mapping for now until all tests pass
 ENV DISABLE_KERNEL_MAPPING=1
 
@@ -26,16 +28,13 @@ 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 $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --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<2.16" "tensorflow_probability<0.22" && 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 -U timm
-
-RUN [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git || echo "Don't install detectron2 with nightly torch"
-
-RUN python3 -m pip install --no-cache-dir pytesseract
+RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$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"
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
@@ -44,8 +43,6 @@ RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/pef
 
 # For bettertransformer
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
-# For kernels
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/kernels@main#egg=kernels
 
 # For video model testing
 RUN python3 -m pip install --no-cache-dir av
@@ -56,14 +53,15 @@ RUN python3 -m pip install --no-cache-dir bitsandbytes
 # Some tests require quanto
 RUN python3 -m pip install --no-cache-dir quanto
 
-# After using A10 as CI runner, let's run FA2 tests
-RUN [ "$PYTORCH" != "pre" ] && python3 -m pip uninstall -y ninja && python3 -m pip install --no-cache-dir ninja && python3 -m pip install flash-attn --no-cache-dir --no-build-isolation || echo "Don't install FA2 with nightly torch"
-
-# TODO (ydshieh): check this again
 # `quanto` will install `ninja` which leads to many `CUDA error: an illegal memory access ...` in some model tests
 # (`deformable_detr`, `rwkv`, `mra`)
 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
+
 # For `nougat` tokenizer
 RUN python3 -m pip install --no-cache-dir python-Levenshtein
 

docker/transformers-gpu/Dockerfile

@@ -17,7 +17,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     jupyter \
     tensorflow \
     torch
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/kernels@main#egg=kernels
 
 RUN git clone https://github.com/NVIDIA/apex
 RUN cd apex && \

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM rocm/pytorch:rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.7.1
+FROM rocm/pytorch:rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.6.0
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -20,12 +20,8 @@ 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
 
-# On ROCm, torchcodec is required to decode audio files
-# RUN python3 -m pip install --no-cache-dir torchcodec
-# Install transformers
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video,audio]
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
 
-# Remove tensorflow and flax as they are no longer supported by transformers
 RUN python3 -m pip uninstall -y tensorflow flax
 
 # When installing in editable mode, `transformers` is not recognized as a package.
@@ -36,4 +32,4 @@ RUN cd transformers && python3 setup.py develop
 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 -y kernels

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

@@ -4,7 +4,7 @@ LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG PYTORCH='2.8.0'
+ARG PYTORCH='2.6.0'
 # Example: `cu102`, `cu113`, etc.
 ARG CUDA='cu126'
 
@@ -21,7 +21,7 @@ RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'p
 # Install latest release PyTorch
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 

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

@@ -19,7 +19,7 @@ RUN python3 -m pip uninstall -y torch torchvision torchaudio
 # Install **nightly** release PyTorch (flag `--pre`)
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
+RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$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'

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-ubuntu22.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -11,20 +11,18 @@ 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='2.8.0'
+ARG PYTORCH='2.6.0'
 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

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

@@ -26,7 +26,7 @@ RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch';
 RUN echo torch=$VERSION
 # `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build.
 # 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 $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
+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 git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
@@ -78,10 +78,6 @@ RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submod
 # 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 fp-quant for quantization testing
-# Requires py3.11 but our CI runs on 3.9
-# RUN python3 -m pip install --no-cache-dir "fp-quant>=0.1.6"
-
 # Add compressed-tensors for quantization testing
 RUN python3 -m pip install --no-cache-dir compressed-tensors
 
@@ -97,9 +93,6 @@ 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
-
 # 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

docs/README.md

@@ -20,21 +20,22 @@ To generate the documentation, you first have to build it. Several packages are
 you can install them with the following command, at the root of the code repository:
 
 ```bash
-pip install -e ".[dev]"
+pip install -e ".[docs]"
 ```
 
-> [!NOTE]
-> This command might fail for some OS that are missing dependencies. Check step 4 in [Create a Pull Request](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request) to workaround it.
-
 Then you need to install our special tool that builds the documentation:
 
 ```bash
 pip install git+https://github.com/huggingface/doc-builder
 ```
 
-> [!NOTE]
-> You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-> check how they look before committing for instance). You don't have to commit the built documentation.
+---
+**NOTE**
+
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+check how they look before committing for instance). You don't have to commit the built documentation.
+
+---
 
 ## Building the documentation
 
@@ -71,8 +72,12 @@ doc-builder preview transformers docs/source/en/
 
 The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
 
-> [!NOTE]
-> The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+---
+**NOTE**
+
+The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+
+---
 
 ## Adding a new element to the navigation bar
 
@@ -159,9 +164,6 @@ These classes should be added using our Markdown syntax. Usually as follows:
 [[autodoc]] XXXConfig
 ```
 
-> [!IMPORTANT]
-> Always add a blank line after `[[autodoc]]` to ensure it passes the CI/CD checks.
-
 This will include every public method of the configuration that is documented. If for some reason you wish for a method
 not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
 
@@ -276,7 +278,7 @@ 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:

docs/source/ar/bertology.md

@@ -3,16 +3,16 @@
 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
+  https://arxiv.org/abs/1905.05950
+- Are Sixteen Heads Really Better than One? بواسطة Paul Michel و Omer Levy و Graham Neubig: https://arxiv.org/abs/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
+  Manning: https://arxiv.org/abs/1906.04341
+- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/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):
+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://arxiv.org/abs/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.
+- استرجاع قيم ومشتقات  مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://arxiv.org/abs/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

@@ -304,7 +304,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint, dtype=torch.bfloat16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
 
 ```python
 messages = [

docs/source/ar/conversations.md

@@ -25,7 +25,7 @@ chat = [
 import torch
 from transformers import pipeline
 
-pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", dtype=torch.bfloat16, device_map="auto")
+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'])
 ```
@@ -126,7 +126,7 @@ chat = [
 ]
 
 # 1: تحميل النموذج والمحلل
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", dtype=torch.bfloat16)
+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
@@ -164,7 +164,7 @@ print("Decoded output:\n", decoded_output)
 
 ### 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 "dtype" كما فعلنا أعلاه.
+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`:
 

docs/source/ar/custom_models.md

@@ -30,7 +30,7 @@ class ResnetConfig(PretrainedConfig):
     def __init__(
         self,
         block_type="bottleneck",
-        layers: list[int] = [3, 4, 6, 3],
+        layers: List[int] = [3, 4, 6, 3],
         num_classes: int = 1000,
         input_channels: int = 3,
         cardinality: int = 1,
@@ -280,7 +280,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 الآن لإرسال النموذج إلى Hub، تأكد من تسجيل الدخول. إما تشغيل في المحطة الأوامر الطرفية الخاصة بك:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 أو من دفتر ملاحظات:

docs/source/ar/glossary.md

@@ -135,7 +135,7 @@
 في كل وحدة الانتباه الباقية في المحولات، تلي طبقة الاهتمام الانتباه عادة طبقتان للتغذية الأمامية.
 حجم تضمين الطبقة الأمامية الوسيطة أكبر عادة من حجم المخفي للنموذج (على سبيل المثال، لـ
 `google-bert/bert-base-uncased`).
-بالنسبة لإدخال بحجم `[batch_size, sequence_length]`، يمكن أن تمثل الذاكرة المطلوبة لتخزين التضمينات الأمامية الوسيطة `[batch_size، sequence_length, config.intermediate_size]` جزءًا كبيرًا من استخدام الذاكرة. لاحظ مؤلفو (https://huggingface.co/papers/2001.04451)[Reformer: The Efficient Transformer] أنه نظرًا لأن الحساب مستقل عن بعد `sequence_length`، فإنه من المكافئ رياضيًا حساب تضمينات الإخراج الأمامية `[batch_size، config.hidden_size]_0, ..., [batch_size، `config_size]_n
+بالنسبة لإدخال بحجم `[batch_size, sequence_length]`، يمكن أن تمثل الذاكرة المطلوبة لتخزين التضمينات الأمامية الوسيطة `[batch_size، sequence_length, config.intermediate_size]` جزءًا كبيرًا من استخدام الذاكرة. لاحظ مؤلفو (https://arxiv.org/abs/2001.04451)[Reformer: The Efficient Transformer] أنه نظرًا لأن الحساب مستقل عن بعد `sequence_length`، فإنه من المكافئ رياضيًا حساب تضمينات الإخراج الأمامية `[batch_size، config.hidden_size]_0, ..., [batch_size، `config_size]_n
 فردياً والتوصيل بها لاحقًا إلى `[batch_size, sequence_length, config.hidden_size]` مع `n = sequence_length`، والذي يتداول زيادة وقت الحساب مقابل تقليل استخدام الذاكرة، ولكنه ينتج عنه نتيجة مكافئة رياضيا.
 
 بالنسبة للنماذج التي تستخدم الدالة `[apply_chunking_to_forward]`، يحدد `chunk_size` عدد التضمينات يتم حساب الإخراج بالتوازي وبالتالي يحدد المقايضة بين حجم الذاكرة والتعقيد الوقت. إذا تم تعيين `chunk_size` إلى `0`، فلن يتم إجراء تجزئة التغذية الأمامية.
@@ -173,7 +173,7 @@
 
 <Youtube id="VFp38yj8h3A"/>
 
-يعمل كل محلل لغوي بشكل مختلف ولكن الآلية الأساسية تبقى كما هي. إليك مثال باستخدام محلل BERT اللغوي، والذي يعد محلل لغوي [WordPiece](https://huggingface.co/papers/1609.08144):
+يعمل كل محلل لغوي بشكل مختلف ولكن الآلية الأساسية تبقى كما هي. إليك مثال باستخدام محلل BERT اللغوي، والذي يعد محلل لغوي [WordPiece](https://arxiv.org/pdf/1609.08144.pdf):
 
 ```python
 >>> from transformers import BertTokenizer

docs/source/ar/llm_tutorial_optimization.md

@@ -6,18 +6,18 @@
 تحقق نماذج اللغة الكبيرة (LLMs) مثل GPT3/4، [Falcon](https://huggingface.co/tiiuae/falcon-40b)، و [Llama](https://huggingface.co/meta-llama/Llama-2-70b-hf) تقدمًا سريعًا في قدرتها على معالجة المهام التي تركز على الإنسان، مما يجعلها أدوات أساسية في الصناعات القائمة على المعرفة الحديثة.
 لا يزال نشر هذه النماذج في المهام الواقعية يمثل تحديًا، ومع ذلك:
 
--   لكي تظهر نماذج اللغة الكبيرة قدرات فهم وتوليد النصوص قريبة من قدرات الإنسان، فإنها تتطلب حاليًا  إلى تكوينها من مليارات المعلمات (انظر [كابلان وآخرون](https://huggingface.co/papers/2001.08361)، [وي وآخرون](https://huggingface.co/papers/2206.07682)). وهذا بدوره يزيد من متطلبات الذاكرة للاستدلال.
+-   لكي تظهر نماذج اللغة الكبيرة قدرات فهم وتوليد النصوص قريبة من قدرات الإنسان، فإنها تتطلب حاليًا  إلى تكوينها من مليارات المعلمات (انظر [كابلان وآخرون](https://arxiv.org/abs/2001.08361)، [وي وآخرون](https://arxiv.org/abs/2206.07682)). وهذا بدوره يزيد من متطلبات الذاكرة للاستدلال.
 -   في العديد من المهام الواقعية، تحتاج نماذج اللغة الكبيرة إلى معلومات سياقية شاملة. يتطلب ذلك قدرة النموذج على إدارة تسلسلات إدخال طويلة للغاية أثناء الاستدلال.
 
 يكمن جوهر صعوبة هذه التحديات في تعزيز القدرات الحسابية والذاكرة لنماذج اللغة الكبيرة، خاصة عند التعامل مع تسلسلات الإدخال الضخمة.
 
 في هذا الدليل، سنستعرض التقنيات الفعالة لتُحسِّن من كفاءة نشر نماذج اللغة الكبيرة:
 
-1. سنتناول تقنية "دقة أقل" التي أثبتت الأبحاث فعاليتها في تحقيق مزايا حسابية دون التأثير بشكل ملحوظ على أداء النموذج عن طريق العمل بدقة رقمية أقل [8 بت و4 بت](/main_classes/quantization).
+1. سنتناول تقنية "دقة أقل" التي أثبتت الأبحاث فعاليتها في تحقيق مزايا حسابية دون التأثير بشكل ملحوظ على أداء النموذج عن طريق العمل بدقة رقمية أقل [8 بت و4 بت](/main_classes/quantization.md).
 
 2.  **اFlash Attention:** إن Flash Attention وهي نسخة مُعدَّلة من خوارزمية الانتباه التي لا توفر فقط نهجًا أكثر كفاءة في استخدام الذاكرة، ولكنها تحقق أيضًا كفاءة متزايدة بسبب الاستخدام الأمثل لذاكرة GPU.
 
-3.  **الابتكارات المعمارية:** حيث تم اقتراح هياكل متخصصة تسمح باستدلال أكثر فعالية نظرًا لأن نماذج اللغة الكبيرة يتم نشرها دائمًا بنفس الطريقة أثناء عملية الاستدلال، أي توليد النص التنبؤي التلقائي مع سياق الإدخال الطويل، فقد تم اقتراح بنيات نموذج متخصصة تسمح بالاستدلال الأكثر كفاءة. أهم تقدم في بنيات النماذج هنا هو [عذر](https://huggingface.co/papers/2108.12409)، [الترميز الدوار](https://huggingface.co/papers/2104.09864)، [الاهتمام متعدد الاستعلامات (MQA)](https://huggingface.co/papers/1911.02150) و [مجموعة الانتباه بالاستعلام (GQA)](https://huggingface.co/papers/2305.13245).
+3.  **الابتكارات المعمارية:** حيث تم اقتراح هياكل متخصصة تسمح باستدلال أكثر فعالية نظرًا لأن نماذج اللغة الكبيرة يتم نشرها دائمًا بنفس الطريقة أثناء عملية الاستدلال، أي توليد النص التنبؤي التلقائي مع سياق الإدخال الطويل، فقد تم اقتراح بنيات نموذج متخصصة تسمح بالاستدلال الأكثر كفاءة. أهم تقدم في بنيات النماذج هنا هو [عذر](https://arxiv.org/abs/2108.12409)، [الترميز الدوار](https://arxiv.org/abs/2104.09864)، [الاهتمام متعدد الاستعلامات (MQA)](https://arxiv.org/abs/1911.02150) و [مجموعة الانتباه بالاستعلام (GQA)]((https://arxiv.org/abs/2305.13245)).
 
 على مدار هذا الدليل، سنقدم تحليلًا للتوليد التنبؤي التلقائي من منظور المُوتِّرات. نتعمق في مزايا وعيوب استخدام دقة أقل، ونقدم استكشافًا شاملاً لخوارزميات الانتباه الأحدث، ونناقش بنيات نماذج نماذج اللغة الكبيرة المحسنة. سندعم الشرح بأمثلة عملية تُبرِز كل تحسين على حدة.
 
@@ -73,7 +73,7 @@ model = AutoModelForCausalLM.from_pretrained("bigscience/bloom", device_map="aut
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
 
-model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", dtype=torch.bfloat16, device_map="auto", pad_token_id=0)
+model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", torch_dtype=torch.bfloat16, device_map="auto", pad_token_id=0)
 tokenizer = AutoTokenizer.from_pretrained("bigcode/octocoder")
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
@@ -114,7 +114,7 @@ bytes_to_giga_bytes(torch.cuda.max_memory_allocated())
 
 > يتم تدريب جميع النماذج تقريبًا بتنسيق bfloat16 في الوقت الحالي، ولا يوجد سبب لتشغيل النموذج بدقة float32 الكاملة إذا [كانت وحدة معالجة الرسومات (GPU) الخاصة بك تدعم bfloat16](https://discuss.pytorch.org/t/bfloat16-native-support/117155/5). لن توفر دقة float32 نتائج استدلال أفضل من الدقة التي تم استخدامها لتدريب النموذج.
 
-إذا لم تكن متأكدًا من تنسيق تخزين أوزان النموذج على Hub، فيمكنك دائمًا الاطلاع على تهيئة نقطة التفتيش في `"dtype"`، على سبيل المثال [هنا](https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/config.json#L21). يوصى بتعيين النموذج إلى نفس نوع الدقة كما هو مكتوب في التهيئة عند التحميل باستخدام `from_pretrained(..., dtype=...)` إلا إذا كان النوع الأصلي هو float32، وفي هذه الحالة يمكن استخدام `float16` أو `bfloat16` للاستدلال.
+إذا لم تكن متأكدًا من تنسيق تخزين أوزان النموذج على Hub، فيمكنك دائمًا الاطلاع على تهيئة نقطة التفتيش في `"torch_dtype"`، على سبيل المثال [هنا](https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/config.json#L21). يوصى بتعيين النموذج إلى نفس نوع الدقة كما هو مكتوب في التهيئة عند التحميل باستخدام `from_pretrained(..., torch_dtype=...)` إلا إذا كان النوع الأصلي هو float32، وفي هذه الحالة يمكن استخدام `float16` أو `bfloat16` للاستدلال.
 
 
 دعونا نحدد وظيفة `flush(...)` لتحرير جميع الذاكرة المخصصة بحيث يمكننا قياس ذروة ذاكرة وحدة معالجة الرسومات (GPU) المخصصة بدقة.
@@ -152,8 +152,8 @@ from accelerate.utils import release_memory
 release_memory(model)
 ```
 
-والآن ماذا لو لم يكن لدى وحدة معالجة الرسومات (GPU) لديك 32 جيجا بايت من ذاكرة الفيديو العشوائية (VRAM)؟ لقد وجد أن أوزان النماذج يمكن تحويلها إلى 8 بتات أو 4 بتات دون خسارة كبيرة في الأداء (انظر [Dettmers et al.](https://huggingface.co/papers/2208.07339)).
-يمكن تحويل النموذج إلى 3 بتات أو 2 بتات مع فقدان مقبول في الأداء كما هو موضح في ورقة [GPTQ](https://huggingface.co/papers/2210.17323) 🤯.
+والآن ماذا لو لم يكن لدى وحدة معالجة الرسومات (GPU) لديك 32 جيجا بايت من ذاكرة الفيديو العشوائية (VRAM)؟ لقد وجد أن أوزان النماذج يمكن تحويلها إلى 8 بتات أو 4 بتات دون خسارة كبيرة في الأداء (انظر [Dettmers et al.](https://arxiv.org/abs/2208.07339)).
+يمكن تحويل النموذج إلى 3 بتات أو 2 بتات مع فقدان مقبول في الأداء كما هو موضح في ورقة [GPTQ](https://arxiv.org/abs/2210.17323) 🤯.
 
 دون الدخول في الكثير من التفاصيل، تهدف مخططات التكميم إلى تخفيض دقة الأوزان مع محاولة الحفاظ على دقة نتائج النموذج كما هي (*أي* أقرب ما يمكن إلى bfloat16).
 لاحظ أن التكميم يعمل بشكل خاص جيدًا لتوليد النص حيث كل ما نهتم به هو اختيار *مجموعة الرموز الأكثر احتمالًا التالية* ولا نهتم حقًا بالقيم الدقيقة لتوزيع الرمز التالي *logit*.
@@ -231,7 +231,7 @@ flush()
 دعنا نرى ما هو استهلاك ذاكرة GPU الذروة الذي يوفره تكميم 4 بت. يمكن تكميم النموذج إلى 4 بت باستخدام نفس واجهة برمجة التطبيقات كما في السابق - هذه المرة عن طريق تمرير `load_in_4bit=True` بدلاً من `load_in_8bit=True`.
 
 ```python
-model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, pad_token_id=0)
+model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, low_cpu_mem_usage=True, pad_token_id=0)
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
@@ -304,7 +304,7 @@ $$ \textbf{O} = \text{Attn}(\mathbf{X}) = \mathbf{V} \times \text{Softmax}(\math
 
 مع تحسن LLMs في فهم النص وتوليد النص، يتم تطبيقها على مهام متزايدة التعقيد. في حين أن النماذج كانت تتعامل سابقًا مع ترجمة أو تلخيص بضع جمل، فإنها الآن تدير صفحات كاملة، مما يتطلب القدرة على معالجة أطوال إدخال واسعة.
 
-كيف يمكننا التخلص من متطلبات الذاكرة الباهظة للتطويلات المدخلة الكبيرة؟ نحن بحاجة إلى طريقة جديدة لحساب آلية الاهتمام الذاتي التي تتخلص من مصفوفة \\( QK^T \\). [طريقه داو وآخرون.](https://huggingface.co/papers/2205.14135) طوروا بالضبط مثل هذا الخوارزمية الجديدة وأطلقوا عليها اسم **Flash Attention**.
+كيف يمكننا التخلص من متطلبات الذاكرة الباهظة للتطويلات المدخلة الكبيرة؟ نحن بحاجة إلى طريقة جديدة لحساب آلية الاهتمام الذاتي التي تتخلص من مصفوفة \\( QK^T \\). [طريقه داو وآخرون.](Https://arxiv.org/abs/2205.14135) طوروا بالضبط مثل هذا الخوارزمية الجديدة وأطلقوا عليها اسم **Flash Attention**.
 
 باختصار، يكسر الاهتمام الفلاشي حساب \\( \mathbf{V} \times \operatorname{Softmax}(\mathbf{QK}^T\\)) ويحسب بدلاً من ذلك قطعًا أصغر من الإخراج عن طريق التكرار عبر العديد من خطوات حساب Softmax:
 
@@ -318,7 +318,7 @@ $$ \textbf{O}_i \leftarrow s^a_{ij} * \textbf{O}_i + s^b_{ij} * \mathbf{V}_{j} \
 
 > من خلال تتبع إحصائيات التطبيع softmax واستخدام بعض الرياضيات الذكية، يعطي Flash Attention **مخرجات متطابقة رقميًا** مقارنة بطبقة الاهتمام الذاتي الافتراضية بتكلفة ذاكرة لا تزيد خطيًا مع \\( N \\).
 
-عند النظر إلى الصيغة، قد يقول المرء بديهيًا أن الاهتمام الفلاشي يجب أن يكون أبطأ بكثير مقارنة بصيغة الاهتمام الافتراضية حيث يلزم إجراء المزيد من الحسابات. في الواقع، يتطلب Flash Attention المزيد من عمليات الفاصلة العائمة مقارنة بالاهتمام العادي حيث يجب إعادة حساب إحصائيات التطبيع softmax باستمرار (راجع [الورقة](https://huggingface.co/papers/2205.14135) لمزيد من التفاصيل إذا كنت مهتمًا)
+عند النظر إلى الصيغة، قد يقول المرء بديهيًا أن الاهتمام الفلاشي يجب أن يكون أبطأ بكثير مقارنة بصيغة الاهتمام الافتراضية حيث يلزم إجراء المزيد من الحسابات. في الواقع، يتطلب Flash Attention المزيد من عمليات الفاصلة العائمة مقارنة بالاهتمام العادي حيث يجب إعادة حساب إحصائيات التطبيع softmax باستمرار (راجع [الورقة](https://arxiv.org/abs/2205.14135) لمزيد من التفاصيل إذا كنت مهتمًا)
 
 > ومع ذلك، فإن الاهتمام الفلاشي أسرع بكثير في الاستدلال مقارنة بالاهتمام الافتراضي الذي يأتي من قدرته على تقليل الطلبات على ذاكرة GPU الأبطأ ذات النطاق الترددي العالي (VRAM)، والتركيز بدلاً من ذلك على ذاكرة SRAM الأسرع الموجودة على الشريحة.
 
@@ -389,7 +389,7 @@ long_prompt = 10 * system_prompt + prompt
 نقوم بتنفيذ نموذجنا مرة أخرى بدقة bfloat16.
 
 ```python
-model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", dtype=torch.bfloat16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", torch_dtype=torch.bfloat16, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained("bigcode/octocoder")
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
@@ -535,20 +535,20 @@ flush()
 لكي يفهم LLM ترتيب الجملة، يلزم وجود *إشارة* إضافية ويتم تطبيقها عادةً في شكل *الترميزات الموضعية* (أو ما يُطلق عليه أيضًا *الترميزات الموضعية*).
 لم يتم ترجمة النص الخاص والروابط وأكواد HTML وCSS بناءً على طلبك.
 
-قدم مؤلفو الورقة البحثية [*Attention Is All You Need*](https://huggingface.co/papers/1706.03762) تضمينات موضعية جيبية مثلثية \\( \mathbf{P} = \mathbf{p}_1, \ldots, \mathbf{p}_N \\) حيث يتم حساب كل متجه \\( \mathbf{p}_i \\) كدالة جيبية لموضعه \\( i \\) .
+قدم مؤلفو الورقة البحثية [*Attention Is All You Need*](https://arxiv.org/abs/1706.03762) تضمينات موضعية جيبية مثلثية \\( \mathbf{P} = \mathbf{p}_1, \ldots, \mathbf{p}_N \\) حيث يتم حساب كل متجه \\( \mathbf{p}_i \\) كدالة جيبية لموضعه \\( i \\) .
 بعد ذلك يتم ببساطة إضافة التضمينات الموضعية إلى متجهات تسلسل الإدخال \\( \mathbf{\hat{X}} = \mathbf{\hat{x}}_1, \ldots, \mathbf{\hat{x}}_N \\) = \\( \mathbf{x}_1 + \mathbf{p}_1, \ldots, \mathbf{x}_N + \mathbf{p}_N \\) وبالتالي توجيه النموذج لتعلم ترتيب الجملة بشكل أفضل.
 
-بدلاً من استخدام التضمينات الموضعية الثابتة، استخدم آخرون (مثل [Devlin et al.](https://huggingface.co/papers/1810.04805)) تضمينات موضعية مكتسبة يتم من خلالها تعلم التضمينات الموضعية \\( \mathbf{P} \\) أثناء التدريب.
+بدلاً من استخدام التضمينات الموضعية الثابتة، استخدم آخرون (مثل [Devlin et al.](https://arxiv.org/abs/1810.04805)) تضمينات موضعية مكتسبة يتم من خلالها تعلم التضمينات الموضعية \\( \mathbf{P} \\) أثناء التدريب.
 
 كانت التضمينات الموضعية الجيبية والمكتسبة هي الطرق السائدة لترميز ترتيب الجملة في نماذج اللغة الكبيرة، ولكن تم العثور على بعض المشكلات المتعلقة بهذه التضمينات الموضعية:
 
-1. التضمينات الموضعية الجيبية والمكتسبة هي تضمينات موضعية مطلقة، أي ترميز تضمين فريد لكل معرف موضعي: \\( 0, \ldots, N \\) . كما أظهر [Huang et al.](https://huggingface.co/papers/2009.13658) و [Su et al.](https://huggingface.co/papers/2104.09864)، تؤدي التضمينات الموضعية المطلقة إلى أداء ضعيف لنماذج اللغة الكبيرة للمدخلات النصية الطويلة. بالنسبة للمدخلات النصية الطويلة، يكون من المفيد إذا تعلم النموذج المسافة الموضعية النسبية التي تمتلكها رموز المدخلات إلى بعضها البعض بدلاً من موضعها المطلق.
+1. التضمينات الموضعية الجيبية والمكتسبة هي تضمينات موضعية مطلقة، أي ترميز تضمين فريد لكل معرف موضعي: \\( 0, \ldots, N \\) . كما أظهر [Huang et al.](https://arxiv.org/abs/2009.13658) و [Su et al.](https://arxiv.org/abs/2104.09864)، تؤدي التضمينات الموضعية المطلقة إلى أداء ضعيف لنماذج اللغة الكبيرة للمدخلات النصية الطويلة. بالنسبة للمدخلات النصية الطويلة، يكون من المفيد إذا تعلم النموذج المسافة الموضعية النسبية التي تمتلكها رموز المدخلات إلى بعضها البعض بدلاً من موضعها المطلق.
 2. عند استخدام التضمينات الموضعية المكتسبة، يجب تدريب نموذج اللغة الكبيرة على طول إدخال ثابت \\( N \\)، مما يجعل من الصعب الاستقراء إلى طول إدخال أطول مما تم تدريبه عليه.
 
 في الآونة الأخيرة، أصبحت التضمينات الموضعية النسبية التي يمكنها معالجة المشكلات المذكورة أعلاه أكثر شعبية، وأبرزها:
 
--   [تضمين الموضع الدوراني (RoPE)](https://huggingface.co/papers/2104.09864)
--   [ALiBi](https://huggingface.co/papers/2108.12409)
+-   [تضمين الموضع الدوراني (RoPE)](https://arxiv.org/abs/2104.09864)
+-   [ALiBi](https://arxiv.org/abs/2108.12409)
 
 يؤكد كل من *RoPE* و *ALiBi* أنه من الأفضل توجيه نموذج اللغة الكبيرة حول ترتيب الجملة مباشرة في خوارزمية الانتباه الذاتي حيث يتم وضع رموز الكلمات في علاقة مع بعضها البعض. على وجه التحديد، يجب توجيه ترتيب الجملة عن طريق تعديل عملية \\( \mathbf{QK}^T \\) .
 
@@ -563,14 +563,14 @@ $$ \mathbf{\hat{q}}_i^T \mathbf{\hat{x}}_j = \mathbf{{q}}_i^T \mathbf{R}_{\theta
 يستخدم *RoPE* في العديد من نماذج اللغة الكبيرة الأكثر أهمية اليوم، مثل:
 
 -   [**Falcon**](https://huggingface.co/tiiuae/falcon-40b)
--   [**Llama**](https://huggingface.co/papers/2302.13971)
--   [**PaLM**](https://huggingface.co/papers/2204.02311)
+-   [**Llama**](https://arxiv.org/abs/2302.13971)
+-   [**PaLM**](https://arxiv.org/abs/2204.02311)
 
 كبديل، يقترح *ALiBi* مخطط ترميز موضعي نسبي أبسط بكثير. يتم إضافة المسافة النسبية التي تمتلكها رموز المدخلات إلى بعضها البعض كعدد صحيح سلبي مقياس بقيمة محددة مسبقًا `m` إلى كل إدخال استعلام-مفتاح لمصفوفة \\( \mathbf{QK}^T \\) مباشرة قبل حساب softmax.
 
 ![](/blog/assets/163_optimize_llm/alibi.png)
 
-كما هو موضح في ورقة [ALiBi](https://huggingface.co/papers/2108.12409)، يسمح هذا الترميز الموضعي النسبي البسيط للنموذج بالحفاظ على أداء عالٍ حتى في تسلسلات المدخلات النصية الطويلة جدًا.
+كما هو موضح في ورقة [ALiBi](https://arxiv.org/abs/2108.12409)، يسمح هذا الترميز الموضعي النسبي البسيط للنموذج بالحفاظ على أداء عالٍ حتى في تسلسلات المدخلات النصية الطويلة جدًا.
 
 يُستخدم *ALiBi* في العديد من أهم نماذج اللغة الكبيرة المستخدمة اليوم، مثل:
 
@@ -579,7 +579,7 @@ $$ \mathbf{\hat{q}}_i^T \mathbf{\hat{x}}_j = \mathbf{{q}}_i^T \mathbf{R}_{\theta
 
 يمكن لكل من ترميزات الموضع *RoPE* و *ALiBi* الاستقراء إلى أطوال إدخال لم يتم ملاحظتها أثناء التدريب، في حين ثبت أن الاستقراء يعمل بشكل أفضل بكثير خارج الصندوق لـ *ALiBi* مقارنة بـ *RoPE*.
 بالنسبة لـ ALiBi، ما عليك سوى زيادة قيم مصفوفة الموضع المثلث السفلي لمطابقة طول تسلسل الإدخال.
-بالنسبة لـ *RoPE*، يؤدي الحفاظ على نفس \\( \theta \\) الذي تم استخدامه أثناء التدريب إلى نتائج سيئة عند تمرير إدخالات نصية أطول بكثير من تلك التي شوهدت أثناء التدريب، راجع [Press et al.](https://huggingface.co/papers/2108.12409). ومع ذلك، وجد المجتمع بعض الحيل الفعالة التي تقوم بتعديل \\( \theta \\)، مما يسمح لترميزات الموضع *RoPE* بالعمل بشكل جيد لتسلسلات إدخال النص المستقرئة (راجع [هنا](https://github.com/huggingface/transformers/pull/24653)).
+بالنسبة لـ *RoPE*، يؤدي الحفاظ على نفس \\( \theta \\) الذي تم استخدامه أثناء التدريب إلى نتائج سيئة عند تمرير إدخالات نصية أطول بكثير من تلك التي شوهدت أثناء التدريب، راجع [Press et al.](https://arxiv.org/abs/2108.12409). ومع ذلك، وجد المجتمع بعض الحيل الفعالة التي تقوم بتعديل \\( \theta \\)، مما يسمح لترميزات الموضع *RoPE* بالعمل بشكل جيد لتسلسلات إدخال النص المستقرئة (راجع [هنا](https://github.com/huggingface/transformers/pull/24653)).
 
 > كل من RoPE و ALiBi عبارة عن ترميزات موضع نسبي *لا* يتم تعلمها أثناء التدريب، ولكن بدلاً من ذلك تستند إلى الحدس التالي:
  -   يجب إعطاء الإشارات الموضعية حول إدخالات النص مباشرة إلى مصفوفة \\( QK^T \\) لطبقة الاهتمام الذاتي
@@ -755,21 +755,21 @@ Roughly 8 مليار قيمة عائمة! يتطلب تخزين 8 مليارات
 
 #### 3.2.2 Multi-Query-Attention (MQA)
 
-[Multi-Query-Attention](https://huggingface.co/papers/1911.02150) اقترحها Noam Shazeer في ورقته *Fast Transformer Decoding: One Write-Head is All You Need*. كما يقول العنوان، اكتشف Noam أنه بدلاً من استخدام `n_head` من أوزان إسقاط القيمة الرئيسية، يمكن استخدام زوج واحد من أوزان إسقاط رأس القيمة التي يتم مشاركتها عبر جميع رؤوس الاهتمام دون أن يتدهور أداء النموذج بشكل كبير.
+[Multi-Query-Attention](https://arxiv.org/abs/1911.02150) اقترحها Noam Shazeer في ورقته *Fast Transformer Decoding: One Write-Head is All You Need*. كما يقول العنوان، اكتشف Noam أنه بدلاً من استخدام `n_head` من أوزان إسقاط القيمة الرئيسية، يمكن استخدام زوج واحد من أوزان إسقاط رأس القيمة التي يتم مشاركتها عبر جميع رؤوس الاهتمام دون أن يتدهور أداء النموذج بشكل كبير.
 
 > باستخدام زوج واحد من أوزان إسقاط رأس القيمة، يجب أن تكون متجهات القيمة الرئيسية \\( \mathbf{k}_i، \mathbf{v}_i \\) متطابقة عبر جميع رؤوس الاهتمام والتي بدورها تعني أننا بحاجة فقط إلى تخزين زوج إسقاط قيمة رئيسي واحد في ذاكرة التخزين المؤقت بدلاً من `n_head` منها.
 
 نظرًا لأن معظم LLMs تستخدم ما بين 20 و100 رأس اهتمام، فإن MQA يقلل بشكل كبير من استهلاك الذاكرة لذاكرة التخزين المؤقت key-value. بالنسبة إلى LLM المستخدم في هذا الدفتر، يمكننا تقليل استهلاك الذاكرة المطلوبة من 15 جيجابايت إلى أقل من 400 ميجابايت عند طول تسلسل الإدخال 16000.
 
 بالإضافة إلى توفير الذاكرة، يؤدي MQA أيضًا إلى تحسين الكفاءة الحسابية كما هو موضح في ما يلي.
-في فك التشفير التلقائي، يجب إعادة تحميل متجهات القيمة الرئيسية الكبيرة، ودمجها مع زوج متجه القيمة الحالي، ثم إدخالها في \\( \mathbf{q}_c\mathbf{K}^T \\) الحساب في كل خطوة. بالنسبة لفك التشفير التلقائي، يمكن أن تصبح عرض النطاق الترددي للذاكرة المطلوبة لإعادة التحميل المستمر عنق زجاجة زمنيًا خطيرًا. من خلال تقليل حجم متجهات القيمة الرئيسية، يجب الوصول إلى ذاكرة أقل، وبالتالي تقليل عنق الزجاجة في عرض النطاق الترددي للذاكرة. لمزيد من التفاصيل، يرجى إلقاء نظرة على [ورقة Noam](https://huggingface.co/papers/1911.02150).
+في فك التشفير التلقائي، يجب إعادة تحميل متجهات القيمة الرئيسية الكبيرة، ودمجها مع زوج متجه القيمة الحالي، ثم إدخالها في \\( \mathbf{q}_c\mathbf{K}^T \\) الحساب في كل خطوة. بالنسبة لفك التشفير التلقائي، يمكن أن تصبح عرض النطاق الترددي للذاكرة المطلوبة لإعادة التحميل المستمر عنق زجاجة زمنيًا خطيرًا. من خلال تقليل حجم متجهات القيمة الرئيسية، يجب الوصول إلى ذاكرة أقل، وبالتالي تقليل عنق الزجاجة في عرض النطاق الترددي للذاكرة. لمزيد من التفاصيل، يرجى إلقاء نظرة على [ورقة Noam](https://arxiv.org/abs/1911.02150).
 
 الجزء المهم الذي يجب فهمه هنا هو أن تقليل عدد رؤوس الاهتمام بالقيمة الرئيسية إلى 1 لا معنى له إلا إذا تم استخدام ذاكرة التخزين المؤقت للقيمة الرئيسية. يظل الاستهلاك الذروي لذاكرة النموذج لمرور واحد للأمام بدون ذاكرة التخزين المؤقت للقيمة الرئيسية دون تغيير لأن كل رأس اهتمام لا يزال لديه متجه استعلام فريد بحيث يكون لكل رأس اهتمام مصفوفة \\( \mathbf{QK}^T \\) مختلفة.
 
 شهدت MQA اعتمادًا واسع النطاق من قبل المجتمع ويتم استخدامها الآن بواسطة العديد من LLMs الأكثر شهرة:
 
 -   [**Falcon**](https://huggingface.co/tiiuae/falcon-40b)
--   [**PaLM**](https://huggingface.co/papers/2204.02311)
+-   [**PaLM**](https://arxiv.org/abs/2204.02311)
 -   [**MPT**](https://huggingface.co/mosaicml/mpt-30b)
 -   [**BLOOM**](https://huggingface.co/bigscience/bloom)
 
@@ -777,7 +777,7 @@ Roughly 8 مليار قيمة عائمة! يتطلب تخزين 8 مليارات
 
 #### 3.2.3 مجموعة الاستعلام الاهتمام (GQA)
 
-[مجموعة الاستعلام الاهتمام](https://huggingface.co/papers/2305.13245)، كما اقترح Ainslie et al. من Google، وجد أن استخدام MQA يمكن أن يؤدي غالبًا إلى تدهور الجودة مقارنة باستخدام إسقاطات رأس القيمة الرئيسية المتعددة. تجادل الورقة بأنه يمكن الحفاظ على أداء النموذج بشكل أكبر عن طريق تقليل عدد أوزان إسقاط رأس الاستعلام بشكل أقل حدة. بدلاً من استخدام وزن إسقاط قيمة رئيسية واحدة فقط، يجب استخدام `n <n_head` أوزان إسقاط قيمة رئيسية. من خلال اختيار `n` إلى قيمة أقل بكثير من `n_head`، مثل 2 أو 4 أو 8، يمكن الاحتفاظ بمعظم مكاسب الذاكرة والسرعة من MQA مع التضحية بقدر أقل من سعة النموذج وبالتالي، من المفترض، أقل أداء.
+[مجموعة الاستعلام الاهتمام](https://arxiv.org/abs/2305.13245)، كما اقترح Ainslie et al. من Google، وجد أن استخدام MQA يمكن أن يؤدي غالبًا إلى تدهور الجودة مقارنة باستخدام إسقاطات رأس القيمة الرئيسية المتعددة. تجادل الورقة بأنه يمكن الحفاظ على أداء النموذج بشكل أكبر عن طريق تقليل عدد أوزان إسقاط رأس الاستعلام بشكل أقل حدة. بدلاً من استخدام وزن إسقاط قيمة رئيسية واحدة فقط، يجب استخدام `n <n_head` أوزان إسقاط قيمة رئيسية. من خلال اختيار `n` إلى قيمة أقل بكثير من `n_head`، مثل 2 أو 4 أو 8، يمكن الاحتفاظ بمعظم مكاسب الذاكرة والسرعة من MQA مع التضحية بقدر أقل من سعة النموذج وبالتالي، من المفترض، أقل أداء.
 
 علاوة على ذلك، اكتشف مؤلفو GQA أنه يمكن *تدريب* نقاط تفتيش النموذج الموجودة ليكون لها بنية GQA باستخدام 5% فقط من الحوسبة الأصلية للتعليم المسبق. في حين أن 5% من الحوسبة الأصلية للتعليم المسبق يمكن أن تكون كمية هائلة، يسمح GQA *uptraining* بنقاط تفتيش موجودة للاستفادة من تسلسلات الإدخال الأطول.
 
@@ -789,7 +789,7 @@ Roughly 8 مليار قيمة عائمة! يتطلب تخزين 8 مليارات
 
 ## الخاتمة
 
-مجتمع البحث يأتي باستمرار بطرق جديدة ومبتكرة لتسريع وقت الاستدلال للنماذج اللغوية الكبيرة على الإطلاق. كمثال، أحد اتجاهات البحث الواعدة هو [فك التشفير التخميني](https://huggingface.co/papers/2211.17192) حيث تقوم "الرموز السهلة" بإنشائها نماذج اللغة الأصغر والأسرع ويتم إنشاء "الرموز الصعبة" فقط بواسطة LLM نفسه. إن التعمق في التفاصيل يتجاوز نطاق هذا الدفتر، ولكن يمكن قراءته في هذه [تدوينة المدونة اللطيفة](https://huggingface.co/blog/assisted-generation).
+مجتمع البحث يأتي باستمرار بطرق جديدة ومبتكرة لتسريع وقت الاستدلال للنماذج اللغوية الكبيرة على الإطلاق. كمثال، أحد اتجاهات البحث الواعدة هو [فك التشفير التخميني](https://arxiv.org/abs/2211.17192) حيث تقوم "الرموز السهلة" بإنشائها نماذج اللغة الأصغر والأسرع ويتم إنشاء "الرموز الصعبة" فقط بواسطة LLM نفسه. إن التعمق في التفاصيل يتجاوز نطاق هذا الدفتر، ولكن يمكن قراءته في هذه [تدوينة المدونة اللطيفة](https://huggingface.co/blog/assisted-generation).
 
 السبب في أن LLMs الضخمة مثل GPT3/4، وLlama-2-70b، وClaude، وPaLM يمكن أن تعمل بسرعة كبيرة في واجهات الدردشة مثل [Hugging Face Chat](https://huggingface.co/chat/) أو ChatGPT يرجع إلى حد كبير إلى التحسينات المذكورة أعلاه في الدقة والخوارزميات والهندسة المعمارية.
 في المستقبل، ستكون أجهزة التسريع مثل وحدات معالجة الرسومات (GPUs) ووحدات معالجة الرسومات (TPUs)، وما إلى ذلك... ستكون أسرع فقط وستسمح بمزيد من الذاكرة، ولكن يجب دائمًا التأكد من استخدام أفضل الخوارزميات والهندسة المعمارية المتاحة للحصول على أكبر قدر من المال

docs/source/ar/model_memory_anatomy.md

@@ -165,7 +165,7 @@ default_args = {
 
 يمكن أن تكون هذه المعرفة مفيدة لمعرفة عند تحليل اختناقات الأداء.
 
-هذا الملخص مُشتق من [نقل البيانات هو كل ما تحتاجه: دراسة حالة حول تحسين المحولات 2020](https://huggingface.co/papers/2007.00072)
+هذا الملخص مُشتق من [نقل البيانات هو كل ما تحتاجه: دراسة حالة حول تحسين المحولات 2020](https://arxiv.org/abs/2007.00072)
 
 
 ## تشريح ذاكرة النموذج

docs/source/ar/model_sharing.md

@@ -41,7 +41,7 @@ picture-in-picture" allowfullscreen></iframe>
 قبل مشاركة نموذج على Hub، ستحتاج إلى بيانات اعتماد حساب Hugging Face الخاصة بك.  إذا كنت تستخدم منصة الأوامر، فقم بتشغيل الأمر التالي في بيئة افتراضية حيث تم تثبيت 🤗 Transformers. سيقوم هذا الأمر بتخزين رمز الدخول الخاص بك في مجلد تخزين المؤقت لـ Hugging Face (`~/.cache/` بشكل افتراضي):
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 إذا كنت تستخدم دفتر ملاحظات مثل Jupyter أو Colaboratory، فتأكد من تثبيت مكتبة [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library). تسمح لك هذه المكتبة بالتفاعل برمجيًا مع Hub.

docs/source/ar/model_summary.md

@@ -1,6 +1,6 @@
 # عائلة نماذج المحول
 
-منذ إطلاقه في عام 2017، ألهم نموذج [المحول الأصلي](https://huggingface.co/papers/1706.03762) (راجع مدونة [المحول المشروح](http://nlp.seas.harvard.edu/2018/04/03/attention.html) لمقدمة تقنية مبسطة)، ألهم العديد من النماذج الجديدة والمبتكرة التي تتجاوز مهام معالجة اللغات الطبيعية (NLP). هناك نماذج للتنبؤ [بالبنية البروتينات المطوية](https://huggingface.co/blog/deep-learning-with-proteins)، و[تدريب على اتخاذ القرار](https://huggingface.co/blog/train-decision-transformers)، و[التنبؤ بالسلاسل الزمنية](https://huggingface.co/blog/time-series-transformers). مع وجود العديد من متغيرات المحول المتاحة، قد يكون من السهل أن تفوتك الصورة الأكبر. ما تشترك فيه جميع هذه النماذج هو أنها تستند إلى بنية المحول الأصلية. تستخدم بعض النماذج فقط الترميز أو فك الترميز، بينما تستخدم نماذج أخرى كليهما. يوفر هذا تصنيفًا مفيدًا لتصنيف واستعراض الفروقات الرئيسية بين نماذج عائلة المحولات، وسيساعدك على فهم النماذج التي لم تصادفها من قبل.
+منذ إطلاقه في عام 2017، ألهم نموذج [المحول الأصلي](https://arxiv.org/abs/1706.03762) (راجع مدونة [المحول المشروح](http://nlp.seas.harvard.edu/2018/04/03/attention.html) لمقدمة تقنية مبسطة)، ألهم العديد من النماذج الجديدة والمبتكرة التي تتجاوز مهام معالجة اللغات الطبيعية (NLP). هناك نماذج للتنبؤ [بالبنية البروتينات المطوية](https://huggingface.co/blog/deep-learning-with-proteins)، و[تدريب على اتخاذ القرار](https://huggingface.co/blog/train-decision-transformers)، و[التنبؤ بالسلاسل الزمنية](https://huggingface.co/blog/time-series-transformers). مع وجود العديد من متغيرات المحول المتاحة، قد يكون من السهل أن تفوتك الصورة الأكبر. ما تشترك فيه جميع هذه النماذج هو أنها تستند إلى بنية المحول الأصلية. تستخدم بعض النماذج فقط الترميز أو فك الترميز، بينما تستخدم نماذج أخرى كليهما. يوفر هذا تصنيفًا مفيدًا لتصنيف واستعراض الفروقات الرئيسية بين نماذج عائلة المحولات، وسيساعدك على فهم النماذج التي لم تصادفها من قبل.
 
 إذا لم تكن على دراية بنموذج المحول الأصلي أو تحتاج إلى تذكير، فراجع الفصل الخاص بـ [كيف تعمل المحولات](https://huggingface.co/course/chapter1/4؟fw=pt) من دورة Hugging Face.
 
@@ -14,7 +14,7 @@
 
 ### الشبكة التلافيفية (Convolutional network)
 
-لطالما كانت الشبكات التلافيفية (CNNs) الطريقة السائدة لمهام رؤية الحاسب حتى برز [محول الرؤية](https://huggingface.co/papers/2010.11929) قابليته للتطوير وكفاءته العالية. وحتى بعد ذلك، لا تزال بعض أفضل صفات CNN، مثل ثبات الإزاحة، قوية جدًا (خاصة بالنسبة لمهام معينة) لدرجة أن بعض المحولات تدمج التلافيف في بنيتها. قلب [ConvNeXt](model_doc/convnext) هذا التبادل رأسًا على عقب وأدرج خيارات التصميم من المحولات لتحديث CNN. على سبيل المثال، يستخدم ConvNeXt نوافذ منزلقة غير متداخلة لتقسيم الصورة إلى رقع وزيادة حقل مجال العام الخاص بها. كما يقوم ConvNeXt بعدة خيارات مثل تصميم الطبقة لتكون أكثر كفاءة في الذاكرة وتحسين الأداء، مما يجعله منافسًا قويًا للمحولات!
+لطالما كانت الشبكات التلافيفية (CNNs) الطريقة السائدة لمهام رؤية الحاسب حتى برز [محول الرؤية](https://arxiv.org/abs/2010.11929) قابليته للتطوير وكفاءته العالية. وحتى بعد ذلك، لا تزال بعض أفضل صفات CNN، مثل ثبات الإزاحة، قوية جدًا (خاصة بالنسبة لمهام معينة) لدرجة أن بعض المحولات تدمج التلافيف في بنيتها. قلب [ConvNeXt](model_doc/convnext) هذا التبادل رأسًا على عقب وأدرج خيارات التصميم من المحولات لتحديث CNN. على سبيل المثال، يستخدم ConvNeXt نوافذ منزلقة غير متداخلة لتقسيم الصورة إلى رقع وزيادة حقل مجال العام الخاص بها. كما يقوم ConvNeXt بعدة خيارات مثل تصميم الطبقة لتكون أكثر كفاءة في الذاكرة وتحسين الأداء، مما يجعله منافسًا قويًا للمحولات!
 
 ### الترميز[[cv-encoder]] (Encoder)
 
@@ -40,7 +40,7 @@
 
 نموذج [BERT](model_doc/bert) هو محوّل (Transformer)  يعتمد على الترميز فقط يقوم بشكل عشوائي بإخفاء رموز معينة في المدخلات لتجنب رؤية باقى الرموز الأخرى، مما يسمح له "بالغش". يتمثل هدف التدريب المسبق في التنبؤ بالرمز المخفي بناءً على السياق. يسمح هذا لـ BERT باستخدام السياقات اليمنى واليسرى بالكامل لمساعدته في تعلم تمثيل أعمق وأغنى للبيانات المدخلة. ومع ذلك، كان هناك مجال للتحسين في استراتيجية التدريب المسبق لـ BERT. نموذج [RoBERTa](model_doc/roberta) اضاف تحسين من خلال تقديم وصفة تدريب مسبق جديدة تشمل التدريب لفترة أطول وعلى دفعات أكبر، وإخفاء الرموز عشوائيًا في كل حقبة بدلاً من مرة واحدة فقط أثناء المعالجة المسبقة، وإزالة هدف التنبؤ بالجملة التالية.
 
-تتمثل الاستراتيجية السائدة لتحسين الأداء في زيادة حجم النموذج. ولكن تدريب النماذج الكبيرة مكلف من الناحية الحسابية. إحدى طرق تقليل التكاليف الحسابية هي استخدام نموذج أصغر مثل [DistilBERT](model_doc/distilbert). يستخدم DistilBERT [ تقنية تقطير المعرفة](https://huggingface.co/papers/1503.02531) - وهي تقنية ضغط - لإنشاء نموذج أصغر من BERT مع الحفاظ على معظم قدراته على فهم اللغةا.
+تتمثل الاستراتيجية السائدة لتحسين الأداء في زيادة حجم النموذج. ولكن تدريب النماذج الكبيرة مكلف من الناحية الحسابية. إحدى طرق تقليل التكاليف الحسابية هي استخدام نموذج أصغر مثل [DistilBERT](model_doc/distilbert). يستخدم DistilBERT [ تقنية تقطير المعرفة](https://arxiv.org/abs/1503.02531) - وهي تقنية ضغط - لإنشاء نموذج أصغر من BERT مع الحفاظ على معظم قدراته على فهم اللغةا.
 
 مرت معظم نماذج المحول في الاتجاه نحو المزيد من المعلمات، مما أدى إلى ظهور نماذج جديدة تركز على تحسين كفاءة التدريب. يقلّل [ALBERT](model_doc/albert) من استهلاك الذاكرة عن طريق تقليل عدد المعلمات بطريقتين: فصل تضمين المفردات الأكبر إلى مصفوفتين أصغر والسماح للمستويات بمشاركة المعلمات. أضاف [DeBERTa](model_doc/deberta) آلية انتباه منفصلة حيث يتم ترميز الكلمة وموضعها بشكل منفصل في متجهين. يتم حساب الانتباه من هذه المتجهات المنفصلة بدلاً من متجه واحد يحتوي على تضمين الكلمة والموقع. ركز [Longformer](model_doc/longformer) أيضًا على جعل الانتباه أكثر كفاءة، خاصة لمعالجة المستندات ذات تسلسلات أطولل. فهو يستخدم مزيجًا من  انتباه النوافذ المحلية (يتم حساب الانتباه فقط ن نافذة ذات حجم ثابت حول كل رمز) والانتباه العام (فقط لرموز مهمة محددة مثل `[CLS]` للتصنيف) لإنشاء مصفوفة انتباه متفرقة بدلاً من مصفوفة انتباه كاملة.
 

docs/source/ar/peft.md

@@ -33,7 +33,7 @@ pip install git+https://github.com/huggingface/peft.git
 
 - [محولات الرتبة المنخفضة](https://huggingface.co/docs/peft/conceptual_guides/lora)
 - [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
-- [AdaLoRA](https://huggingface.co/papers/2303.10512)
+- [AdaLoRA](https://arxiv.org/abs/2303.10512)
 
 إذا كنت تريد استخدام طرق PEFT الأخرى، مثل تعلم المحث أو ضبط المحث، أو حول مكتبة 🤗 PEFT بشكل عام، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/peft/index).
 

docs/source/ar/pipeline_tutorial.md

@@ -90,7 +90,7 @@ out = transcriber(...)  # سيتم الرجوع إلى استخدام `my_parame
 transcriber = pipeline(model="openai/whisper-large-v2", device=0)
 ```
 
-إذا كان النموذج كبيرًا جدًا بالنسبة لوحدة معالجة الرسومات (GPU) واحدة، وأنت تستخدم PyTorch، فيمكنك تعيين `dtype='float16'` لتمكين الاستدلال بدقة FP16. عادةً ما لا يتسبب ذلك في حدوث انخفاضات كبيرة في الأداء، ولكن تأكد من تقييمه على نماذجك!
+إذا كان النموذج كبيرًا جدًا بالنسبة لوحدة معالجة الرسومات (GPU) واحدة، وأنت تستخدم PyTorch، فيمكنك تعيين `torch_dtype='float16'` لتمكين الاستدلال بدقة FP16. عادةً ما لا يتسبب ذلك في حدوث انخفاضات كبيرة في الأداء، ولكن تأكد من تقييمه على نماذجك!
 
 بدلاً من ذلك، يمكنك تعيين `device_map="auto"` لتحديد كيفية تحميل مخزنات النموذج وتخزينها تلقائيًا. يتطلب استخدام معامل `device_map` مكتبه 🤗 [Accelerate](https://huggingface.co/docs/accelerate):
 
@@ -273,7 +273,7 @@ pip install pytesseract
 import torch
 from transformers import pipeline
 
-pipe = pipeline(model="facebook/opt-1.3b", dtype=torch.bfloat16, device_map="auto")
+pipe = pipeline(model="facebook/opt-1.3b", torch_dtype=torch.bfloat16, device_map="auto")
 output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
 ```
 

docs/source/ar/run_scripts.md

@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 يمكن لجميع النصوص البرمجية رفع نموذجك النهائي إلى [مركز النماذج](https://huggingface.co/models). تأكد من تسجيل الدخول إلى Hugging Face قبل البدء:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 ثم أضف المعلمة `push_to_hub` إلى النص البرمجي . ستقوم هذه المعلمة بإنشاء مستودع باستخدام اسم مستخدم Hugging Face واسم المجلد المحدد في `output_dir`.

docs/source/ar/tasks_explained.md

@@ -103,7 +103,7 @@
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convolution.gif"/>
 </div>
 
-<small>عملية التفاف أساسية بدون حشو أو خطو خطوة واسعة، مأخوذة من  <a href="https://huggingface.co/papers/1603.07285">دليل لحساب الالتفاف للتعلم العميق.</a></small>
+<small>عملية التفاف أساسية بدون حشو أو خطو خطوة واسعة، مأخوذة من  <a href="https://arxiv.org/abs/1603.07285">دليل لحساب الالتفاف للتعلم العميق.</a></small>
 
 يمكنك تغذية هذا الناتج إلى طبقة التفاف أخرى،  ومع كل طبقة متتالية، تتعلم الشبكة أشياء أكثر تعقيدًا وتجريدية مثل النقانق أو الصواريخ. بين طبقات الالتفاف، من الشائع إضافة طبقة تجميع لتقليل الأبعاد وجعل النموذج أكثر قوة للتغيرات في موضع الميزة.
 

docs/source/ar/tokenizer_summary.md

@@ -94,7 +94,7 @@
 
 ### ترميز الأزواج البايتية (BPE)
 
-تم تقديم رميز أزواج البايت (BPE) في ورقة بحثية بعنوان [الترجمة الآلية العصبية للكلمات النادرة باستخدام وحدات subword (Sennrich et al.، 2015)](https://huggingface.co/papers/1508.07909). يعتمد BPE على مُجزّئ أولي يقسم بيانات التدريب إلى
+تم تقديم رميز أزواج البايت (BPE) في ورقة بحثية بعنوان [الترجمة الآلية العصبية للكلمات النادرة باستخدام وحدات subword (Sennrich et al.، 2015)](https://arxiv.org/abs/1508.07909). يعتمد BPE على مُجزّئ أولي يقسم بيانات التدريب إلى
 كلمات. يمكن أن يكون التحليل المسبق بسيطًا مثل التقسيم المكاني، على سبيل المثال [GPT-2](model_doc/gpt2)، [RoBERTa](model_doc/roberta). تشمل التقسيم الأكثر تقدمًا معتمد على التحليل القائم على القواعد، على سبيل المثال [XLM](model_doc/xlm)، [FlauBERT](model_doc/flaubert) الذي يستخدم Moses لمعظم اللغات، أو [GPT](model_doc/openai-gpt) الذي يستخدم spaCy و ftfy، لحساب تكرار كل كلمة في مجموعة بيانات التدريب.
 
 بعد التحليل المسبق، يتم إنشاء مجموعة من الكلمات الفريدة وقد تم تحديد تكرار كل كلمة في تم تحديد بيانات التدريب. بعد ذلك، يقوم BPE بإنشاء مفردات أساسية تتكون من جميع الرموز التي تحدث في مجموعة الكلمات الفريدة ويتعلم قواعد الدمج لتشكيل رمز جديد من رمزين من المفردات الأساسية. إنه يفعل ذلك حتى تصل المفردات إلى حجم المفردات المطلوب. لاحظ أن حجم المفردات هو فرط معلمة لتحديد قبل تدريب مُجزّئ  النصوص.
@@ -158,7 +158,7 @@ BPE. أولاً، يقوم WordPiece بتكوين المفردات لتضمين
 ### Unigram
 
 Unigram هو خوارزمية توكنيز subword التي تم تقديمها في [تنظيم subword: تحسين نماذج الترجمة الشبكة العصبية
-نماذج مع مرشحين subword متعددة (Kudo، 2018)](https://huggingface.co/papers/1804.10959). على عكس BPE أو
+نماذج مع مرشحين subword متعددة (Kudo، 2018)](https://arxiv.org/pdf/1804.10959.pdf). على عكس BPE أو
 WordPiece، يقوم Unigram بتكوين مفرداته الأساسية إلى عدد كبير من الرموز ويقللها تدريجياً للحصول على مفردات أصغر. يمكن أن تتوافق المفردات الأساسية على سبيل المثال مع جميع الكلمات المسبقة التوكنز والسلاسل الفرعية الأكثر شيوعًا. لا يتم استخدام Unigram مباشرة لأي من النماذج في المحولات، ولكنه يستخدم بالاقتران مع [SentencePiece](#sentencepiece).
 
 في كل خطوة تدريب، يحدد خوارزمية Unigram خسارة (غالبًا ما يتم تعريفها على أنها اللوغاريتم) عبر بيانات التدريب بالنظر إلى المفردات الحالية ونموذج اللغة unigram. بعد ذلك، بالنسبة لكل رمز في المفردات، يحسب الخوارزمية مقدار زيادة الخسارة الإجمالية إذا تم إزالة الرمز من المفردات. ثم يقوم Unigram بإزالة p (مع p عادة ما تكون 10% أو 20%) في المائة من الرموز التي تكون زيادة الخسارة فيها هي الأدنى، *أي* تلك
@@ -188,7 +188,7 @@ $$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )
 
 تحتوي جميع خوارزميات توكنز الموصوفة حتى الآن على نفس المشكلة: من المفترض أن النص المدخل يستخدم المسافات لفصل الكلمات. ومع ذلك، لا تستخدم جميع اللغات المسافات لفصل الكلمات. أحد الحلول الممكنة هو استخداممعالج مسبق للغة محدد، *مثال* [XLM](model_doc/xlm) يلذي يستخدم معالجات مسبقة محددة للصينية واليابانية والتايلاندية.
 لحل هذه المشكلة بشكل أعم، [SentencePiece: A simple and language independent subword tokenizer and
-detokenizer for Neural Text Processing (Kudo et al.، 2018)](https://huggingface.co/papers/1808.06226) يتعامل مع المدخلات
+detokenizer for Neural Text Processing (Kudo et al.، 2018)](https://arxiv.org/pdf/1808.06226.pdf) يتعامل مع المدخلات
 كتدفق بيانات خام، وبالتالي يشمل المسافة في مجموعة الأحرف التي سيتم استخدامها. ثم يستخدم خوارزمية BPE أو unigram
 لبناء المفردات المناسبة.
 

docs/source/ar/trainer.md

@@ -306,48 +306,78 @@ pip install galore-torch
 ثم أضف ببساطة أحد `["galore_adamw"، "galore_adafactor"، "galore_adamw_8bit"]` في `optim` جنبًا إلى جنب مع `optim_target_modules`، والتي يمكن أن تكون قائمة من السلاسل أو التعبيرات النمطية regex أو المسار الكامل المطابق لأسماء الوحدات المستهدفة التي تريد تكييفها. فيما يلي مثال على النص البرمجي كامل(تأكد من `pip install trl datasets`):
 
 ```python
+import torch
 import datasets
-from trl import SFTConfig, SFTTrainer
+import trl
+
+from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM
 
 train_dataset = datasets.load_dataset('imdb', split='train')
-args = SFTConfig(
-    output_dir="./test-galore",
+
+args = TrainingArguments(
+    output_dir="./test-galore"،
     max_steps=100,
-    optim="galore_adamw",
-    optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
-    gradient_checkpointing=True,
+    per_device_train_batch_size=2,
+    optim="galore_adamw"،
+    optim_target_modules=[r".*.attn.*"، r".*.mlp.*"]
 )
-trainer = SFTTrainer(
-    model="google/gemma-2b",
+
+model_id = "google/gemma-2b"
+
+config = AutoConfig.from_pretrained(model_id)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_config(config).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model, 
     args=args,
     train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=512,
 )
+
 trainer.train()
 ```
 
 لتمرير معامﻻت إضافية يدعمها  GaLore، يجب عليك تمرير `optim_args` بشكل صحيح، على سبيل المثال:
 
 ```python
+import torch
 import datasets
-from trl import SFTConfig, SFTTrainer
+import trl
+
+from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM
 
 train_dataset = datasets.load_dataset('imdb', split='train')
-args = SFTConfig(
+
+args = TrainingArguments(
     output_dir="./test-galore",
     max_steps=100,
+    per_device_train_batch_size=2,
     optim="galore_adamw",
     optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
     optim_args="rank=64, update_proj_gap=100, scale=0.10",
-    gradient_checkpointing=True,
 )
-trainer = SFTTrainer(
-    model="google/gemma-2b",
+
+model_id = "google/gemma-2b"
+
+config = AutoConfig.from_pretrained(model_id)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_config(config).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model, 
     args=args,
     train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=512,
 )
+
 trainer.train()
 ```
-يمكنك قراءة المزيد حول الطريقة في [المستودع الأصلي](https://github.com/jiaweizzhao/GaLore) أو [الورقة البحثية](https://huggingface.co/papers/2403.03507).
+يمكنك قراءة المزيد حول الطريقة في [المستودع الأصلي](https://github.com/jiaweizzhao/GaLore) أو [الورقة البحثية](https://arxiv.org/abs/2403.03507).
 
 حاليًا، يمكنك فقط تدريب الطبقات الخطية التي تعتبر طبقات GaLore وستستخدم التحلل  ذو الرتبة المنخفضة للتدريب بينما سيتم تحسين الطبقات المتبقية بالطريقة التقليدية.
 
@@ -356,22 +386,37 @@ trainer.train()
 يمكنك أيضًا إجراء تحسين طبقة تلو الأخرى عن طريق إضافة `layerwise` إلى اسم المُحسِّن كما هو موضح أدناه:
 
 ```python
+import torch
 import datasets
-from trl import SFTConfig, SFTTrainer
+import trl
 
-train_dataset = datasets.load_dataset('imdb', split='train')
-args = SFTConfig(
-    output_dir="./test-galore",
-    max_steps=100,
-    optim="galore_adamw_layerwise",
-    optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
-    gradient_checkpointing=True,
+from transformers import TrainingArguments، AutoConfig، AutoTokenizer، AutoModelForCausalLM
+
+train_dataset = datasets.load_dataset('imdb'، split='train')
+
+args = TrainingArguments(
+    output_dir="./test-galore"،
+    max_steps=100،
+    per_device_train_batch_size=2،
+    optim="galore_adamw_layerwise"،
+    optim_target_modules=[r".*.attn.*"، r".*.mlp.*"]
 )
-trainer = SFTTrainer(
-    model="google/gemma-2b",
-    args=args,
-    train_dataset=train_dataset,
+
+model_id = "google/gemma-2b"
+
+config = AutoConfig.from_pretrained(model_id)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_config(config).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model،
+    args=args،
+    train_dataset=train_dataset،
+    dataset_text_field='text'،
+    max_seq_length=512،
 )
+
 trainer.train()
 ```
 
@@ -391,21 +436,39 @@ trainer.train()
 فيما يلي نص برمجي بسيط يوضح كيفية ضبط نموذج [google/gemma-2b](https://huggingface.co/google/gemma-2b) على مجموعة بيانات IMDB في الدقة الكاملة:
 
 ```python
+import torch
 import datasets
-from trl import SFTConfig, SFTTrainer
+from transformers import TrainingArguments، AutoTokenizer، AutoModelForCausalLM
+import trl
 
-train_dataset = datasets.load_dataset('imdb', split='train')
-args = SFTConfig(
-    output_dir="./test-lomo",
-    max_steps=100,
-    optim="adalomo",
-    gradient_checkpointing=True,
+train_dataset = datasets.load_dataset('imdb'، split='train')
+
+args = TrainingArguments(
+    output_dir="./test-lomo"،
+    max_steps=100،
+    per_device_train_batch_size=4،
+    optim="adalomo"،
+    gradient_checkpointing=True،
+    logging_strategy="steps"،
+    logging_steps=1،
+    learning_rate=2e-6،
+    save_strategy="no"،
+    run_name="lomo-imdb"،
 )
-trainer = SFTTrainer(
-    model="google/gemma-2b",
-    args=args,
-    train_dataset=train_dataset,
+
+model_id = "google/gemma-2b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id، low_cpu_mem_usage=True).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model،
+    args=args،
+    train_dataset=train_dataset،
+    dataset_text_field='text'،
+    max_seq_length=1024،
 )
+
 trainer.train()
 ```
 
@@ -440,7 +503,7 @@ args = TrainingArguments(
 # تحميل النموذج والمجزىء اللغوي
 model_id = "google/gemma-2b"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
+model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
 
 # تهيئة المدرب
 trainer = Trainer(
@@ -461,21 +524,39 @@ trainer.train()
 
 فيما يلي نص برمجى بسيط لشرح كيفية ضبط [google/gemma-2b](https://huggingface.co/google/gemma-2b) بدقة على مجموعة بيانات IMDB بدقة كاملة:
 ```python
+import torch
 import datasets
-from trl import SFTConfig, SFTTrainer
+from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM
+import trl
 
 train_dataset = datasets.load_dataset('imdb', split='train')
-args = SFTConfig(
-    output_dir="./test-galore",
-    max_steps=100,
+
+args = TrainingArguments(
+    output_dir="./test-schedulefree",
+    max_steps=1000,
+    per_device_train_batch_size=4,
     optim="schedule_free_adamw",
     gradient_checkpointing=True,
+    logging_strategy="steps",
+    logging_steps=1,
+    learning_rate=2e-6,
+    save_strategy="no",
+    run_name="sfo-imdb",
 )
-trainer = SFTTrainer(
-    model="google/gemma-2b",
+
+model_id = "google/gemma-2b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model, 
     args=args,
     train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=1024,
 )
+
 trainer.train()
 ```
 ## تسريع ومدرب

docs/source/de/index.md

@@ -55,148 +55,148 @@ Die Bibliothek enthält derzeit JAX-, PyTorch- und TensorFlow-Implementierungen,
 
 <!--This list is updated automatically from the README with _make fix-copies_. Do not update manually! -->
 
-1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://huggingface.co/papers/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[ALIGN](model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://huggingface.co/papers/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
-1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://huggingface.co/papers/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://huggingface.co/papers/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://huggingface.co/papers/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
-1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://huggingface.co/papers/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
-1. **[BERT](model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://huggingface.co/papers/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERT For Sequence Generation](model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[ALIGN](model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
+1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+1. **[BARTpho](model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
+1. **[BERT](model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
+1. **[BERT For Sequence Generation](model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[BERTweet](model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
-1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[BigBird-RoBERTa](model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[Blenderbot](model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BigBird-RoBERTa](model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[Blenderbot](model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BlenderbotSmall](model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
 1. **[BLOOM](model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
-1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://huggingface.co/papers/2010.10499) by Adrian de Wynter and Daniel J. Perry.
-1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://huggingface.co/papers/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
-1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://huggingface.co/papers/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://huggingface.co/papers/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
-1. **[CodeGen](model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://huggingface.co/papers/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://huggingface.co/papers/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[ConvNeXT](model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
-1. **[ConvNeXTV2](model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://huggingface.co/papers/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
-1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
-1. **[CTRL](model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-1. **[CvT](model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://huggingface.co/papers/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
-1. **[Data2Vec](model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://huggingface.co/papers/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
-1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://huggingface.co/papers/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://huggingface.co/papers/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
-1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://huggingface.co/papers/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
-1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://huggingface.co/papers/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers-research-projects/tree/main/distillation) and a German version of DistilBERT.
-1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://huggingface.co/papers/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
-1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://huggingface.co/papers/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-1. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
-1. **[EfficientNet](model_doc/efficientnet)** (from Google Research) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946)  by Mingxing Tan and Quoc V. Le.
-1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://huggingface.co/papers/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://huggingface.co/papers/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[FLAVA](model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://huggingface.co/papers/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
-1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://huggingface.co/papers/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
-1. **[Funnel Transformer](model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://huggingface.co/papers/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GLPN](model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://huggingface.co/papers/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
+1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
+1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[CodeGen](model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
+1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+1. **[ConvNeXT](model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+1. **[ConvNeXTV2](model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
+1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
+1. **[CTRL](model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
+1. **[CvT](model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
+1. **[Data2Vec](model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
+1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
+1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
+1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
+1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers-research-projects/tree/main/distillation) and a German version of DistilBERT.
+1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
+1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
+1. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
+1. **[EfficientNet](model_doc/efficientnet)** (from Google Research) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)  by Mingxing Tan and Quoc V. Le.
+1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+1. **[FLAVA](model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
+1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
+1. **[Funnel Transformer](model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+1. **[GLPN](model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://openai.com/research/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
-1. **[GPT NeoX](model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://huggingface.co/papers/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
+1. **[GPT NeoX](model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
 1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://openai.com/research/better-language-models/) by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
 1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GPTSAN-japanese](model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama).
-1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://huggingface.co/papers/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
-1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://huggingface.co/papers/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://huggingface.co/papers/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
+1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
 1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
-1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://huggingface.co/papers/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://huggingface.co/papers/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
-1. **[LayoutLMv3](model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://huggingface.co/papers/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
-1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LeViT](model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://huggingface.co/papers/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LongT5](model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://huggingface.co/papers/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
-1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://huggingface.co/papers/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://huggingface.co/papers/1908.07490) by Hao Tan and Mohit Bansal.
-1. **[M-CTC-T](model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://huggingface.co/papers/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
-1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://huggingface.co/papers/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutLMv3](model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
+1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LeViT](model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LongT5](model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
+1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M-CTC-T](model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
+1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
 1. **[MarianMT](model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[Mask2Former](model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://huggingface.co/papers/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
-1. **[MaskFormer](model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://huggingface.co/papers/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
-1. **[mBART](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://huggingface.co/papers/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[mBART-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://huggingface.co/papers/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-1. **[mLUKE](model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://huggingface.co/papers/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
-1. **[MobileBERT](model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://huggingface.co/papers/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
-1. **[MobileViT](model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://huggingface.co/papers/2110.02178) by Sachin Mehta and Mohammad Rastegari.
-1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://huggingface.co/papers/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[MVP](model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://huggingface.co/papers/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
-1. **[Nezha](model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://huggingface.co/papers/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
-1. **[NLLB](model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://huggingface.co/papers/2207.04672) by the NLLB team.
-1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://huggingface.co/papers/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OneFormer](model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://huggingface.co/papers/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
-1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://huggingface.co/papers/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
-1. **[OWL-ViT](model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://huggingface.co/papers/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
-1. **[Pegasus](model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://huggingface.co/papers/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://huggingface.co/papers/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
+1. **[Mask2Former](model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
+1. **[MaskFormer](model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
+1. **[mBART](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
+1. **[mBART-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[Megatron-BERT](model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[mLUKE](model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
+1. **[MobileBERT](model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
+1. **[MobileViT](model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
+1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
+1. **[MVP](model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
+1. **[Nezha](model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
+1. **[NLLB](model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
+1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
+1. **[OneFormer](model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
+1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
+1. **[OWL-ViT](model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
+1. **[Pegasus](model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
+1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
-1. **[PLBart](model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://huggingface.co/papers/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
-1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://huggingface.co/papers/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
-1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://huggingface.co/papers/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
-1. **[RAG](model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://huggingface.co/papers/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
-1. **[REALM](model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://huggingface.co/papers/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
-1. **[Reformer](model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://huggingface.co/papers/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RegNet](model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://huggingface.co/papers/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://huggingface.co/papers/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[ResNet](model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://huggingface.co/papers/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
-1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://huggingface.co/papers/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://huggingface.co/papers/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://huggingface.co/papers/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://huggingface.co/papers/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
-1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://huggingface.co/papers/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
-1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://huggingface.co/papers/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
-1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://huggingface.co/papers/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
-1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://huggingface.co/papers/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
-1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://huggingface.co/papers/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[PLBart](model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
+1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
+1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[RAG](model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
+1. **[REALM](model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[Reformer](model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+1. **[RegNet](model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
+1. **[ResNet](model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
+1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
+1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
+1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
+1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://huggingface.co/papers/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://huggingface.co/papers/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Trajectory Transformer](model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://huggingface.co/papers/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
-1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://huggingface.co/papers/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://huggingface.co/papers/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
-1. **[UL2](model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://huggingface.co/papers/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
+1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
+1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Trajectory Transformer](model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
+1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
+1. **[UL2](model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
 1. **[UMT5](model_doc/umt5)** (from Google Research) released with the paper [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
-1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://huggingface.co/papers/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://huggingface.co/papers/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
-1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://huggingface.co/papers/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
-1. **[VideoMAE](model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://huggingface.co/papers/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
-1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://huggingface.co/papers/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
-1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://huggingface.co/papers/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
-1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://huggingface.co/papers/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
-1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://huggingface.co/papers/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://huggingface.co/papers/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
-1. **[Wav2Vec2-Conformer](model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
-1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://huggingface.co/papers/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://huggingface.co/papers/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://huggingface.co/papers/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
-1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://huggingface.co/papers/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://huggingface.co/papers/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://huggingface.co/papers/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
-1. **[XLM-V](model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://huggingface.co/papers/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
-1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://huggingface.co/papers/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
-1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://huggingface.co/papers/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
-1. **[YOLOS](model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://huggingface.co/papers/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
-1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://huggingface.co/papers/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
+1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
+1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
+1. **[VideoMAE](model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
+1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
+1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
+1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
+1. **[Wav2Vec2-Conformer](model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
+1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
+1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
+1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
+1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
+1. **[XLM-V](model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
+1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOLOS](model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
+1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 
 
 ### Unterstützte Frameworks

docs/source/de/model_sharing.md

@@ -56,7 +56,7 @@ Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können
 Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub.

docs/source/de/peft.md

@@ -44,7 +44,7 @@ Transformers unterstützt nativ einige PEFT-Methoden, d.h. Sie können lokal ode
 
 - [Low Rank Adapters](https://huggingface.co/docs/peft/conceptual_guides/lora)
 - [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
-- [AdaLoRA](https://huggingface.co/papers/2303.10512)
+- [AdaLoRA](https://arxiv.org/abs/2303.10512)
 
 Wenn Sie andere PEFT-Methoden, wie z.B. Prompt Learning oder Prompt Tuning, verwenden möchten, oder über die 🤗 PEFT-Bibliothek im Allgemeinen, lesen Sie bitte die [Dokumentation](https://huggingface.co/docs/peft/index).
 

docs/source/de/run_scripts.md

@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 Alle Skripte können Ihr endgültiges Modell in den [Model Hub](https://huggingface.co/models) hochladen. Stellen Sie sicher, dass Sie bei Hugging Face angemeldet sind, bevor Sie beginnen:
 
 ```bash
-hf auth login
+huggingface-cli login
 ```
 
 Dann fügen Sie dem Skript das Argument `push_to_hub` hinzu. Mit diesem Argument wird ein Repository mit Ihrem Hugging Face-Benutzernamen und dem in `output_dir` angegebenen Ordnernamen erstellt.

docs/source/de/testing.md

@@ -473,6 +473,13 @@ Hier ist zum Beispiel ein Test, der nur ausgeführt werden muss, wenn 2 oder meh
 def test_example_with_multi_gpu():
 ```
 
+Wenn ein Test `tensorflow` benötigt, verwenden Sie den Dekorator `require_tf`. Zum Beispiel:
+
+```python no-style
+@require_tf
+def test_tf_thing_with_tensorflow():
+```
+
 Diese Dekors können gestapelt werden. Wenn zum Beispiel ein Test langsam ist und mindestens eine GPU unter pytorch benötigt, können Sie
 wie Sie ihn einrichten können:
 
@@ -1197,6 +1204,9 @@ if torch.cuda.is_available():
 import numpy as np
 
 np.random.seed(seed)
+
+# tf RNG
+tf.random.set_seed(seed)
 ```
 
 ### Tests debuggen

docs/source/en/_toctree.yml

@@ -17,12 +17,20 @@
       title: Customizing model components
     - local: model_sharing
       title: Sharing
-    - local: modular_transformers
-      title: Contributing a new model to Transformers
     - local: add_new_model
-      title: Legacy model contribution
+      title: Adding a new model to Transformers
+    - local: modular_transformers
+      title: Modular Transformers
     - local: auto_docstring
-      title: Documenting a model
+      title: Document your models
+    - local: task_summary
+      title: What 🤗 Transformers can do
+    - local: tasks_explained
+      title: How 🤗 Transformers solve tasks
+    - local: model_summary
+      title: The Transformer model family
+    - local: attention
+      title: Attention mechanisms
     - local: attention_interface
       title: Customizing attention function
     title: Models
@@ -72,6 +80,8 @@
       title: Caching
     - local: kv_cache
       title: KV cache strategies
+    - local: serving
+      title: Serving
     - local: llm_tutorial_optimization
       title: Getting the most out of LLMs
     - local: perplexity
@@ -81,42 +91,30 @@
     - local: conversations
       title: Chat basics
     - local: chat_templating
-      title: Chat templates
+      title: Templates
     - local: chat_templating_multimodal
-      title: Multimodal chat templates
-    - local: chat_extras
-      title: Tool use
+      title: Multimodal templates
     - local: chat_templating_writing
-      title: Writing a chat template
+      title: Template writing
+    - local: chat_extras
+      title: Tools and RAG
     title: Chat with models
-  - sections:
-    - local: serving
-      title: Serving LLMs, VLMs, and other chat-based models
-    - local: jan
-      title: Jan
-    - local: cursor
-      title: Cursor
-    - local: tiny_agents
-      title: Tiny-Agents CLI and MCP tools
-    - local: open_webui
-      title: Open WebUI
-    title: Serving
   - sections:
     - local: perf_torch_compile
       title: torch.compile
     - local: perf_infer_gpu_one
       title: GPU
     - local: perf_infer_gpu_multi
-      title: Distributed inference
+      title: Distributed GPU inference
     - local: perf_infer_cpu
       title: CPU
+    - local: tf_xla
+      title: XLA
     title: Optimization
   - local: agents
     title: Agents
   - local: tools
     title: Tools
-  - local: transformers_as_backend
-    title: Inference server backends
   title: Inference
 - isExpanded: false
   sections:
@@ -151,6 +149,8 @@
       title: GPU
     - local: perf_train_cpu
       title: CPU
+    - local: perf_train_tpu_tf
+      title: TPU
     - local: perf_train_special
       title: Apple Silicon
     - local: perf_train_gaudi
@@ -189,8 +189,6 @@
     title: FBGEMM
   - local: quantization/finegrained_fp8
     title: Fine-grained FP8
-  - local: quantization/fp_quant
-    title: FP-Quant
   - local: gguf
     title: GGUF
   - local: quantization/gptq
@@ -373,10 +371,6 @@
     - sections:
       - local: model_doc/albert
         title: ALBERT
-      - local: model_doc/apertus
-        title: Apertus
-      - local: model_doc/arcee
-        title: Arcee
       - local: model_doc/bamba
         title: Bamba
       - local: model_doc/bart
@@ -445,10 +439,6 @@
         title: DiffLlama
       - local: model_doc/distilbert
         title: DistilBERT
-      - local: model_doc/doge
-        title: Doge
-      - local: model_doc/dots1
-        title: dots1
       - local: model_doc/dpr
         title: DPR
       - local: model_doc/electra
@@ -457,16 +447,10 @@
         title: Encoder Decoder Models
       - local: model_doc/ernie
         title: ERNIE
-      - local: model_doc/ernie4_5
-        title: Ernie4_5
-      - local: model_doc/ernie4_5_moe
-        title: Ernie4_5_MoE
       - local: model_doc/ernie_m
         title: ErnieM
       - local: model_doc/esm
         title: ESM
-      - local: model_doc/exaone4
-        title: EXAONE-4.0
       - local: model_doc/falcon
         title: Falcon
       - local: model_doc/falcon3
@@ -497,8 +481,6 @@
         title: GLM
       - local: model_doc/glm4
         title: glm4
-      - local: model_doc/glm4_moe
-        title: glm4_moe
       - local: model_doc/openai-gpt
         title: GPT
       - local: model_doc/gpt_neo
@@ -513,8 +495,6 @@
         title: GPT2
       - local: model_doc/gpt_bigcode
         title: GPTBigCode
-      - local: model_doc/gpt_oss
-        title: GptOss
       - local: model_doc/gptsan-japanese
         title: GPTSAN Japanese
       - local: model_doc/gpt-sw3
@@ -533,10 +513,6 @@
         title: HerBERT
       - local: model_doc/hgnet_v2
         title: HGNet-V2
-      - local: model_doc/hunyuan_v1_dense
-        title: HunYuanDenseV1
-      - local: model_doc/hunyuan_v1_moe
-        title: HunYuanMoEV1
       - local: model_doc/ibert
         title: I-BERT
       - local: model_doc/jamba
@@ -547,8 +523,6 @@
         title: Jukebox
       - local: model_doc/led
         title: LED
-      - local: model_doc/lfm2
-        title: LFM2
       - local: model_doc/llama
         title: LLaMA
       - local: model_doc/llama2
@@ -593,8 +567,6 @@
         title: MobileBERT
       - local: model_doc/modernbert
         title: ModernBert
-      - local: model_doc/modernbert-decoder
-        title: ModernBERTDecoder
       - local: model_doc/mpnet
         title: MPNet
       - local: model_doc/mpt
@@ -677,8 +649,6 @@
         title: RoFormer
       - local: model_doc/rwkv
         title: RWKV
-      - local: model_doc/seed_oss
-        title: Seed-Oss
       - local: model_doc/splinter
         title: Splinter
       - local: model_doc/squeezebert
@@ -691,8 +661,6 @@
         title: SwitchTransformers
       - local: model_doc/t5
         title: T5
-      - local: model_doc/t5gemma
-        title: T5Gemma
       - local: model_doc/t5v1.1
         title: T5v1.1
       - local: model_doc/tapex
@@ -719,8 +687,6 @@
         title: XLM-V
       - local: model_doc/xlnet
         title: XLNet
-      - local: model_doc/xlstm
-        title: xLSTM
       - local: model_doc/yoso
         title: YOSO
       - local: model_doc/zamba
@@ -729,8 +695,6 @@
         title: Zamba2
       title: Text models
     - sections:
-      - local: model_doc/aimv2
-        title: Aimv2
       - local: model_doc/beit
         title: BEiT
       - local: model_doc/bit
@@ -747,12 +711,6 @@
         title: D-FINE
       - local: model_doc/dab-detr
         title: DAB-DETR
-      - local: model_doc/deepseek_v2
-        title: DeepSeek-V2
-      - local: model_doc/deepseek_vl
-        title: DeepseekVL
-      - local: model_doc/deepseek_vl_hybrid
-        title: DeepseekVLHybrid
       - local: model_doc/deformable_detr
         title: Deformable DETR
       - local: model_doc/deit
@@ -773,26 +731,18 @@
         title: DINOV2
       - local: model_doc/dinov2_with_registers
         title: DINOv2 with Registers
-      - local: model_doc/dinov3
-        title: DINOv3
       - local: model_doc/dit
         title: DiT
       - local: model_doc/dpt
         title: DPT
       - local: model_doc/efficientformer
         title: EfficientFormer
-      - local: model_doc/efficientloftr
-        title: EfficientLoFTR
       - local: model_doc/efficientnet
         title: EfficientNet
-      - local: model_doc/eomt
-        title: EoMT
       - local: model_doc/focalnet
         title: FocalNet
       - local: model_doc/glpn
         title: GLPN
-      - local: model_doc/hgnet_v2
-        title: HGNet-V2
       - local: model_doc/hiera
         title: Hiera
       - local: model_doc/ijepa
@@ -801,8 +751,6 @@
         title: ImageGPT
       - local: model_doc/levit
         title: LeViT
-      - local: model_doc/lightglue
-        title: LightGlue
       - local: model_doc/mask2former
         title: Mask2Former
       - local: model_doc/maskformer
@@ -891,8 +839,6 @@
         title: CSM
       - local: model_doc/dac
         title: dac
-      - local: model_doc/dia
-        title: Dia
       - local: model_doc/encodec
         title: EnCodec
       - local: model_doc/fastspeech2_conformer
@@ -901,8 +847,6 @@
         title: GraniteSpeech
       - local: model_doc/hubert
         title: Hubert
-      - local: model_doc/kyutai_speech_to_text
-        title: Kyutai Speech-To-Text
       - local: model_doc/mctct
         title: MCTCT
       - local: model_doc/mimi
@@ -953,8 +897,6 @@
         title: WavLM
       - local: model_doc/whisper
         title: Whisper
-      - local: model_doc/xcodec
-        title: X-Codec
       - local: model_doc/xls_r
         title: XLS-R
       - local: model_doc/xlsr_wav2vec2
@@ -963,8 +905,6 @@
     - sections:
       - local: model_doc/timesformer
         title: TimeSformer
-      - local: model_doc/vjepa2
-        title: V-JEPA 2
       - local: model_doc/videomae
         title: VideoMAE
       - local: model_doc/vivit
@@ -997,8 +937,6 @@
         title: CLIPSeg
       - local: model_doc/clvp
         title: CLVP
-      - local: model_doc/cohere2_vision
-        title: Cohere2Vision
       - local: model_doc/colpali
         title: ColPali
       - local: model_doc/colqwen2
@@ -1011,22 +949,12 @@
         title: Donut
       - local: model_doc/emu3
         title: Emu3
-      - local: model_doc/evolla
-        title: Evolla
       - local: model_doc/flava
         title: FLAVA
-      - local: model_doc/florence2
-        title: Florence2
       - local: model_doc/gemma3
         title: Gemma3
-      - local: model_doc/gemma3n
-        title: Gemma3n
       - local: model_doc/git
         title: GIT
-      - local: model_doc/glm4v
-        title: glm4v
-      - local: model_doc/glm4v_moe
-        title: glm4v_moe
       - local: model_doc/got_ocr2
         title: GOT-OCR2
       - local: model_doc/granitevision
@@ -1051,8 +979,6 @@
         title: Janus
       - local: model_doc/kosmos-2
         title: KOSMOS-2
-      - local: model_doc/kosmos2_5
-        title: KOSMOS-2.5
       - local: model_doc/layoutlm
         title: LayoutLM
       - local: model_doc/layoutlmv2
@@ -1066,7 +992,7 @@
       - local: model_doc/llama4
         title: Llama4
       - local: model_doc/llava
-        title: LLaVA
+        title: Llava
       - local: model_doc/llava_next
         title: LLaVA-NeXT
       - local: model_doc/llava_next_video
@@ -1077,24 +1003,18 @@
         title: LXMERT
       - local: model_doc/matcha
         title: MatCha
-      - local: model_doc/metaclip_2
-        title: MetaCLIP 2
       - local: model_doc/mgp-str
         title: MGP-STR
       - local: model_doc/mistral3
         title: Mistral3
       - local: model_doc/mllama
         title: mllama
-      - local: model_doc/mm-grounding-dino
-        title: MM Grounding DINO
       - local: model_doc/nougat
         title: Nougat
       - local: model_doc/omdet-turbo
         title: OmDet-Turbo
       - local: model_doc/oneformer
         title: OneFormer
-      - local: model_doc/ovis2
-        title: Ovis2
       - local: model_doc/owlvit
         title: OWL-ViT
       - local: model_doc/owlv2
@@ -1103,8 +1023,6 @@
         title: PaliGemma
       - local: model_doc/perceiver
         title: Perceiver
-      - local: model_doc/perception_lm
-        title: PerceptionLM
       - local: model_doc/phi4_multimodal
         title: Phi4 Multimodal
       - local: model_doc/pix2struct
@@ -1119,10 +1037,6 @@
         title: Qwen2Audio
       - local: model_doc/qwen2_vl
         title: Qwen2VL
-      - local: model_doc/sam2
-        title: SAM2
-      - local: model_doc/sam2_video
-        title: SAM2 Video
       - local: model_doc/sam
         title: Segment Anything
       - local: model_doc/sam_hq
@@ -1133,8 +1047,6 @@
         title: SigLIP
       - local: model_doc/siglip2
         title: SigLIP2
-      - local: model_doc/smollm3
-        title: SmolLM3
       - local: model_doc/smolvlm
         title: SmolVLM
       - local: model_doc/speech-encoder-decoder
@@ -1161,8 +1073,6 @@
         title: Vision Text Dual Encoder
       - local: model_doc/visual_bert
         title: VisualBERT
-      - local: model_doc/voxtral
-        title: Voxtral
       - local: model_doc/xclip
         title: X-CLIP
       title: Multimodal models
@@ -1220,3 +1130,4 @@
       title: Environment Variables
     title: Reference
   title: API
+

docs/source/en/accelerate.md

@@ -75,7 +75,7 @@ training_args = TrainingArguments(
     per_device_eval_batch_size=16,
     num_train_epochs=2,
     fsdp_config="path/to/fsdp_config",
-    fsdp="full_shard",
+    fsdp_strategy="full_shard",
     weight_decay=0.01,
     eval_strategy="epoch",
     save_strategy="epoch",

docs/source/en/add_new_model.md

@@ -13,7 +13,7 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Legacy model contribution
+# Adding a new model to Transformers
 
 > [!TIP]
 > Try adding new models with a more [modular](./modular_transformers) approach first. This makes it significantly easier to contribute a model to Transformers!
@@ -571,7 +571,7 @@ The processor should call the appropriate modality-specific processors within it
 def __call__(
     self,
     images: ImageInput = None,
-    text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+    text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
     audio=None,
     videos=None,
     **kwargs: Unpack[YourModelProcessorKwargs],

docs/source/en/add_new_pipeline.md

@@ -100,18 +100,19 @@ pipeline("This is the best meal I've ever had")
 
 Register the new task your pipeline supports in the `PIPELINE_REGISTRY`. The registry defines:
 
-- The supported Pytorch model class with `pt_model`
+- the machine learning framework the pipeline supports with either `pt_model` or `tf_model` (add both to ensure it works with either frameworks)
 - a default model which should come from a specific revision (branch, or commit hash) where the model works as expected with `default`
 - the expected input with `type`
 
 ```py
 from transformers.pipelines import PIPELINE_REGISTRY
-from transformers import AutoModelForSequenceClassification
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
 
 PIPELINE_REGISTRY.register_pipeline(
     "new-task",
     pipeline_class=MyPipeline,
     pt_model=AutoModelForSequenceClassification,
+    tf_model=TFAutoModelForSequenceClassification,
     default={"pt": ("user/awesome-model", "branch-name")},
     type="text",
 )
@@ -127,7 +128,7 @@ It's faster to upload your pipeline code to the Hub because it doesn't require a
 
 Add your pipeline code to the Hub in a Python file.
 
-For example, a custom pipeline for sentence pair classification might look like the following code below.
+For example, a custom pipeline for sentence pair classification might look like the following code below. The implementation works for PyTorch and TensorFlow models.
 
 ```py
 import numpy as np
@@ -167,12 +168,13 @@ Save the code in a file named `pair_classification.py`, and import and register
 ```py
 from pair_classification import PairClassificationPipeline
 from transformers.pipelines import PIPELINE_REGISTRY
-from transformers import AutoModelForSequenceClassification
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
 
 PIPELINE_REGISTRY.register_pipeline(
     "pair-classification",
     pipeline_class=PairClassificationPipeline,
     pt_model=AutoModelForSequenceClassification,
+    tf_model=TFAutoModelForSequenceClassification,
 )
 ```
 
@@ -185,6 +187,9 @@ The [register_pipeline](https://github.com/huggingface/transformers/blob/9feae5f
       "pt": [
         "AutoModelForSequenceClassification"
       ],
+      "tf": [
+        "TFAutoModelForSequenceClassification"
+      ],
     }
   },
 ```
@@ -214,11 +219,11 @@ Add your pipeline code as a new module to the [pipelines](https://github.com/hug
 
 Next, add a new test for the pipeline in [transformers/tests/pipelines](https://github.com/huggingface/transformers/tree/main/tests/pipelines). You can look at the other tests for examples of how to test your pipeline.
 
-The [run_pipeline_test](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L186) function should be very generic and run on the models defined in [model_mapping](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L48). This is important for testing future compatibility with new models.
+The [run_pipeline_test](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L186) function should be very generic and run on the models defined in [model_mapping](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L48) and [tf_model_mapping](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L49). This is important for testing future compatibility with new models.
 
 You'll also notice `ANY` is used throughout the [run_pipeline_test](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L186) function. The models are random, so you can't check the actual values. Using `ANY` allows the test to match the output of the pipeline type instead.
 
 Finally, you should also implement the following 4 tests.
 
-1. [test_small_model_pt](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L59), use a small model for these pipelines to make sure they return the correct outputs. The results don't have to make sense. Each pipeline should return the same result.
-1. [test_large_model_pt](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_zero_shot_image_classification.py#L187), use a realistic model for these pipelines to make sure they return meaningful results. These tests are slow and should be marked as slow.
+1. [test_small_model_pt](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L59) and [test_small_model_tf](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_text_classification.py#L150), use a small model for these pipelines to make sure they return the correct outputs. The results don't have to make sense. Each pipeline should return the same result.
+1. [test_large_model_pt](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_zero_shot_image_classification.py#L187) nad [test_large_model_tf](https://github.com/huggingface/transformers/blob/db70426854fe7850f2c5834d633aff637f14772e/tests/pipelines/test_pipelines_zero_shot_image_classification.py#L220), use a realistic model for these pipelines to make sure they return meaningful results. These tests are slow and should be marked as slow.

docs/source/en/agents.md

@@ -14,9 +14,5 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Agents
-
-(deprecated)
-
 > [!WARNING]
 > Agents and tools were spun out into the standalone [smolagents](https://huggingface.co/docs/smolagents/index) library. They were removed from `transformers` in v4.52.

docs/source/en/attention.md

@@ -0,0 +1,61 @@
+<!--Copyright 2023 The HuggingFace 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Attention mechanisms
+
+Most transformer models use full attention in the sense that the attention matrix is square. It can be a big
+computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and
+use a sparse version of the attention matrix to speed up training.
+
+## LSH attention
+
+[Reformer](model_doc/reformer) uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax
+dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can consider only
+the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is
+modified to mask the current token (except at the first position), because it will give a query and a key equal (so
+very similar to each other). Since the hash can be a bit random, several hash functions are used in practice
+(determined by a n_rounds parameter) and then are averaged together.
+
+## Local attention
+
+[Longformer](model_doc/longformer) uses local attention: often, the local context (e.g., what are the two tokens to the
+left and right?) is enough to take action for a given token. Also, by stacking attention layers that have a small
+window, the last layer will have a receptive field of more than just the tokens in the window, allowing them to build a
+representation of the whole sentence.
+
+Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access
+all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
+their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:
+
+<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>
+
+Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence
+length.
+
+## Other tricks
+
+### Axial positional encodings
+
+[Reformer](model_doc/reformer) uses axial positional encodings: in traditional transformer models, the positional encoding
+E is a matrix of size \\(l\\) by \\(d\\), \\(l\\) being the sequence length and \\(d\\) the dimension of the
+hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate
+that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and E2, with
+dimensions \\(l_{1} \times d_{1}\\) and \\(l_{2} \times d_{2}\\), such that \\(l_{1} \times l_{2} = l\\) and
+\\(d_{1} + d_{2} = d\\) (with the product for the lengths, this ends up being way smaller). The embedding for time
+step \\(j\\) in E is obtained by concatenating the embeddings for timestep \\(j \% l1\\) in E1 and \\(j // l1\\)
+in E2.

docs/source/en/attention_interface.md

@@ -60,11 +60,11 @@ You will see it prints "I just entered the attention computation" as many times
 
 ## Dynamically switching attention function
 
-You could dynamically change the model's attention function as well:
+You could dynamically change the model's attention function as well, by overriding the `config._attn_implementation` field:
 
 ```python
 # Back to use original sdpa implementation
-model.set_attn_implementation("sdpa")
+model.config._attn_implementation = "sdpa"
 
 model(torch.ones(1, 5, dtype=int))
 ```
@@ -72,34 +72,6 @@ model(torch.ones(1, 5, dtype=int))
 and it will stop printing the statements, as it now uses the `sdpa` attention.  
 This allows to quickly change an attention function, without needing to reload the model!
 
-## Different attention per backbone in multimodal models
-
-For multimodal models different attention functions may work better for each backbone module. For example, some vision backbones perform better in fp32, but are incompatible with FlashAttention. To continue using FlashAttention while keeping the vision encoder in fp32, create a dict and map each config to an attention implementation as shown below.
-
-```python
-from transformers import AutoModelForImageTextToText
-
-model_id = "facebook/chameleon-7b"
-
-attention_implementation_per_backbone = {"vision_config": "sdpa", "text_config": "flash_attention_2"}
-model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation=attention_implementation_per_backbone)
-
-# NOTE: keys in the attention implementation have to be the same as the sub-config names
-for key in attention_implementation_per_backbone:
-    assert key in model.config.sub_configs, f"Invalid key in `attention_implementation`"
-
-# You can omit certain backbones - the default attention function (SDPA) will be used
-# This is equivalent to the previous example
-model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation={"text_config": "flash_attention_2"})
-
-
-# Set the same attention implementation for all backbones with single string, same as in non-multimodal models
-model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation="eager")
-
-# Alternatively use a dict with an empty key for global configuration
-model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation={"": "eager"})
-```
-
 ## What about new args needed in my custom attention function?
 
 But indeed, what if the new function requires a new arg to be properly used? It's no issue! Models supporting the
@@ -120,7 +92,7 @@ def custom_attention(
     a_new_kwargs = None,  # You can now add as many kwargs as you need
     another_new_kwargs = None,  # You can now add as many kwargs as you need
     **kwargs,  # You need to accept **kwargs as models will pass other args
-) -> tuple[torch.Tensor, Optional[torch.Tensor]]
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]
     ...  # do your magic!
     return attn_output, attn_weights  # attn_weights are optional here
 

docs/source/en/auto_docstring.md

@@ -14,26 +14,43 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Documenting a model
+# Utilizing the @auto_docstring Decorator
 
-The `@auto_docstring` decorator in Transformers generates consistent docstrings for model classes and their methods. It reduces boilerplate by automatically including standard argument descriptions while also allowing overrides to add new or custom arguments. [Contributing a new model](./modular_transformers) is easier because you don't need to manually add the standard docstrings, and only focus on documenting new arguments.
+The `@auto_docstring` decorator in the Hugging Face Transformers library helps generate docstrings for model classes and their methods, which will be used to build the documentation for the library. It aims to improve consistency and reduce boilerplate by automatically including standard argument descriptions and allowing for targeted overrides and additions.
 
-This guide describes how to use the `@auto_docstring` decorator and how it works.
+---
 
-## @auto_docstring
+## 📜 How it Works
 
-Start by importing the decorator in the modeling file (`modular_model.py` or `modeling_model.py`).
+The `@auto_docstring` decorator constructs docstrings by:
+
+1.  **Signature Inspection:** It inspects the signature (arguments, types, defaults) of the decorated class's `__init__` method or the decorated function.
+2.  **Centralized Docstring Fetching:** It retrieves predefined docstrings for common arguments (e.g., `input_ids`, `attention_mask`) from internal library sources (like `ModelArgs` or `ImageProcessorArgs` in `utils/args_doc.py`).
+3.  **Overriding or Adding Arguments Descriptions:**
+    * **Direct Docstring Block:** It incorporates custom docstring content from an `r""" """` (or `""" """`) block below the method signature or within the `__init__` docstring. This is for documenting new arguments or overriding standard descriptions.
+    * **Decorator Arguments (`custom_args`):** A `custom_args` docstring block can be passed to the decorator to provide docstrings for specific arguments directly in the decorator call. This can be used to define the docstring block for new arguments once if they are repeated in multiple places in the modeling file.
+4.  **Adding Classes and Functions Introduction:**
+    * **`custom_intro` argument:** Allows prepending a custom introductory paragraph to a class or function docstring.
+    * **Automatic Introduction Generation:** For model classes with standard naming patterns (like `ModelForCausalLM`) or belonging to a pipeline, the decorator automatically generates an appropriate introductory paragraph using `ClassDocstring` in `utils/args_doc.py` as the source.
+5.  **Templating:** The decorator uses a templating system, allowing predefined docstrings to include dynamic information deduced from the `auto_modules` of the library, such as `{{processor_class}}` or `{{config_class}}`.
+6.  **Deducing Relevant Examples:** The decorator attempts to find appropriate usage examples based on the model's task or pipeline compatibility. It extracts checkpoint information from the model's configuration class to provide concrete examples with real model identifiers.
+7.  **Adding Return Value Documentation:** For methods like `forward`, the decorator can automatically generate the "Returns" section based on the method's return type annotation. For example, for a method returning a `ModelOutput` subclass, it will extracts field descriptions from that class's docstring to create a comprehensive return value description. A custom `Returns` section can also be manually specified in the function docstring block.
+8.  **Unrolling Kwargs Typed With Unpack Operator:** For specific methods (defined in `UNROLL_KWARGS_METHODS`) or classes (defined in `UNROLL_KWARGS_CLASSES`), the decorator processes `**kwargs` parameters that are typed with `Unpack[KwargsTypedDict]`. It extracts the documentation from the TypedDict and adds each parameter to the function's docstring. Currently, this functionality is only supported for `FastImageProcessorKwargs`.
+
+
+---
+
+## 🚀 How to Use @auto_docstring
+
+### 1. Importing the Decorator
+Import the decorator into your modeling file:
 
 ```python
 from ...utils import auto_docstring
 ```
 
-Select whether you'd like to apply `@auto_docstring` to a class or function below to see how to use it.
-
-<hfoptions id="type">
-<hfoption id="classes">
-
-Place `@auto_docstring` directly above the class definition. The decorator derives parameter descriptions from the `__init__` method's signature and docstring.
+### 2. Applying to Classes
+Place `@auto_docstring` directly above the class definition. It uses the `__init__` method's signature and its docstring for parameter descriptions.
 
 ```python
 from transformers.modeling_utils import PreTrainedModel
@@ -56,7 +73,9 @@ class MyAwesomeModel(PreTrainedModel):
     # ... other methods
 ```
 
-Arguments can also be passed directly to `@auto_docstring` for more control. Use the `custom_intro` parameter to describe the argument and the `custom_args` parameter to describe the arguments.
+#### Advanced Class Decoration:
+
+Arguments can be passed directly to `@auto_docstring` for more control:
 
 ```python
 @auto_docstring(
@@ -64,9 +83,9 @@ Arguments can also be passed directly to `@auto_docstring` for more control. Use
     It builds upon the standard Transformer architecture with unique modifications.""",
     custom_args="""
     custom_parameter (`type`, *optional*, defaults to `default_value`):
-        A concise description for custom_parameter if not defined or overriding the description in `auto_docstring.py`.
+        A concise description for custom_parameter if not defined or overriding the description in `args_doc.py`.
     internal_helper_arg (`type`, *optional*, defaults to `default_value`):
-        A concise description for internal_helper_arg if not defined or overriding the description in `auto_docstring.py`.
+        A concise description for internal_helper_arg if not defined or overriding the description in `args_doc.py`.
     """
 )
 class MySpecialModel(PreTrainedModel):
@@ -74,7 +93,7 @@ class MySpecialModel(PreTrainedModel):
         # ...
 ```
 
-You can also choose to only use `custom_intro` and define the custom arguments directly in the class.
+Or:
 
 ```python
 @auto_docstring(
@@ -85,44 +104,15 @@ class MySpecialModel(PreTrainedModel):
     def __init__(self, config: ConfigType, custom_parameter: "type" = "default_value", internal_helper_arg=None):
         r"""
         custom_parameter (`type`, *optional*, defaults to `default_value`):
-            A concise description for custom_parameter if not defined or overriding the description in `auto_docstring.py`.
+            A concise description for custom_parameter if not defined or overriding the description in `args_doc.py`.
         internal_helper_arg (`type`, *optional*, defaults to `default_value`):
-            A concise description for internal_helper_arg if not defined or overriding the description in `auto_docstring.py`.
+            A concise description for internal_helper_arg if not defined or overriding the description in `args_doc.py`.
         """
         # ...
 ```
 
-You should also use the `@auto_docstring` decorator for classes that inherit from [`~utils.ModelOutput`].
-
-```python
-@dataclass
-@auto_docstring(
-    custom_intro="""
-    Custom model outputs with additional fields.
-    """
-)
-class MyModelOutput(ImageClassifierOutput):
-    r"""
-    loss (`torch.FloatTensor`, *optional*):
-        The loss of the model.
-    custom_field (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*):
-        A custom output field specific to this model.
-    """
-
-    # Standard fields like hidden_states, logits, attentions etc. can be automatically documented if the description is the same as the standard arguments.
-    # However, given that the loss docstring is often different per model, you should document it in the docstring above.
-    loss: Optional[torch.FloatTensor] = None
-    logits: Optional[torch.FloatTensor] = None
-    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
-    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
-    # Custom fields need to be documented in the docstring above
-    custom_field: Optional[torch.FloatTensor] = None
-```
-
-</hfoption>
-<hfoption id="functions">
-
-Place `@auto_docstring` directly above the method definition. The decorator derives parameter descriptions from the function signature.
+### 3. Applying to Functions (e.g., `forward` method)
+Apply the decorator above method definitions, such as the `forward` method.
 
 ```python
     @auto_docstring
@@ -141,10 +131,9 @@ Place `@auto_docstring` directly above the method definition. The decorator deri
         # ...
 ```
 
-Arguments can also be passed directly to `@auto_docstring` for more control. Use the `custom_intro` parameter to describe the argument and the `custom_args` parameter to describe the arguments.
-
-The `Returns` and `Examples` parts of the docstring can also be manually specified.
+#### Advanced Function Decoration:
 
+Arguments can be passed directly to `@auto_docstring` for more control. `Returns` and `Examples` sections can also be manually specified:
 
 ```python
 MODEL_COMMON_CUSTOM_ARGS = r"""
@@ -191,117 +180,100 @@ class MyModel(PreTrainedModel):
         # ...
 ```
 
-</hfoption>
-</hfoptions>
+---
 
-## Documenting arguments
+### ✍️ Documenting Arguments: Approach & Priority
 
-There are some rules for documenting different types of arguments and they're listed below.
-
-- Standard arguments (`input_ids`, `attention_mask`, `pixel_values`, etc.) are defined and retrieved from `auto_docstring.py`. It is the single source of truth for standard arguments and should not be redefined locally if an argument's description and shape is the same as an argument in `auto_docstring.py`.
-
-    If a standard argument behaves differently in your model, then you can override it locally in a `r""" """` block. This local definition has a higher priority. For example, the `labels` argument is often customized per model and typically requires overriding.
-
-
-- New or custom arguments should be documented within an `r""" """` block after the signature if it is a function or in the `__init__` method's docstring if it is a class.
-
-    ```py
-    argument_name (`type`, *optional*, defaults to `X`):
-        Description of the argument.
-        Explain its purpose, expected shape/type if complex, and default behavior.
-        This can span multiple lines.
-    ```
+1.  **Standard Arguments (e.g., `input_ids`, `attention_mask`, `pixel_values`, `encoder_hidden_states` etc.):**
+    * `@auto_docstring` retrieves descriptions from a central source. Do not redefine these locally if their description and shape are the same as in `args_doc.py`.
 
+2.  **New or Custom Arguments:**
+    * **Primary Method:** Document these within an `r""" """` docstring block following the signature (for functions) or in the `__init__` method's docstring (for class parameters).
+    * **Format:**
+        ```
+        argument_name (`type`, *optional*, defaults to `X`):
+            Description of the argument.
+            Explain its purpose, expected shape/type if complex, and default behavior.
+            This can span multiple lines.
+        ```
     * Include `type` in backticks.
-    * Add *optional* if the argument is not required or has a default value.
-    * Add "defaults to X" if it has a default value. You don't need to add "defaults to `None`" if the default value is `None`.
+    * Add "*optional*" if the argument is not required (has a default value).
+    * Add "defaults to `X`" if it has a default value (no need to specify "defaults to `None`" if the default value is `None`).
 
-    These arguments can also be passed to `@auto_docstring` as a `custom_args` argument. It is used to define the docstring block for new arguments once if they are repeated in multiple places in the modeling file.
+3.  **Overriding Standard Arguments:**
+    * If a standard argument behaves differently (e.g., different expected shape, model-specific behavior), provide its complete description in the local `r""" """` docstring. This local definition takes precedence.
+    * The `labels` argument is often customized per model and typically requires a specific docstring.
 
-    ```py
-    class MyModel(PreTrainedModel):
-    # ...
-    @auto_docstring(
-        custom_intro="""
-        This is a custom introduction for the function.
-        """
-        custom_args=r"""
-        common_arg_1 (`torch.Tensor`, *optional*, defaults to `default_value`):
-            Description of common_arg_1
-        """
-    )
-    ```
+4.  **Using Decorator Arguments for Overrides or New Arguments (`custom_args`):**
+    * New or custom arguments docstrings can also be passed to `@auto_docstring` as a `custom_args` argument. This can be used to define the docstring block for new arguments once if they are repeated in multiple places in the modeling file.
 
-## Checking the docstrings
+---
 
-Transformers includes a utility script to validate the docstrings when you open a Pull Request which triggers CI (continuous integration) checks. The script checks for the following criteria.
+### Usage with [modular files](./modular_transformers)
 
-* Ensures `@auto_docstring` is applied to relevant mode classes and public methods.
-* Ensures arguments are complete and consistent. It checks that documented arguments exist in the signature and verifies whether the types and default values in the docstring match the signature. Arguments that aren't known standard arguments or if they lack a local description are flagged.
-* Reminds you to complete placeholders like `<fill_type>` and `<fill_docstring>`.
-* Ensures docstrings are formatted according to the expected docstring style.
+When working with modular files, follow these guidelines for applying the `@auto_docstring` decorator:
 
-You can run this check locally - before committing - by running the following command.
+- **For standalone models in modular files:**
+  Apply the `@auto_docstring` decorator just as you would in regular modeling files.
+
+- **For models inheriting from other library models:**
+  - When inheriting from a parent model, decorators (including `@auto_docstring`) are automatically carried over to the generated modeling file without needing to add them in your modular file.
+  - If you need to modify the `@auto_docstring` behavior, apply the customized decorator in your modular file, making sure to *include all other decorators* that were present on the original function/class.
+
+  > **Warning**: When overriding any decorator in a modular file, you must include ALL decorators that were applied to that function/class in the parent model. If you only override some decorators, the others won't be included in the generated modeling file.
+
+
+**Note**: The `check_auto_docstrings` tool doesn't check modular files directly, but it will check (and modify when using `--fix_and_overwrite`) the generated modeling files. If issues are found in the generated files, you'll need to update your modular files accordingly.
+
+---
+
+## ✅ Checking Your Docstrings with `check_auto_docstrings`
+
+The library includes a utility script to validate docstrings. This check is typically run during Continuous Integration (CI).
+
+#### What it Checks:
+
+* **Decorator Presence:** Ensures `@auto_docstring` is applied to relevant model classes and public methods. (TODO)
+* **Argument Completeness & Consistency:**
+    * Flags arguments in the signature that are not known standard arguments and lack a local description.
+    * Ensures documented arguments exist in the signature. (TODO)
+    * Verifies that types and default values in the docstring match the signature. (TODO)
+* **Placeholder Detection:** Reminds you to complete placeholders like `<fill_type>` or `<fill_docstring>`.
+* **Formatting:** Adherence to the expected docstring style.
+
+#### Running the Check Locally:
+
+Run this check locally before committing. The common command is:
 
 ```bash
 make fix-copies
 ```
 
-`make fix-copies` runs several other checks as well. If you don't need those checks, run the command below to only perform docstring and auto-docstring checks.
+Alternatively, to only perform docstrings and auto-docstring checks, you can use:
 
 ```bash
 python utils/check_docstrings.py # to only check files included in the diff without fixing them
-# python utils/check_docstrings.py --fix_and_overwrite # to fix and overwrite the files in the diff
-# python utils/check_docstrings.py --fix_and_overwrite --check_all # to fix and overwrite all files
+# Or: python utils/check_docstrings.py --fix_and_overwrite # to fix and overwrite the files in the diff
+# Or: python utils/check_docstrings.py --fix_and_overwrite --check_all # to fix and overwrite all files
 ```
 
-## modular_model.py files
+#### Workflow with the Checker:
 
-When working with modular files (`modular_model.py`), follow the guidelines below for applying `@auto_docstring`.
+1.  Add `@auto_docstring(...)` to the class or method.
+2.  For new, custom, or overridden arguments, add descriptions in an `r""" """` block.
+3.  Run `make fix-copies` (or the `check_docstrings.py` utility).
+    * For unrecognized arguments lacking documentation, the utility will create placeholder entries.
+4.  Manually edit these placeholders with accurate types and descriptions.
+5.  Re-run the check to ensure all issues are resolved.
 
-- For standalone models in modular files, apply `@auto_docstring` like you would in a `modeling_model.py` file.
-- For models that inherit from other library models, `@auto_docstring` is automatically carried over to the generated modeling file. You don't need to add `@auto_docstring` in your modular file.
+---
 
-    If you need to modify the `@auto_docstring` behavior, apply the customized decorator in your modular file. Make sure to **include all other decorators** that are present in the original function or class.
+## 🔑 Key Takeaways & Best Practices
 
-> [!WARNING]
-> When overriding any decorator in a modular file, you must include **all** decorators that were applied to that function or class in the parent model. If you only override some decorators, the others won't be included in the generated modeling file.
-
-## How it works
-
-The `@auto_docstring` decorator automatically generates docstrings by:
-
-1. Inspecting the signature (arguments, types, defaults) of the decorated class' `__init__` method or the decorated function.
-2. Retrieving the predefined docstrings for common arguments (`input_ids`, `attention_mask`, etc.) from internal library sources like [`ModelArgs`], [`ImageProcessorArgs`], and the `auto_docstring.py` file.
-3. Adding argument descriptions in one of two ways as shown below.
-
-    | method | description | usage |
-    |---|---|---|
-    | `r""" """` | add custom docstring content directly to a method signature or within the `__init__` docstring | document new arguments or override standard descriptions |
-    | `custom_args` | add custom docstrings for specific arguments directly in `@auto_docstring` | define docstring for new arguments once if they're repeated in multiple places in the modeling file |
-
-4. Adding class and function descriptions. For model classes with standard naming patterns, like `ModelForCausalLM`, or if it belongs to a pipeline, `@auto_docstring` automatically generates the appropriate descriptions with `ClassDocstring` from `auto_docstring.py`.
-
-    `@auto_docstring` also accepts the `custom_intro` argument to describe a class or function.
-
-5. Using a templating system to allow predefined docstrings to include dynamic information from Transformers' [auto_modules](https://github.com/huggingface/transformers/tree/main/src/transformers/models/auto) such as `{{processor_class}}` and `{{config_class}}`.
-
-6. Finding appropriate usage examples based on the model's task or pipeline compatibility. It extracts checkpoint information form the model's configuration class to provide concrete examples with real model identifiers.
-
-7. Adding return values to the docstring. For methods like `forward`, the decorator automatically generates the `Returns` field in the docstring based on the method's return type annotation.
-
-    For example, if a method returns a [`~transformers.utils.ModelOutput`] subclass, `@auto_docstring` extracts the field descriptions from the class' docstring to create a comprehensive return value description. You can also manually specify a custom `Returns` field in a functions docstring.
-
-8. Unrolling kwargs typed with the unpack operator. For specific methods (defined in `UNROLL_KWARGS_METHODS`) or classes (defined in `UNROLL_KWARGS_CLASSES`), the decorator processes `**kwargs` parameters that are typed with `Unpack[KwargsTypedDict]`. It extracts the documentations from the `TypedDict` and adds each parameter to the function's docstring.
-
-    Currently only supported for [`FastImageProcessorKwargs`].
-
-## Best practices
-
-Follow the best practices below to help maintain consistent and informative documentation for Transformers!
-
-* Use `@auto_docstring` for new PyTorch model classes ([`PreTrainedModel`] subclasses) and their primary methods like `forward` or `get_text_features`.
-* For classes, `@auto_docstring` retrieves parameter descriptions from the `__init__` method's docstring.
-* Rely on standard docstrings and do not redefine common arguments unless their behavior is different in your model.
+* Use `@auto_docstring` for new PyTorch model classes (`PreTrainedModel` subclasses) and their primary for methods (e.g., `forward`, `get_text_features` etc.).
+* For classes, the `__init__` method's docstring is the main source for parameter descriptions when using `@auto_docstring` on the class.
+* Rely on standard docstrings; do not redefine common arguments unless their behavior is different in your specific model.
 * Document new or custom arguments clearly.
 * Run `check_docstrings` locally and iteratively.
+
+By following these guidelines, you help maintain consistent and informative documentation for the Hugging Face Transformers library 🤗.

docs/source/en/cache_explanation.md

@@ -15,7 +15,6 @@ rendered properly in your Markdown viewer.
 -->
 
 # Caching
-
 Imagine you're having a conversation with someone, and instead of remembering what they previously said, they have to start from scratch every time you respond. This would be slow and inefficient, right?
 
 You can extend this analogy to transformer models. Autoregressive model generation can be slow because it makes a prediction one token at a time. Each new prediction is dependent on all the previous context.
@@ -83,37 +82,41 @@ When you use Transformers' [`Cache`] class, the self-attention module performs s
 
 ## Cache storage implementation
 
-Caches are structured as a list of layers, where each layer contains a key and value cache. The key and value caches are tensors with the shape `[batch_size, num_heads, seq_len, head_dim]`.
+The actual storage of key-value pairs varies between cache implementations. As an example, consider the [`DynamicCache`].
 
-Layers can be of different types (e.g. `DynamicLayer`, `StaticLayer`, `SlidingWindowLayer`), which mostly changes how sequence length is handled and how the cache is updated.
 
-The simplest is a `DynamicLayer` that grows as more tokens are processed. The sequence length dimension (`seq_len`) increases with each new token:
+In [`DynamicCache`], the key-value pairs are stored as two lists of tensors. Each tensor in the lists have the shape `[batch_size, num_heads, seq_len, head_dim]`.
+- `key_cache`: A list of tensors, one for each layer.
+- `value_cache`: A list of tensors, one for each layer.
 
+When new tokens are processed:
+
+1. For each layer, the new key and value states are concatenated with the existing cache.
 ```py
-cache.layers[idx].keys = torch.cat([cache.layers[idx].keys, key_states], dim=-2)
-cache.layers[idx].values = torch.cat([cache.layers[idx].values, value_states], dim=-2)
+self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
 ```
 
-Other layer types like `StaticLayer` and `SlidingWindowLayer` have a fixed sequence length that is set when the cache is created. This makes them compatible with `torch.compile`. In the case of `SlidingWindowLayer`, existing tokens are shifted out of the cache when a new token is added.
+2. The cache grows dynamically as more tokens are processed. The sequence length dimension (`seq_len`) increases with each new token.
+
+3. The cache maintains a count of seen tokens through `self._seen_tokens`. This is updated when the first layer processes a new token.
 
 The example below demonstrates how to create a generation loop with [`DynamicCache`]. As discussed, the attention mask is a concatenation of past and current token values and `1` is added to the cache position for the next token.
 
 ```py
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache, infer_device
-
-device = f"{infer_device()}:0"
+from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
 
 model_id = "meta-llama/Llama-2-7b-chat-hf"
-model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map=device)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda:0")
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-past_key_values = DynamicCache(config=model.config)
+past_key_values = DynamicCache()
 messages = [{"role": "user", "content": "Hello, what's your name."}]
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda:0")
 
 generated_ids = inputs.input_ids
-cache_position = torch.arange(inputs.input_ids.shape[1], dtype=torch.int64, device=model.device)
+cache_position = torch.arange(inputs.input_ids.shape[1], dtype=torch.int64, device="cuda:0")
 max_new_tokens = 10
 
 for _ in range(max_new_tokens):
@@ -131,36 +134,6 @@ for _ in range(max_new_tokens):
 print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
 "[INST] Hello, what's your name. [/INST]  Hello! My name is LLaMA,"
 ```
-
-## Cache position
-
-The cache position tracks where to insert new tokens in the attention cache. It represents the *absolute* position of each token in the context, independent of padding or batch structure. Suppose you already cached `N` tokens and are now processing `K` new tokens. The cache position for the new tokens will range from `N` to `N + K - 1`. In other words, you're processing tokens at positions - `[N, N + 1, N + 2, ..., N + K - 1]`.
-
-Cache position is used internally for two purposes:
-
-1. Selecting new tokens to process in the input sequence and ensuring only tokens that haven’t been cached yet are passed to the model's `forward`.
-2. Storing key/value pairs at the correct positions in the cache. This is especially important for fixed-size caches, that pre-allocates a specific cache length.
-
-The generation loop usually takes care of the cache position, but if you're writing a custom generation method, it is important that cache positions are accurate since they are used to write and read key/value states into fixed slots.
-
-
-```py
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache, infer_device
-
-device = f"{infer_device()}:0"
-
-model_id = "meta-llama/Llama-2-7b-chat-hf"
-model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map=device)
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-messages = [{"role": "user", "content": "You are a helpful assistant."}]
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
-generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=10)
-
-```
-
-
 ## Legacy cache format
 
 Before the [`Cache`] class, the cache used to be stored as a tuple of tuples of tensors. This format is dynamic because it grows as text is generated, similar to [`DynamicCache`].
@@ -170,14 +143,14 @@ The legacy format is essentially the same data structure but organized different
 - The tensors have the same shape `[batch_size, num_heads, seq_len, head_dim]`.
 - The format is less flexible and doesn't support features like quantization or offloading.
 
-If your project depends on this legacy format, we recommend to convert to [`DynamicCache`] with [`~DynamicCache.from_legacy_cache`]. Note that legacy cache format is deprecated and not used anymore in `Transformers`. You can convert back to tuple format with [`DynamicCache.to_legacy_cache`] functions, which is helpful if you have custom logic for manipulating a cache in a specific format.
+If your project depends on this legacy format, you can convert between [`DynamicCache`] and a tuple of tuples as shown below with the [`~DynamicCache.from_legacy_cache`] and [`DynamicCache.to_legacy_cache`] functions. This is helpful if you have custom logic for manipulating a cache in a specific format.
 
 ```py
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", dtype=torch.float16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
 
 # `return_dict_in_generate=True` is required to return the cache and `return_legacy_cache` forces the returned cache
@@ -186,4 +159,4 @@ generation_outputs = model.generate(**inputs, return_dict_in_generate=True, retu
 
 cache = DynamicCache.from_legacy_cache(generation_outputs.past_key_values)
 legacy_format_cache = cache.to_legacy_cache()
-```
+```

docs/source/en/chat_extras.md

@@ -14,64 +14,64 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Tool use
+# Tools and RAG
 
-Chat models are commonly trained with support for "function-calling" or "tool-use". Tools are functions supplied by the user, which the model can choose to call as part of its response. For example, models could have access to a calculator tool to perform arithmetic without having to it internally.
+The [`~PreTrainedTokenizerBase.apply_chat_template`] method supports virtually any additional argument types - strings, lists, dicts - besides the chat message. This makes it possible to use chat templates for many use cases.
 
-This guide will demonstrate how to define tools, how to pass them to a chat model, and how to handle the model's output when it calls a tool.
+This guide will demonstrate how to use chat templates with tools and retrieval-augmented generation (RAG).
 
-## Passing tools
+## Tools
 
-When a model supports tool-use, pass functions to the `tools` argument of [`~PreTrainedTokenizerBase.apply_chat_template`].
-The tools are passed as either a [JSON schema](https://json-schema.org/learn) or Python functions. If you pass Python functions,
-the arguments, argument types, and function docstring are parsed in order to generate the JSON schema automatically.
+Tools are functions a large language model (LLM) can call to perform specific tasks. It is a powerful way to extend the capabilities of conversational agents with real-time information, computational tools, or access to large databases.
 
-Although passing Python functions is very convenient, the parser can only handle [Google-style](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings)
-docstrings. Refer to the examples below for how to format a tool-ready function.
+Follow the rules below when creating a tool.
 
+1. The function should have a descriptive name.
+2. The function arguments must have a type hint in the function header (don't include in the `Args` block).
+3. The function must have a [Google-style](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) docstring.
+4. The function can have a return type and `Returns` block, but these are optional because most tool use models ignore them.
+
+An example tool to get temperature and wind speed is shown below.
 
 ```py
-def get_current_temperature(location: str, unit: str):
+def get_current_temperature(location: str, unit: str) -> float:
     """
     Get the current temperature at a location.
     
     Args:
         location: The location to get the temperature for, in the format "City, Country"
         unit: The unit to return the temperature in. (choices: ["celsius", "fahrenheit"])
+    Returns:
+        The current temperature at the specified location in the specified units, as a float.
     """
     return 22.  # A real function should probably actually get the temperature!
 
-def get_current_wind_speed(location: str):
+def get_current_wind_speed(location: str) -> float:
     """
     Get the current wind speed in km/h at a given location.
     
     Args:
-        location: The location to get the wind speed for, in the format "City, Country"
+        location: The location to get the temperature for, in the format "City, Country"
+    Returns:
+        The current wind speed at the given location in km/h, as a float.
     """
     return 6.  # A real function should probably actually get the wind speed!
 
 tools = [get_current_temperature, get_current_wind_speed]
 ```
 
-You can optionally add a `Returns:` block to the docstring and a return type to the function header, but most models won't use this information. The parser will also ignore the actual code inside the function!
-
-What really matters is the function name, argument names, argument types, and docstring describing the function's purpose
-and the purpose of its arguments. These create the "signature" the model will use to decide whether to call the tool.
-
-## Tool-calling Example
-
 Load a model and tokenizer that supports tool-use like [NousResearch/Hermes-2-Pro-Llama-3-8B](https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B), but you can also consider a larger model like [Command-R](./model_doc/cohere) and [Mixtral-8x22B](./model_doc/mixtral) if your hardware can support it.
 
 ```py
 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, dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained( "NousResearch/Hermes-2-Pro-Llama-3-8B")
+tokenizer = AutoTokenizer.from_pretrained( "NousResearch/Hermes-2-Pro-Llama-3-8B")
+model = AutoModelForCausalLM.from_pretrained( "NousResearch/Hermes-2-Pro-Llama-3-8B", torch_dtype=torch.bfloat16, device_map="auto")
 ```
 
-Create a chat history.
+Create a chat message.
 
 ```py
 messages = [
@@ -80,11 +80,12 @@ messages = [
 ]
 ```
 
-Next, pass `messages` and a list of tools to [`~PreTrainedTokenizerBase.apply_chat_template`]. Tokenize the chat and generate a response.
+Pass `messages` and a list of tools to [`~PreTrainedTokenizerBase.apply_chat_template`]. Then you can pass the inputs to the model for generation.
 
 ```py
 inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)
+inputs = {k: v for k, v in inputs.items()}
+outputs = model.generate(**inputs, max_new_tokens=128)
 print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):]))
 ```
 
@@ -94,52 +95,60 @@ print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):]))
 </tool_call><|im_end|>
 ```
 
-The chat model called the `get_current_temperature` tool with the correct parameters from the docstring. It inferred France as the location based on Paris, and that it should use Celsius for the units of temperature.
+The chat model called the `get_current_temperature` tool with the correct parameters from the docstring. It inferred France as the location based on Paris, and that it should use Celsius for the units of temperature. 
 
-A model **cannot actually call the tool itself**. It requests a tool call, and it's your job to handle the call and append it and the result to the chat history.
-
-Hold the call in the `tool_calls` key of an `assistant` message. This is the recommended API, and should be supported by the chat template of most tool-using models.
+Now append the `get_current_temperature` function and these arguments to the chat message as `tool_call`. The `tool_call` dictionary should be provided to the `assistant` role instead of the `system` or `user`.
 
 > [!WARNING]
-> Although `tool_calls` is similar to the OpenAI API, the OpenAI API uses a JSON string as its `tool_calls` format. This may cause errors or strange model behavior if used in Transformers, which expects a dict.
+> The OpenAI API uses a JSON string as its `tool_call` format. This may cause errors or strange model behavior if used in Transformers, which expects a dict.
 
+<hfoptions id="tool-call">
+<hfoption id="Llama">
 
 ```py
 tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
 messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
 ```
 
-Append the tool response to the chat history with the `tool` role.
-
-```py
-messages.append({"role": "tool", "content": "22"})  # Note that the returned content is always a string!
-```
-
-Finally, allow the model to read the tool response and reply to the user.
+Allow the assistant to read the function outputs and chat with the user.
 
 ```py
 inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-out = model.generate(**inputs.to(model.device), max_new_tokens=128)
+inputs = {k: v for k, v in inputs.items()}
+out = model.generate(**inputs, max_new_tokens=128)
 print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
 ```
 
 ```txt
-The temperature in Paris, France right now is 22°C.<|im_end|>
+The temperature in Paris, France right now is approximately 12°C (53.6°F).<|im_end|>
 ```
 
-> [!WARNING]
-> Although the key in the assistant message is called `tool_calls`, in most cases, models only emit a single tool call at a time. Some older models emit multiple tool calls at the same time, but this is a
-> significantly more complex process, as you need to handle multiple tool responses at once and disambiguate them, often using tool call IDs. Please refer to the model card to see exactly what format a model expects for tool calls.
+</hfoption>
+<hfoption id="Mistral/Mixtral">
 
+For [Mistral](./model_doc/mistral) and [Mixtral](./model_doc/mixtral) models, you need an additional `tool_call_id`. The `tool_call_id` is 9 randomly generated alphanumeric characters assigned to the `id` key in the `tool_call` dictionary.
 
-## JSON schemas
+```py
+tool_call_id = "9Ae3bDc2F"
+tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
+messages.append({"role": "assistant", "tool_calls": [{"type": "function", "id": tool_call_id, "function": tool_call}]})
+```
 
-Another way to define tools is by passing a [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
+```py
+inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
+inputs = {k: v for k, v in inputs.items()}
+out = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
+```
 
-You can also manually call the low-level functions that convert Python functions to JSON schemas, and then check or edit the generated schemas. This is usually not necessary, but is useful for understanding the underlying mechanics. It's particularly important
-for chat template authors who need to access the JSON schema to render the tool definitions.
+</hfoption>
+</hfoptions>
 
-The  [`~PreTrainedTokenizerBase.apply_chat_template`] method uses the [get_json_schema](https://github.com/huggingface/transformers/blob/14561209291255e51c55260306c7d00c159381a5/src/transformers/utils/chat_template_utils.py#L205) function to convert Python functions to a JSON schema.
+## Schema
+
+[`~PreTrainedTokenizerBase.apply_chat_template`] converts functions into a [JSON schema](https://json-schema.org/learn/getting-started-step-by-step) which is passed to the chat template. A LLM never sees the code inside the function. In other words, a LLM doesn't care how the function works technically, it only cares about function **definition** and **arguments**.
+
+The JSON schema is automatically generated behind the scenes as long as your function follows the [rules](#tools) listed earlier above. But you can use [get_json_schema](https://github.com/huggingface/transformers/blob/14561209291255e51c55260306c7d00c159381a5/src/transformers/utils/chat_template_utils.py#L205) to manually convert a schema for more visibility or debugging.
 
 ```py
 from transformers.utils import get_json_schema
@@ -182,7 +191,12 @@ print(schema)
 }
 ```
 
-We won't go into the details of JSON schema itself here, since it's already [very well documented](https://json-schema.org/) elsewhere. We will, however, mention that you can pass JSON schema dicts to the `tools` argument of [`~PreTrainedTokenizerBase.apply_chat_template`] instead of Python functions:
+You can edit the schema or write one entirely from scratch. This gives you a lot of flexibility to define precise schemas for more complex functions.
+
+> [!WARNING]
+> Try keeping your function signatures simple and the arguments to a minimum. These are easier for a model to understand and use than complex functions for example with nested arguments.
+
+The example below demonstrates writing a schema manually and then passing it to [`~PreTrainedTokenizerBase.apply_chat_template`].
 
 ```py
 # A simple function that takes no arguments
@@ -224,4 +238,62 @@ model_input = tokenizer.apply_chat_template(
     messages,
     tools = [current_time, multiply]
 )
-```
+```
+
+## RAG
+
+Retrieval-augmented generation (RAG) models enhance a models existing knowledge by allowing it to search documents for additional information before returning a query. For RAG models, add a `documents` parameter to [`~PreTrainedTokenizerBase.apply_chat_template`]. This `documents` parameter should be a list of documents, and each document should be a single dict with `title` and `content` keys.
+
+> [!TIP]
+> The `documents` parameter for RAG isn't widely supported and many models have chat templates that ignore `documents`. Verify if a model supports `documents` by reading its model card or executing `print(tokenizer.chat_template)` to see if the `documents` key is present. [Command-R](https://hf.co/CohereForAI/c4ai-command-r-08-2024) and [Command-R+](https://hf.co/CohereForAI/c4ai-command-r-plus-08-2024) both support `documents` in their RAG chat templates.
+
+Create a list of documents to pass to the model.
+
+```py
+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..."
+    }
+]
+```
+
+Set `chat_template="rag"` in [`~PreTrainedTokenizerBase.apply_chat_template`] and generate a response.
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01-4bit")
+model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01-4bit", device_map="auto")
+device = model.device # Get the device the model is loaded on
+
+# Define conversation input
+conversation = [
+    {"role": "user", "content": "What has Man always dreamed of?"}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    conversation=conversation,
+    documents=documents,
+    chat_template="rag",
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt").to(device)
+
+# Generate a response 
+generated_tokens = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.3,
+    )
+
+# Decode and print the generated text along with generation prompt
+generated_text = tokenizer.decode(generated_tokens[0])
+print(generated_text)
+```

docs/source/en/chat_templating.md

@@ -14,19 +14,11 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Chat templates
+# Templates
 
-The [chat basics](./conversations) guide covers how to store chat histories and generate text from chat models using [`TextGenerationPipeline`]. 
+The [chat pipeline](./conversations) guide introduced [`TextGenerationPipeline`] and the concept of a chat prompt or chat template for conversing with a model. Underlying this high-level pipeline is the [`apply_chat_template`] method. A chat template is a part of the tokenizer and it specifies how to convert conversations into a single tokenizable string in the expected model format.
 
-This guide is intended for more advanced users, and covers the underlying classes and methods, as well as the key concepts for understanding what's actually going on when you chat with a model.
-
-The critical insight needed to understand chat models is this: All causal LMs, whether chat-trained or not, continue a sequence of tokens. When causal LMs are trained, the training usually begins with "pre-training" on a huge corpus of text, which creates a "base" model.
-These base models are then often "fine-tuned" for chat, which means training them on data that is formatted as a sequence of messages. The chat is still just a sequence of tokens, though! The list of `role` and `content` dictionaries that you pass
-to a chat model get converted to a token sequence, often with control tokens like `<|user|>` or `<|assistant|>` or `<|end_of_message|>`, which allow the model to see the chat structure. 
-There are many possible chat formats, and different models may use different formats or control tokens, even if they were fine-tuned from the same base model!
-
-Don't panic, though - you don't need to memorize every possible chat format in order to use chat models. Chat models come with **chat templates**, which indicate how they expect chats to be formatted.
-You can access these with the [`apply_chat_template`] method. Let's see two examples. Both of these models are fine-tuned from the same `Mistral-7B` base model:
+In the example below, Mistral-7B-Instruct and Zephyr-7B are finetuned from the same base model but they’re trained with different chat formats. Without chat templates, you have to manually write formatting code for each model and even minor errors can hurt performance. Chat templates offer a universal way to format chat inputs to any model.
 
 <hfoptions id="template">
 <hfoption id="Mistral">
@@ -69,24 +61,20 @@ tokenizer.apply_chat_template(chat, tokenize=False)
 </hfoption>
 </hfoptions>
 
-Mistral-7B-Instruct uses `[INST]` and `[/INST]` tokens to indicate the start and end of user messages, while Zephyr-7B uses `<|user|>` and `<|assistant|>` tokens to indicate speaker roles. This is why chat templates are important - with the wrong control tokens, these models would have drastically worse performance.
+This guide explores [`apply_chat_template`] and chat templates in more detail.
 
-## Using `apply_chat_template`
+## apply_chat_template
 
-The input to `apply_chat_template` should be structured as a list of dictionaries with `role` and `content` keys. The `role` key specifies the speaker, and the `content` key contains the message. The common roles are:
+Chats should be structured as a list of dictionaries with `role` and `content` keys. The `role` key specifies the speaker (usually between you and the system), and the `content` key contains your message. For the system, the `content` is a high-level description of how the model should behave and respond when you’re chatting with it.
 
- - `user` for messages from the user
- - `assistant` for messages from the model
- - `system` for directives on how the model should act (usually placed at the beginning of the chat)
-
-[`apply_chat_template`] takes this list and returns a formatted sequence. Set `tokenize=True` if you want to tokenize the sequence.
+Pass your messages to [`apply_chat_template`] to tokenize and format them. You can set [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) to `True` to indicate the start of a message.
 
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto", dtype=torch.bfloat16)
+model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto", torch_dtype=torch.bfloat16)
 
 messages = [
     {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate",},
@@ -95,7 +83,6 @@ messages = [
 tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
 print(tokenizer.decode(tokenized_chat[0]))
 ```
-
 ```md
 <|system|>
 You are a friendly chatbot who always responds in the style of a pirate</s>
@@ -104,7 +91,7 @@ How many helicopters can a human eat in one sitting?</s>
 <|assistant|>
 ```
 
-Pass the tokenized chat to [`~GenerationMixin.generate`] to generate a response.
+Now pass the tokenized chat to [`~GenerationMixin.generate`] to generate a response.
 
 ```py
 outputs = model.generate(tokenized_chat, max_new_tokens=128) 
@@ -119,17 +106,10 @@ How many helicopters can a human eat in one sitting?</s>
 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.
 ```
 
-> [!WARNING]
-> Some tokenizers add special `<bos>` and `<eos>` tokens. Chat templates should already include all the necessary special tokens, and adding additional special tokens is often incorrect or duplicated, hurting model performance. When you format text with `apply_chat_template(tokenize=False)`, make sure you set `add_special_tokens=False` if you tokenize later to avoid duplicating these tokens.
-> This isn’t an issue if you use `apply_chat_template(tokenize=True)`, which means it's usually the safer option!
-
 ### add_generation_prompt
+The [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) parameter adds tokens that indicate the start of a response. This ensures the chat model generates a system response instead of continuing a users message.
 
-You may have noticed the [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) argument in the above examples. 
-This argument adds tokens to the end of the chat that indicate the start of an `assistant` response. Remember: Beneath all the chat abstractions, chat models are still just language models that continue a sequence of tokens!
-If you include tokens that tell it that it's now in an `assistant` response, it will correctly write a response, but if you don't include these tokens, the model may get confused and do something strange, like **continuing** the user's message instead of replying to it! 
-
-Let's see an example to understand what `add_generation_prompt` is actually doing. First, let's format a chat without `add_generation_prompt`:
+Not all models require generation prompts, and some models, like [Llama](./model_doc/llama), don’t have any special tokens before the system response. In this case, [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) has no effect.
 
 ```py
 tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
@@ -144,32 +124,11 @@ Nice to meet you!<|im_end|>
 Can I ask a question?<|im_end|>
 ```
 
-Now, let's format the same chat with `add_generation_prompt=True`:
-
-```py
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-tokenized_chat
-```
-```md
-<|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
-
-```
-
-When `add_generation_prompt=True`, `<|im_start|>assistant` is added at the end to indicate the start of an `assistant` message. This lets the model know an `assistant` response is next.
-
-Not all models require generation prompts, and some models, like [Llama](./model_doc/llama), don’t have any special tokens before the `assistant` response. In these cases, [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) has no effect.
-
 ### continue_final_message
 
 The [continue_final_message](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.continue_final_message) parameter controls whether the final message in the chat should be continued or not instead of starting a new one. It removes end of sequence tokens so that the model continues generation from the final message.
 
-This is useful for “prefilling” a model response. In the example below, the model generates text that continues the JSON string rather than starting a new message. It can be very useful for improving the accuracy of instruction following when you know how to start its replies.
+This is useful for “prefilling” a model response. In the example below, the model generates text that continues the JSON string rather than starting a new message. It can be very useful for improving the accuracy for instruction following when you know how to start its replies.
 
 ```py
 chat = [
@@ -184,12 +143,52 @@ model.generate(**formatted_chat)
 > [!WARNING]
 > You shouldn’t use [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) and [continue_final_message](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.continue_final_message) together. The former adds tokens that start a new message, while the latter removes end of sequence tokens. Using them together returns an error.
 
-[`TextGenerationPipeline`] sets [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) to `True` by default to start a new message. However, if the final message in the chat has the `assistant` role, it assumes the message is a prefill and switches to `continue_final_message=True`. This is because most models don’t support multiple consecutive assistant messages. To override this behavior, explicitly pass the [continue_final_message](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.continue_final_message) argument to the pipeline.
+[`TextGenerationPipeline`] sets [add_generation_prompt](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.add_generation_prompt) to `True` by default to start a new message. However, if the final message in the chat has the “assistant” role, it assumes the message is a prefill and switches to `continue_final_message=True`. This is because most models don’t support multiple consecutive assistant messages. To override this behavior, explicitly pass the [continue_final_message](https://huggingface.co/docs/transformers/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.apply_chat_template.continue_final_message) to the pipeline.
 
+## Multiple templates
+
+A model may have several different templates for different use cases. For example, a model may have a template for regular chat, tool use, and RAG.
+
+When there are multiple templates, the chat template is a dictionary. Each key corresponds to the name of a template. [`apply_chat_template`] handles multiple templates based on their name. It looks for a template named `default` in most cases and if it can’t find one, it raises an error.
+
+For a tool calling template, if a user passes a `tools` parameter and a `tool_use` template exists, the tool calling template is used instead of `default`.
+
+To access templates with other names, pass the template name to the `chat_template` parameter in [`apply_chat_template`]. For example, if you’re using a RAG template then set `chat_template="rag"`.
+
+It can be confusing to manage multiple templates though, so we recommend using a single template for all use cases. Use Jinja statements like `if tools is defined` and `{% macro %}` definitions to wrap multiple code paths in a single template.
+
+## Template selection
+
+It is important to set a chat template format that matches the template format a model was pretrained on, otherwise performance may suffer. Even if you’re training the model further, performance is best if the chat tokens are kept constant.
+
+But if you’re training a model from scratch or finetuning a model for chat, you have more options to select a template. For example, [ChatML](https://github.com/openai/openai-python/blob/release-v0.28.0/chatml.md) is a popular format that is flexbile enough to handle many use cases. It even includes support for [generation prompts](#add_generation_prompt), but it doesn’t add beginning-of-string (`BOS`) or end-of-string (`EOS`) tokens. If your model expects `BOS` and `EOS` tokens, set `add_special_tokens=True` and make sure to add them to your template.
+
+```py
+{%- for message in messages %}
+    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
+{%- endfor %}
+```
+
+Set the template with the following logic to support [generation prompts](#add_generation_prompt). The template wraps each message with `<|im_start|>` and `<|im_end|>` tokens and writes the role as a string. This allows you to easily customize the roles you want to train with.
+
+```py
+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 %}"
+```
+
+The `user`, `system` and `assistant` roles are standard roles in chat templates. We recommend using these roles when it makes sense, especially if you’re using your model with the [`TextGenerationPipeline`].
+
+```py
+<|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|>
+```
 
 ## Model training
 
-Training a model with a chat template is a good way to ensure the template matches the tokens the model was trained on. Apply the chat template as a preprocessing step to your dataset. Set `add_generation_prompt=False` because the additional tokens to prompt an assistant response aren’t helpful during training.
+Training a model with a chat template is a good way to ensure a chat template matches the tokens a model is trained on. Apply the chat template as a preprocessing step to your dataset. Set `add_generation_prompt=False` because the additional tokens to prompt an assistant response aren’t helpful during training.
 
 An example of preprocessing a dataset with a chat template is shown below.
 
@@ -220,3 +219,11 @@ The sun.</s>
 ```
 
 After this step, you can continue following the [training recipe](./tasks/language_modeling) for causal language models using the `formatted_chat` column.
+
+Some tokenizers add special `<bos>` and `<eos>` tokens. Chat templates should already include all the necessary special tokens, and adding additional special tokens is often incorrect or duplicated, hurting model performance. When you format text with `apply_chat_template(tokenize=False)`, make sure you set `add_special_tokens=False` as well to avoid duplicating them.
+
+```py
+apply_chat_template(messages, tokenize=False, add_special_tokens=False)
+```
+
+This isn’t an issue if `apply_chat_template(tokenize=True)`.

docs/source/en/chat_templating_multimodal.md

@@ -14,21 +14,22 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Multimodal chat templates
+# Multimodal templates
 
-Multimodal chat models accept inputs like images, audio or video, in addition to text. The `content` key in a multimodal chat history is a list containing multiple items of different types. This is unlike text-only chat models whose `content` key is a single string.
+Multimodal model chat templates expect a similar [template](./chat_templating) as text-only models. It needs `messages` that includes a dictionary of the `role` and `content`.
 
+Multimodal templates are included in the [Processor](./processors) class and require an additional `type` key for specifying whether the included content is an image, video, or text.
 
-In the same way the [Tokenizer](./fast_tokenizer) class handles chat templates and tokenization for text-only models, 
-the [Processor](./processors) class handles preprocessing, tokenization and chat templates for multimodal models. Their [`~ProcessorMixin.apply_chat_template`] methods are almost identical.
-
-This guide will show you how to chat with multimodal models with the high-level [`ImageTextToTextPipeline`] and at a lower level using the [`~ProcessorMixin.apply_chat_template`] and [`~GenerationMixin.generate`] methods.
+This guide will show you how to format chat templates for multimodal models as well as some best practices for configuring the template
 
 ## ImageTextToTextPipeline
 
 [`ImageTextToTextPipeline`] is a high-level image and text generation class with a “chat mode”. Chat mode is enabled when a conversational model is detected and the chat prompt is [properly formatted](./llm_tutorial#wrong-prompt-format).
 
-Add image and text blocks to the `content` key in the chat history.
+Start by building a chat history with the following two roles.
+
+- `system` describes how the model should behave and respond when you’re chatting with it. This role isn’t supported by all chat models.
+- `user` is where you enter your first message to the model.
 
 ```py
 messages = [
@@ -46,35 +47,39 @@ messages = [
 ]
 ```
 
-Create an [`ImageTextToTextPipeline`] and pass the chat to it. For large models, setting [device_map=“auto”](./models#big-model-inference) helps load the model quicker and automatically places it on the fastest device available. Setting the data type to [auto](./models#model-data-type) also helps save memory and improve speed.
+Create a [`ImageTextToTextPipeline`] and pass the chat to it. For large models, setting [device_map=“auto”](./models#big-model-inference) helps load the model quicker and automatically places it on the fastest device available. Changing the data type to [torch.bfloat16](./models#model-data-type) also helps save memory.
+
+> [!TIP]
+> The [`ImageTextToTextPipeline`] accepts chats in the OpenAI format to make inference easier and more accessible. 
 
 ```python
 import torch
 from transformers import pipeline
 
-pipe = pipeline("image-text-to-text", model="Qwen/Qwen2.5-VL-3B-Instruct", device_map="auto", dtype="auto")
-out = pipe(text=messages, max_new_tokens=128)
-print(out[0]['generated_text'][-1]['content'])
+pipeline = pipeline("image-text-to-text", model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf", device="cuda", torch_dtype=torch.float16)
+pipeline(text=messages, max_new_tokens=50, return_full_text=False)
+[{'input_text': [{'role': 'system',
+    'content': [{'type': 'text',
+      'text': 'You are a friendly chatbot who always responds in the style of a pirate'}]},
+   {'role': 'user',
+    'content': [{'type': 'image',
+      'url': 'http://images.cocodataset.org/val2017/000000039769.jpg'},
+     {'type': 'text', 'text': 'What are these?'}]}],
+  'generated_text': 'The image shows two cats lying on a pink surface, which appears to be a cushion or a soft blanket. The cat on the left has a striped coat, typical of tabby cats, and is lying on its side with its head resting on the'}]
 ```
 
+## Image inputs
 
-```
-Ahoy, me hearty! These be two feline friends, likely some tabby cats, taking a siesta on a cozy pink blanket. They're resting near remote controls, perhaps after watching some TV or just enjoying some quiet time together. Cats sure know how to find comfort and relaxation, don't they?
-```
-
-Aside from the gradual descent from pirate-speak into modern American English (it **is** only a 3B model, after all), this is correct!
-
-## Using `apply_chat_template`
-
-Like [text-only models](./chat_templating), use the [`~ProcessorMixin.apply_chat_template`] method to prepare the chat messages for multimodal models. 
-This method handles the tokenization and formatting of the chat messages, including images and other media types. The resulting inputs are passed to the model for generation.
+For multimodal models that accept images like [LLaVA](./model_doc/llava), include the following in `content` as shown below.
 
+- The content `"type"` can be an `"image"` or `"text"`.
+- For images, it can be a link to the image (`"url"`), a file path (`"path"`), or `"base64"`. Images are automatically loaded, processed, and prepared into pixel values as inputs to the model.
 
 ```python
-from transformers import AutoProcessor, AutoModelForImageTextToText
+from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 
-model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", device_map="auto", torch_dtype="auto")
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
+model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
+processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
 
 messages = [
     {
@@ -91,28 +96,14 @@ messages = [
 ]
 ```
 
-Pass `messages` to [`~ProcessorMixin.apply_chat_template`] to tokenize the input content. Unlike text models, the output of `apply_chat_template`
-contains a `pixel_values` key with the preprocessed image data, in addition to the tokenized text.
+Pass `messages` to [`~ProcessorMixin.apply_chat_template`] to tokenize the input content and return the `input_ids` and `pixel_values`.
 
 ```py
 processed_chat = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
-print(list(processed_chat.keys()))
+print(processed_chat.keys())
 ```
 
-
-```
-['input_ids', 'attention_mask', 'pixel_values', 'image_grid_thw']
-```
-
-Pass these inputs to [`~GenerationMixin.generate`].
-
-```python
-out = model.generate(**processed_chat.to(model.device), max_new_tokens=128)
-print(processor.decode(out[0]))
-```
-
-The decoded output contains the full conversation so far, including the user message and the placeholder tokens that contain the image information. You may need to trim the previous conversation from the output before displaying it to the user.
-
+These inputs are now ready to be used in [`~GenerationMixin.generate`].
 
 ## Video inputs
 
@@ -120,7 +111,6 @@ Some vision models also support video inputs. The message format is very similar
 
 - The content `"type"` should be `"video"` to indicate the content is a video.
 - For videos, it can be a link to the video (`"url"`) or it could be a file path (`"path"`). Videos loaded from a URL can only be decoded with [PyAV](https://pyav.basswood-io.com/docs/stable/) or [Decord](https://github.com/dmlc/decord).
-- In addition to loading videos from a URL or file path, you can also pass decoded video data directly. This is useful if you’ve already preprocessed or decoded video frames elsewhere in memory (e.g., using OpenCV, decord, or torchvision). You don't need to save to files or store it in an URL.
 
 > [!WARNING]
 > Loading a video from `"url"` is only supported by the PyAV or Decord backends.
@@ -147,52 +137,6 @@ messages = [
 ]
 ```
 
-### Example: Passing decoded video objects
-```python
-import numpy as np
-
-video_object1 = np.random.randint(0, 255, size=(16, 224, 224, 3), dtype=np.uint8),
-
-messages = [
-    {
-        "role": "system",
-        "content": [{"type": "text", "text": "You are a friendly chatbot who always responds in the style of a pirate"}],
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "video", "video": video_object1},
-            {"type": "text", "text": "What do you see in this video?"}
-        ],
-    },
-]
-```
-You can also use existing (`"load_video()"`) function to load a video, edit the video in memory and pass it in the messages.
-```python
-
-# Make sure a video backend library (pyav, decord, or torchvision) is available.
-from transformers.video_utils import load_video
-
-# load a video file in memory for testing
-video_object2, _ = load_video(
-    "https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_10MB.mp4"
-)
-
-messages = [
-    {
-        "role": "system",
-        "content": [{"type": "text", "text": "You are a friendly chatbot who always responds in the style of a pirate"}],
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "video", "video": video_object2},
-            {"type": "text", "text": "What do you see in this video?"}
-        ],
-    },
-]
-```
-
 Pass `messages` to [`~ProcessorMixin.apply_chat_template`] to tokenize the input content. There are a few extra parameters to include in [`~ProcessorMixin.apply_chat_template`] that controls the sampling process.
 
 The `video_load_backend` parameter refers to a specific framework to load a video. It supports [PyAV](https://pyav.basswood-io.com/docs/stable/), [Decord](https://github.com/dmlc/decord), [OpenCV](https://github.com/opencv/opencv), and [torchvision](https://pytorch.org/vision/stable/index.html).
@@ -231,7 +175,7 @@ processed_chat = processor.apply_chat_template(
     add_generation_prompt=True,
     tokenize=True,
     return_dict=True,
-    video_fps=16,
+    video_fps=32,
     video_load_backend="decord",
 )
 print(processed_chat.keys())
@@ -272,3 +216,28 @@ print(processed_chat.keys())
 </hfoption>
 </hfoptions>
 
+## Template configuration
+
+You can create a custom chat template with [Jinja](https://jinja.palletsprojects.com/en/3.1.x/templates/) and set it with [`~ProcessorMixin.apply_chat_template`]. Refer to the [Template writing](./chat_templating_writing) guide for more details.
+
+For example, to enable a template to handle a *list of content* from multiple modalities while still supporting plain strings for text-only inference, specify how to handle the `content['type']` if it is an image or text as shown below in the Llama 3.2 Vision Instruct [template](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct/blob/main/chat_template.json).
+
+```jinja
+{% for message in messages %}
+{% if loop.index0 == 0 %}{{ bos_token }}{% endif %}
+{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' }}
+{% if message['content'] is string %}
+{{ message['content'] }}
+{% else %}
+{% for content in message['content'] %}
+{% if content['type'] == 'image' %}
+{{ '<|image|>' }}
+{% elif content['type'] == 'text' %}
+{{ content['text'] }}
+{% endif %}
+{% endfor %}
+{% endif %}
+{{ '<|eot_id|>' }}
+{% endfor %}
+{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}
+```