Merge branch 'main' into tensor-cache

.circleci/config.yml

@@ -31,14 +31,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: if [ "$CIRCLE_PULL_REQUEST" == "" -a "$CIRCLE_BRANCH" != "main" ]; then echo "Not a PR and not the main branch, skip test!"; circleci-agent step halt; fi
-            - run: 'curl -L -H "Accept: application/vnd.github+json" -H "X-GitHub-Api-Version: 2022-11-28" https://api.github.com/repos/huggingface/transformers/pulls/${CIRCLE_PULL_REQUEST##*/} >> github.txt'
-            - run: cat github.txt
-            - run: (python3 -c 'import json; from datetime import datetime; fp = open("github.txt"); data = json.load(fp); fp.close(); f = "%Y-%m-%dT%H:%M:%SZ"; created = datetime.strptime(data["created_at"], f); updated = datetime.strptime(data["updated_at"], f); s = (updated - created).total_seconds(); print(int(s))' || true) > elapsed.txt
-            - run: if [ "$(cat elapsed.txt)" == "" ]; then echo 60 > elapsed.txt; fi
-            - run: cat elapsed.txt
-            - run: if [ "$(cat elapsed.txt)" -lt "30" ]; then echo "PR is just opened, wait some actions from GitHub"; sleep 30; fi
-            - run: 'if grep -q "\"draft\": true," github.txt; then echo "draft mode, skip test!"; circleci-agent step halt; fi'
             - run: uv pip install -U -e .
             - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
             - run: mkdir -p test_preparation
@@ -66,7 +58,7 @@ jobs:
             - run:
                 name: "Prepare pipeline parameters"
                 command: |
-                    python utils/process_test_artifacts.py
+                    python utils/process_test_artifacts.py 
 
             # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
             # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
@@ -118,7 +110,7 @@ jobs:
             - run:
                 name: "Prepare pipeline parameters"
                 command: |
-                    python utils/process_test_artifacts.py
+                    python utils/process_test_artifacts.py 
 
             # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
             # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
@@ -178,7 +170,7 @@ jobs:
             - store_artifacts:
                   path: ~/transformers/installed.txt
             - run: python utils/check_copies.py
-            - run: python utils/check_modular_conversion.py --num_workers 4
+            - run: python utils/check_modular_conversion.py
             - run: python utils/check_table.py
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py

.circleci/create_circleci_config.py

@@ -28,6 +28,8 @@ COMMON_ENV_VARIABLES = {
     "TRANSFORMERS_IS_CI": True,
     "PYTEST_TIMEOUT": 120,
     "RUN_PIPELINE_TESTS": False,
+    "RUN_PT_TF_CROSS_TESTS": False,
+    "RUN_PT_FLAX_CROSS_TESTS": False,
 }
 # Disable the use of {"s": None} as the output is way too long, causing the navigation on CircleCI impractical
 COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "vvv": None, "rsfE":None}
@@ -175,6 +177,23 @@ class CircleCIJob:
 
 
 # JOBS
+torch_and_tf_job = CircleCIJob(
+    "torch_and_tf",
+    docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
+    additional_env={"RUN_PT_TF_CROSS_TESTS": True},
+    marker="is_pt_tf_cross_test",
+    pytest_options={"rA": None, "durations": 0},
+)
+
+
+torch_and_flax_job = CircleCIJob(
+    "torch_and_flax",
+    additional_env={"RUN_PT_FLAX_CROSS_TESTS": True},
+    docker_image=[{"image":"huggingface/transformers-torch-jax-light"}],
+    marker="is_pt_flax_cross_test",
+    pytest_options={"rA": None, "durations": 0},
+)
+
 torch_job = CircleCIJob(
     "torch",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
@@ -334,7 +353,7 @@ doc_test_job = CircleCIJob(
     pytest_num_workers=1,
 )
 
-REGULAR_TESTS = [torch_job, tf_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
+REGULAR_TESTS = [torch_and_tf_job, torch_and_flax_job, torch_job, tf_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
 EXAMPLES_TESTS = [examples_torch_job, examples_tensorflow_job]
 PIPELINE_TESTS = [pipelines_torch_job, pipelines_tf_job]
 REPO_UTIL_TESTS = [repo_utils_job]

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -106,7 +106,6 @@ body:
       label: Reproduction
       description: |
         Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        Please include relevant config information with your code, for example your Trainers, TRL, Peft, and DeepSpeed configs.
         If you have code snippets, error messages, stack traces please provide them here as well.
         Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
         Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.

.github/workflows/benchmark.yml

@@ -18,8 +18,7 @@ jobs:
     name: Benchmark
     strategy:
       matrix:
-        # group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus] (A100 runner is not enabled)
-        group: [aws-g5-4xlarge-cache]
+        group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus]
     runs-on:
       group: ${{ matrix.group }}
     if: |
@@ -64,7 +63,7 @@ jobs:
             commit_id=$GITHUB_SHA
           fi
           commit_msg=$(git show -s --format=%s | cut -c1-70)
-          python3 benchmark/benchmarks_entrypoint.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
+          python3 benchmark/benchmarks_entrypoint.py "${{ github.head_ref || github.ref_name }}" "$commit_id" "$commit_msg"
         env:
           HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
           # Enable this to see debug logs
@@ -73,4 +72,3 @@ jobs:
           PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
           PGUSER: transformers_benchmarks
           PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

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

@@ -26,7 +26,7 @@ jobs:
 
     strategy:
       matrix:
-        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "jax-light", "examples-torch",  "examples-tf"]
+        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "torch-jax-light", "jax-light", "examples-torch",  "examples-tf"]
     continue-on-error: true
 
     steps:
@@ -34,11 +34,11 @@ jobs:
         name: Set tag
         run: |
               if ${{contains(github.event.head_commit.message, '[build-ci-image]')}}; then
-                  echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV"
+                  echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV" 
                   echo "setting it to DEV!"
               else
                   echo "TAG=huggingface/transformers-${{ matrix.file }}" >> "$GITHUB_ENV"
-
+                  
               fi
       -
         name: Set up Docker Buildx

.github/workflows/change_pr_to_draft.yml

@@ -1,22 +0,0 @@
-name: Change PR to draft
-
-on:
-  pull_request_target:
-    types: [opened, reopened]
-
-jobs:
-  convert_pr_to_draft:
-    runs-on: ubuntu-22.04
-    name: Convert PR to draft
-    if: github.event.pull_request.draft == false
-    steps:
-      - name: Convert PR to draft
-        shell: bash
-        env:
-          PR_NUMBER: ${{ github.event.number }}
-          GH_TOKEN: ${{ github.token }}
-          REPO: ${{ github.repository }}
-        run: |
-          echo $PR_NUMBER
-          gh pr ready $PR_NUMBER --repo $REPO --undo
-          gh pr comment $PR_NUMBER --repo $REPO --body "Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. When it is ready for review, please click the \`Ready for review\` button (at the button of the PR page)."

.github/workflows/check_failed_model_tests.yml

@@ -22,6 +22,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 

.github/workflows/model_jobs.yml

@@ -30,6 +30,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:

.github/workflows/model_jobs_amd.yml

@@ -30,6 +30,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:

.github/workflows/new_model_pr_merged_notification.yml

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

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

@@ -7,13 +7,14 @@ on:
 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
+  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 
+  RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
   get_modified_models:
@@ -24,13 +25,13 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-
+      
       - name: Get changed files
         id: changed-files
         uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
         with:
           files: src/transformers/models/**
-
+      
       - name: Run step if only the files listed above change
         if: steps.changed-files.outputs.any_changed == 'true'
         id: set-matrix
@@ -59,41 +60,41 @@ jobs:
     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:
+      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]"
+          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
@@ -102,13 +103,13 @@ jobs:
           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
@@ -118,7 +119,7 @@ jobs:
 
       - name: Post to Slack
         if: always()
-        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        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 }}

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

@@ -22,6 +22,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -29,7 +30,7 @@ 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"]'), 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"]'), 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:
@@ -97,7 +98,6 @@ jobs:
     if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
     outputs:
       models: ${{ steps.models_to_run.outputs.models }}
-      quantizations: ${{ steps.models_to_run.outputs.quantizations }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -121,8 +121,6 @@ jobs:
           python -m pip install GitPython
           python utils/pr_slow_ci_models.py --message "$PR_COMMENT" | tee output.txt
           echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV
-          python utils/pr_slow_ci_models.py --message "$PR_COMMENT" --quantization | tee output2.txt
-          echo "quantizations=$(tail -n 1 output2.txt)" >> $GITHUB_ENV
 
       - name: Show models to test
         id: models_to_run
@@ -130,12 +128,10 @@ jobs:
           echo "${{ env.models }}"
           echo "models=${{ env.models }}" >> $GITHUB_ENV
           echo "models=${{ env.models }}" >> $GITHUB_OUTPUT
-          echo "${{ env.quantizations }}"
-          echo "quantizations=${{ env.quantizations }}" >> $GITHUB_OUTPUT
 
   reply_to_comment:
     name: Reply to the comment
-    if: ${{ needs.get-tests.outputs.models != '[]'  || needs.get-tests.outputs.quantizations != '[]' }}
+    if: ${{ needs.get-tests.outputs.models != '[]' }}
     needs: [get-pr-number, get-tests]
     permissions:
       pull-requests: write
@@ -145,18 +141,17 @@ jobs:
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           MODELS: ${{ needs.get-tests.outputs.models }}
-          BODY: "This comment contains run-slow, running the specified jobs:\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}"
         run: |
           gh api \
             --method POST \
             -H "Accept: application/vnd.github+json" \
             -H "X-GitHub-Api-Version: 2022-11-28" \
             repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f "body=This comment contains run-slow, running the specified jobs: ${{ env.BODY }} ..."
+            -f "body=This comment contains run-slow, running the specified jobs: ${{ env.MODELS }} ..."
 
   create_run:
     name: Create run
-    if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }}
+    if: ${{ needs.get-tests.outputs.models != '[]' }}
     needs: [get-sha, get-tests, reply_to_comment]
     permissions:
       statuses: write
@@ -178,20 +173,20 @@ jobs:
             -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Slow CI job" -f "context=pytest/custom-tests"
 
   run_models_gpu:
-    name: Run all tests for the model
-    if: ${{ needs.get-tests.outputs.models != '[]' }}
-    needs: [get-pr-number, get-sha, get-tests, create_run]
-    strategy:
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
-    runs-on:
-       group: '${{ 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/
-    steps:
+      name: Run all tests for the model
+      if: ${{ needs.get-tests.outputs.models != '[]' }}
+      needs: [get-pr-number, get-sha, get-tests, create_run]
+      strategy:
+        fail-fast: false
+        matrix:
+          folders: ${{ fromJson(needs.get-tests.outputs.models) }}
+          machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+      runs-on:
+         group: '${{ 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/
+      steps:
       - name: Echo input and matrix info
         shell: bash
         run: |
@@ -211,20 +206,20 @@ jobs:
       - name: Checkout to PR merge commit
         working-directory: /transformers
         run: |
-          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git log -1 --format=%H
+            git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+            git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+            git log -1 --format=%H
 
       - name: Verify merge commit SHA
         env:
           VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
         working-directory: /transformers
         run: |
-          PR_MERGE_SHA=$(git log -1 --format=%H)
-          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
-            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-            exit -1;
-          fi
+            PR_MERGE_SHA=$(git log -1 --format=%H)
+            if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
+              echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
+              exit -1;
+            fi
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -284,106 +279,9 @@ jobs:
           name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
           path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
 
-  run_quantization_torch_gpu:
-    name: Run all tests for a quantization
-    if: ${{ needs.get-tests.outputs.quantizations != '[]' }}
-    needs: [get-pr-number, get-sha, get-tests, create_run]
-    strategy:
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
-    container:
-      image: huggingface/transformers-quantization-latest-gpu
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          matrix_folders=${matrix_folders/'quantization/'/'quantization_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
-
-      - name: Checkout to PR merge commit
-        working-directory: /transformers
-        run: |
-          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-          git log -1 --format=%H
-
-      - name: Verify merge commit SHA
-        env:
-          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
-        working-directory: /transformers
-        run: |
-          PR_MERGE_SHA=$(git log -1 --format=%H)
-          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
-            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-            exit -1;
-          fi
-
-      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Environment
-        working-directory: /transformers
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Run quantization tests on GPU
-        working-directory: /transformers
-        run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
-
-      - name: Make sure report directory exists
-        shell: bash
-        run: |
-          mkdir -p /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports"
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
-
   update_run_status:
     name: Update Check Run Status
-    needs: [get-sha, create_run, run_models_gpu, run_quantization_torch_gpu]
+    needs: [get-sha, create_run, run_models_gpu]
     permissions:
       statuses: write
     if: ${{ always() && needs.create_run.result == 'success' }}
@@ -391,17 +289,16 @@ jobs:
     env:
       GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-      STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.run_models_gpu.result) && contains(fromJSON('["skipped", "success"]'), needs.run_quantization_torch_gpu.result) }}
     steps:
       - name: Get `run_models_gpu` job status
         run: |
           echo "${{ needs.run_models_gpu.result }}"
-          echo "${{ needs.run_quantization_torch_gpu.result }}"
-          echo $STATUS_OK
-          if [ "$STATUS_OK" = "true" ]; then
+          if [ "${{ needs.run_models_gpu.result }}" = "cancelled" ]; then
+            echo "STATUS=failure" >> $GITHUB_ENV
+          elif [ "${{ needs.run_models_gpu.result }}" = "skipped" ]; then
             echo "STATUS=success" >> $GITHUB_ENV
           else
-            echo "STATUS=failure" >> $GITHUB_ENV
+            echo "STATUS=${{ needs.run_models_gpu.result }}" >> $GITHUB_ENV
           fi
 
       - name: Update PR commit statuses

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

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

.github/workflows/self-push.yml

@@ -24,6 +24,7 @@ env:
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -292,7 +293,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-
+          
       - name: Update clone using environment variables
         working-directory: /transformers
         run: |
@@ -405,7 +406,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-
+          
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -515,7 +516,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-
+          
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -647,6 +648,6 @@ jobs:
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |
           pip install huggingface_hub
-          pip install slack_sdk
+          pip install slack_sdk 
           pip show slack_sdk
           python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

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

@@ -15,7 +15,7 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_models_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
@@ -26,7 +26,7 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_pipelines_torch_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
@@ -37,7 +37,7 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_examples_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
@@ -48,7 +48,7 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       runner: mi250
       docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250

.github/workflows/self-scheduled.yml

@@ -40,6 +40,7 @@ env:
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
   SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
+  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
   NUM_SLICES: 2
 
@@ -365,7 +366,7 @@ jobs:
         run: |
           python3 -m pip uninstall -y deepspeed
           rm -rf DeepSpeed
-          git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build
+          git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
           DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI
@@ -570,4 +571,4 @@ jobs:
     with:
       docker: ${{ inputs.docker }}
       start_sha: ${{ github.sha }}
-    secrets: inherit
+    secrets: inherit

.github/workflows/ssh-runner.yml

@@ -5,7 +5,7 @@ on:
     inputs:
       runner_type:
         description: 'Type of runner to test (a10 or t4)'
-        required: true
+        required: true 
       docker_image:
         description: 'Name of the Docker image'
         required: true
@@ -15,14 +15,15 @@ on:
 
 env:
   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
+  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 
   CUDA_VISIBLE_DEVICES: 0,1
+  RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
   get_runner:
@@ -77,7 +78,7 @@ jobs:
       - name: Show installed libraries and their versions
         working-directory: /transformers
         run: pip freeze
-
+      
       - name: NVIDIA-SMI
         run: |
           nvidia-smi

.github/workflows/trufflehog.yml

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

CONTRIBUTING.md

@@ -343,6 +343,8 @@ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/t
 
 Like the slow tests, there are other environment variables available which are not enabled by default during testing:
 - `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers.
+- `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration.
+- `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration.
 
 More environment variables and additional information can be found in the [testing_utils.py](https://github.com/huggingface/transformers/blob/main/src/transformers/testing_utils.py).
 

README.md

@@ -283,7 +283,7 @@ If you'd like to play with the examples or need the bleeding edge of the code an
 ```
 git clone https://github.com/huggingface/transformers.git
 cd transformers
-pip install .
+pip install
 ```
 
 ### With conda

awesome-transformers.md

@@ -15,7 +15,7 @@ to add it.
 
 Keywords: Open-source, LLaMa, GPT-J, instruction, assistant
 
-## [recommenders](https://github.com/recommenders-team/recommenders)
+## [recommenders](https://github.com/microsoft/recommenders)
 
 This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization
 
@@ -39,15 +39,15 @@ MindsDB is a low-code ML platform, which automates and integrates several ML fra
 
 Keywords: Database, low-code, AI table
 
-## [langchain](https://github.com/langchain-ai/langchain)
+## [langchain](https://github.com/hwchase17/langchain)
 
-[langchain](https://github.com/langchain-ai/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
+[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
 
 Keywords: LLMs, Large Language Models, Agents, Chains
 
-## [LlamaIndex](https://github.com/run-llama/llama_index)
+## [LlamaIndex](https://github.com/jerryjliu/llama_index)
 
-[LlamaIndex](https://github.com/run-llama/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
+[LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
 
 Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation 
 
@@ -146,9 +146,9 @@ Keywords: Framework, simplicity, NLP
 
 Keywords: LLM, Agents, HF Hub
 
-## [transformers.js](https://github.com/huggingface/transformers.js/)
+## [transformers.js](https://xenova.github.io/transformers.js/)
 
-[transformers.js](https://github.com/huggingface/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
+[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
 
 Keywords: Transformers, JavaScript, browser
 
@@ -437,7 +437,7 @@ Keywords: DALL-E, Russian
 
 Keywords: Knowledge Extraction, Knowledge Graphs
 
-## [Nebuly](https://github.com/nebuly-ai/optimate)
+## [Nebuly](https://github.com/nebuly-ai/nebuly)
 
 Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
 

conftest.py

@@ -61,6 +61,7 @@ NOT_DEVICE_TESTS = {
     "test_load_save_without_tied_weights",
     "test_tied_weights_keys",
     "test_model_weights_reload_no_missing_tied_weights",
+    "test_pt_tf_model_equivalence",
     "test_mismatched_shapes_have_properly_initialized_weights",
     "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
     "test_model_is_small",
@@ -84,6 +85,12 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_configure(config):
+    config.addinivalue_line(
+        "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"
+    )
+    config.addinivalue_line(
+        "markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"
+    )
     config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested")
     config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment")
     config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate")

docker/consistency.dockerfile

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

docker/custom-tokenizers.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 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

docker/examples-tf.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git

docker/examples-torch.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 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

docker/exotic-models.dockerfile

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

docker/jax-light.dockerfile

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

docker/pipeline-tf.dockerfile

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

docker/pipeline-torch.dockerfile

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

docker/quality.dockerfile

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

docker/tf-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
@@ -7,6 +7,6 @@ RUN apt-get install -y  cmake
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN 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 install --no-cache-dir  "protobuf==3.20.3" 
 RUN pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-jax-light.dockerfile

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

docker/torch-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
@@ -7,5 +7,5 @@ ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words]"
-RUN pip uninstall -y transformers
+RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken]"
+RUN pip uninstall -y transformers

docker/torch-tf-light.dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 RUN echo ${REF}

docker/transformers-all-latest-gpu/Dockerfile

@@ -9,7 +9,7 @@ SHELL ["sh", "-lc"]
 # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
 # to be used as arguments for docker build (so far).
 
-ARG PYTORCH='2.6.0'
+ARG PYTORCH='2.5.1'
 # (not always a valid torch version)
 ARG INTEL_TORCH_EXT='2.3.0'
 # Example: `cu102`, `cu113`, etc.

docker/transformers-past-gpu/Dockerfile

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

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,18 +1,18 @@
-FROM rocm/dev-ubuntu-22.04:6.2.4
+FROM rocm/dev-ubuntu-22.04:6.3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 RUN apt update && \
-    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs && \
+    apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg && \
     apt clean && \
     rm -rf /var/lib/apt/lists/*
 
-RUN git lfs install
+RUN export PATH="${PATH:+${PATH}:}~/opt/rocm/bin"
 
 RUN python3 -m pip install --no-cache-dir --upgrade pip numpy
 
-RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4
+RUN python3 -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3/
 
 RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0"
 

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

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

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

@@ -1,5 +1,5 @@
 # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11
-FROM nvcr.io/nvidia/pytorch:23.11-py3
+FROM nvcr.io/nvidia/pytorch:23.04-py3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive

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

@@ -34,8 +34,8 @@ RUN python3 -m pip uninstall -y torch-tensorrt apex
 # Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed
 # This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
-# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
-# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
+# Issue: https://github.com/microsoft/DeepSpeed/issues/2010
+# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 ## For `torchdynamo` tests

docker/transformers-pytorch-gpu/Dockerfile

@@ -11,7 +11,7 @@ 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='2.6.0'
+ARG PYTORCH='2.5.1'
 ARG TORCH_VISION=''
 ARG TORCH_AUDIO=''
 # Example: `cu102`, `cu113`, etc.

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -53,9 +53,6 @@ RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
 # Add vptq for quantization testing
 RUN python3 -m pip install --no-cache-dir vptq
 
-# Add spqr for quantization testing
-RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
-
 # Add hqq for quantization testing
 RUN python3 -m pip install --no-cache-dir hqq
 
@@ -76,9 +73,6 @@ RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git
 RUN python3 -m pip install --no-cache-dir flute-kernel==0.3.0 -i https://flute-ai.github.io/whl/cu118
 RUN python3 -m pip install --no-cache-dir fast_hadamard_transform==1.0.4.post1
 
-# Add compressed-tensors for quantization testing
-RUN python3 -m pip install --no-cache-dir compressed-tensors
-
 # 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/source/ar/notebooks.md

@@ -130,6 +130,7 @@
 | دفتر الملاحظات     |      الوصف      |   |   |
 |:----------|:-------------|:-------------|------:|
 | [كيفية تكميم نموذج باستخدام ONNX Runtime لتصنيف النص](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| يوضح كيفية تطبيق التكميم الثابت والديناميكي على نموذج باستخدام [ONNX Runtime](https://github.com/microsoft/onnxruntime) لأي مهمة GLUE. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)|
+| [كيفية تكميم نموذج باستخدام Intel Neural Compressor لتصنيف النص](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| يوضح كيفية تطبيق التكميم الثابت والديناميكي والتدريبي على نموذج باستخدام [Intel Neural Compressor (INC)](https://github.com/intel/neural-compressor) لأي مهمة GLUE. | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_inc.ipynb)|
 | [كيفية ضبط نموذج بدقة على تصنيف النص باستخدام ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| يوضح كيفية معالجة البيانات مسبقًا وضبط نموذج بدقة على أي مهمة GLUE باستخدام [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)|
 | [كيفية ضبط نموذج بدقة على التلخيص باستخدام ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| يوضح كيفية معالجة البيانات مسبقًا وضبط نموذج بدقة على XSUM باستخدام [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)|
 

docs/source/ar/trainer.md

@@ -673,29 +673,6 @@ tpu_use_sudo: false
 use_cpu: false
 ```
 
-</hfoption>
-<hfoption id="Tensor Parallelism with PyTorch 2">
-
-```yml
-compute_environment: LOCAL_MACHINE
-tp_config:
-  tp_size: 4
-distributed_type: TP
-downcast_bf16: 'no'
-machine_rank: 0
-main_training_function: main
-mixed_precision: 'no'
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
-
-```
-
 </hfoption>
 </hfoptions>
 يُعد أمر  [`accelerate_launch`](https://huggingface.co/docs/accelerate/package_reference/cli#accelerate-launch) هو الطريقة المُوصى بها لتشغيل نص البرمجى للتدريب على نظام موزع باستخدام Accelerate و [`Trainer`] مع المعلمات المحددة في `config_file.yaml`. يتم حفظ هذا الملف في مجلد ذاكرة التخزين المؤقت لـ Accelerate ويتم تحميله تلقائيًا عند تشغيل `accelerate_launch`.

docs/source/de/contributing.md

@@ -283,6 +283,8 @@ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/t
 Wie bei den langsamen Tests gibt es auch andere Umgebungsvariablen, die standardmäßig beim Testen nicht gesetzt sind:
 
 * `RUN_CUSTOM_TOKENIZERS`: Aktiviert Tests für benutzerdefinierte Tokenizer.
+* `RUN_PT_FLAX_CROSS_TESTS`: Aktiviert Tests für die Integration von PyTorch + Flax.
+* `RUN_PT_TF_CROSS_TESTS`: Aktiviert Tests für die Integration von TensorFlow + PyTorch.
 
 Weitere Umgebungsvariablen und zusätzliche Informationen finden Sie in der [testing_utils.py](src/transformers/testing_utils.py).
 

docs/source/en/_toctree.yml

@@ -110,17 +110,6 @@
     - local: kv_cache
       title: Best Practices for Generation with Cache
     title: Generation
-  - isExpanded: false
-    sections:
-    - local: chat_template_basics
-      title: Getting Started with Chat Templates for Text LLMs
-    - local: chat_template_multimodal
-      title: Multimodal Chat Templates for Vision and Audio LLMs
-    - local: chat_template_tools_and_documents
-      title: Expanding Chat Templates with Tools and Documents
-    - local: chat_template_advanced
-      title: Advanced Usage and Customizing Your Chat Templates
-    title: Chat Templates
   - isExpanded: false
     sections:
     - local: tasks/idefics
@@ -138,6 +127,8 @@
     title: Use model-specific APIs
   - local: custom_models
     title: Share a custom model
+  - local: chat_templating
+    title: Chat templates
   - local: trainer
     title: Trainer
   - local: sagemaker
@@ -175,8 +166,6 @@
   - local: quantization/aqlm
     title: AQLM
   - local: quantization/vptq
-    title: SpQR
-  - local: quantization/spqr
     title: VPTQ
   - local: quantization/quanto
     title: Quanto
@@ -196,8 +185,6 @@
     title: BitNet
   - local: quantization/compressed_tensors
     title: compressed-tensors
-  - local: quantization/finegrained_fp8
-    title: Fine-grained FP8
   - local: quantization/contribute
     title: Contribute new quantization method
   title: Quantization Methods
@@ -461,8 +448,6 @@
         title: Granite
       - local: model_doc/granitemoe
         title: GraniteMoe
-      - local: model_doc/granitemoeshared
-        title: GraniteMoeShared
       - local: model_doc/granitevision
         title: GraniteVision
       - local: model_doc/helium
@@ -641,8 +626,6 @@
         title: YOSO
       - local: model_doc/zamba
         title: Zamba
-      - local: model_doc/zamba2
-        title: Zamba2
       title: Text models
     - isExpanded: false
       sections:
@@ -658,8 +641,6 @@
         title: ConvNeXTV2
       - local: model_doc/cvt
         title: CvT
-      - local: model_doc/dab-detr
-        title: DAB-DETR
       - local: model_doc/deformable_detr
         title: Deformable DETR
       - local: model_doc/deit
@@ -668,8 +649,6 @@
         title: Depth Anything
       - local: model_doc/depth_anything_v2
         title: Depth Anything V2
-      - local: model_doc/depth_pro
-        title: DepthPro
       - local: model_doc/deta
         title: DETA
       - local: model_doc/detr
@@ -726,8 +705,6 @@
         title: ResNet
       - local: model_doc/rt_detr
         title: RT-DETR
-      - local: model_doc/rt_detr_v2
-        title: RT-DETRv2
       - local: model_doc/segformer
         title: SegFormer
       - local: model_doc/seggpt
@@ -893,8 +870,6 @@
         title: FLAVA
       - local: model_doc/git
         title: GIT
-      - local: model_doc/got_ocr2
-        title: GOT-OCR2
       - local: model_doc/grounding-dino
         title: Grounding DINO
       - local: model_doc/groupvit
@@ -965,10 +940,6 @@
         title: Segment Anything
       - local: model_doc/siglip
         title: SigLIP
-      - local: model_doc/siglip2
-        title: SigLIP2
-      - local: model_doc/smolvlm
-        title: SmolVLM
       - local: model_doc/speech-encoder-decoder
         title: Speech Encoder Decoder Models
       - local: model_doc/tapas

docs/source/en/agents_advanced.md

@@ -162,7 +162,7 @@ agent.run(
 improved_prompt could be "A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background"
 
 Now that I have improved the prompt, I can use the image generator tool to generate an image based on this prompt.
-=== Agent is executing the code below:
+>>> Agent is executing the code below:
 image = image_generator(prompt="A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background")
 final_answer(image)
 ```

docs/source/en/chat_template_advanced.md

@@ -1,463 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-# Advanced Usage and Customizing Your Chat Templates
-
-In this page, we’ll explore more advanced techniques for working with chat templates in Transformers. Whether you’re looking to write your own templates, create custom components, or optimize your templates for efficiency, we’ll cover everything you need to take your templates to the next level. Let’s dive into the tools and strategies that will help you get the most out of your chat models.
-
-
-## How do chat templates work?
-
-The chat template for a model is stored on the `tokenizer.chat_template` attribute. Let's take a look at a `Zephyr` chat template, though note this
-one is a little simplified from the actual one!
-
-```
-{%- for message in messages %}
-    {{- '<|' + message['role'] + '|>\n' }}
-    {{- message['content'] + eos_token }}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|assistant|>\n' }}
-{%- endif %}
-```
-
-If you've never seen one of these before, this is a [Jinja template](https://jinja.palletsprojects.com/en/3.1.x/templates/).
-Jinja is a templating language that allows you to write simple code that generates text. In many ways, the code and
-syntax resembles Python. In pure Python, this template would look something like this:
-
-```python
-for message in messages:
-    print(f'<|{message["role"]}|>')
-    print(message['content'] + eos_token)
-if add_generation_prompt:
-    print('<|assistant|>')
-```
-
-Effectively, the template does three things:
-1. For each message, print the role enclosed in `<|` and `|>`, like `<|user|>` or `<|assistant|>`.
-2. Next, print the content of the message, followed by the end-of-sequence token.
-3. Finally, if `add_generation_prompt` is set, print the assistant token, so that the model knows to start generating
-   an assistant response.
-
-This is a pretty simple template but Jinja gives you a lot of flexibility to do more complex things! Let's see a Jinja
-template that can format inputs similarly to the way LLaMA formats them (note that the real LLaMA template includes 
-handling for default system messages and slightly different system message handling in general - don't use this one 
-in your actual code!)
-
-```
-{%- for message in messages %}
-    {%- if message['role'] == 'user' %}
-        {{- bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
-    {%- elif message['role'] == 'system' %}
-        {{- '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
-    {%- elif message['role'] == 'assistant' %}
-        {{- ' '  + message['content'] + ' ' + eos_token }}
-    {%- endif %}
-{%- endfor %}
-```
-
-Hopefully if you stare at this for a little bit you can see what this template is doing - it adds specific tokens like
-`[INST]` and `[/INST]` based on the role of each message. User, assistant and system messages are clearly
-distinguishable to the model because of the tokens they're wrapped in.
-
-
-## How do I create a chat template?
-
-Simple, just write a jinja template and set `tokenizer.chat_template`. You may find it easier to start with an 
-existing template from another model and simply edit it for your needs! For example, we could take the LLaMA template
-above and add "[ASST]" and "[/ASST]" to assistant messages:
-
-```
-{%- for message in messages %}
-    {%- if message['role'] == 'user' %}
-        {{- bos_token + '[INST] ' + message['content'].strip() + ' [/INST]' }}
-    {%- elif message['role'] == 'system' %}
-        {{- '<<SYS>>\\n' + message['content'].strip() + '\\n<</SYS>>\\n\\n' }}
-    {%- elif message['role'] == 'assistant' %}
-        {{- '[ASST] '  + message['content'] + ' [/ASST]' + eos_token }}
-    {%- endif %}
-{%- endfor %}
-```
-
-Now, simply set the `tokenizer.chat_template` attribute. Next time you use [`~PreTrainedTokenizer.apply_chat_template`], it will
-use your new template! This attribute will be saved in the `tokenizer_config.json` file, so you can use
-[`~utils.PushToHubMixin.push_to_hub`] to upload your new template to the Hub and make sure everyone's using the right
-template for your model!
-
-```python
-template = tokenizer.chat_template
-template = template.replace("SYS", "SYSTEM")  # Change the system token
-tokenizer.chat_template = template  # Set the new template
-tokenizer.push_to_hub("model_name")  # Upload your new template to the Hub!
-```
-
-The method [`~PreTrainedTokenizer.apply_chat_template`] which uses your chat template is called by the [`TextGenerationPipeline`] class, so 
-once you set the correct chat template, your model will automatically become compatible with [`TextGenerationPipeline`].
-
-<Tip>
-If you're fine-tuning a model for chat, in addition to setting a chat template, you should probably add any new chat
-control tokens as special tokens in the tokenizer. Special tokens are never split, 
-ensuring that your control tokens are always handled as single tokens rather than being tokenized in pieces. You 
-should also set the tokenizer's `eos_token` attribute to the token that marks the end of assistant generations in your
-template. This will ensure that text generation tools can correctly figure out when to stop generating text.
-</Tip>
-
-
-## Why do some models have multiple templates?
-
-Some models use different templates for different use cases. For example, they might use one template for normal chat
-and another for tool-use, or retrieval-augmented generation. In these cases, `tokenizer.chat_template` is a dictionary.
-This can cause some confusion, and where possible, we recommend using a single template for all use-cases. You can use
-Jinja statements like `if tools is defined` and `{% macro %}` definitions to easily wrap multiple code paths in a
-single template.
-
-When a tokenizer has multiple templates, `tokenizer.chat_template` will be a `dict`, where each key is the name
-of a template. The `apply_chat_template` method has special handling for certain template names: Specifically, it will
-look for a template named `default` in most cases, and will raise an error if it can't find one. However, if a template
-named `tool_use` exists when the user has passed a `tools` argument, it will use that instead. To access templates
-with other names, pass the name of the template you want to the `chat_template` argument of
-`apply_chat_template()`.
-
-We find that this can be a bit confusing for users, though - so if you're writing a template yourself, we recommend
-trying to put it all in a single template where possible!
-
-
-## What template should I use?
-
-When setting the template for a model that's already been trained for chat, you should ensure that the template
-exactly matches the message formatting that the model saw during training, or else you will probably experience
-performance degradation. This is true even if you're training the model further - you will probably get the best 
-performance if you keep the chat tokens constant. This is very analogous to tokenization - you generally get the
-best performance for inference or fine-tuning when you precisely match the tokenization used during training.
-
-If you're training a model from scratch, or fine-tuning a base language model for chat, on the other hand,
-you have a lot of freedom to choose an appropriate template! LLMs are smart enough to learn to handle lots of different
-input formats. One popular choice is the `ChatML` format, and this is a good, flexible choice for many use-cases. 
-It looks like this:
-
-```
-{%- for message in messages %}
-    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
-{%- endfor %}
-```
-
-If you like this one, here it is in one-liner form, ready to copy into your code. The one-liner also includes
-handy support for [generation prompts](#what-are-generation-prompts), but note that it doesn't add BOS or EOS tokens!
-If your model expects those, they won't be added automatically by `apply_chat_template` - in other words, the
-text will be tokenized with `add_special_tokens=False`. This is to avoid potential conflicts between the template and
-the `add_special_tokens` logic. If your model expects special tokens, make sure to add them to the template!
-
-```python
-tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
-```
-
-This template wraps each message in `<|im_start|>` and `<|im_end|>` tokens, and simply writes the role as a string, which
-allows for flexibility in the roles you train with. The output looks like this:
-
-```text
-<|im_start|>system
-You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
-<|im_start|>user
-How are you?<|im_end|>
-<|im_start|>assistant
-I'm doing great!<|im_end|>
-```
-
-The "user", "system" and "assistant" roles are the standard for chat, and we recommend using them when it makes sense,
-particularly if you want your model to operate well with [`TextGenerationPipeline`]. However, you are not limited
-to these roles - templating is extremely flexible, and any string can be a role.
-
-## I want to add some chat templates! How should I get started?
-
-If you have any chat models, you should set their `tokenizer.chat_template` attribute and test it using
-[`~PreTrainedTokenizer.apply_chat_template`], then push the updated tokenizer to the Hub. This applies even if you're
-not the model owner - if you're using a model with an empty chat template, or one that's still using the default class
-template, please open a [pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) to the model repository so that this attribute can be set properly!
-
-Once the attribute is set, that's it, you're done! `tokenizer.apply_chat_template` will now work correctly for that
-model, which means it is also automatically supported in places like `TextGenerationPipeline`!
-
-By ensuring that models have this attribute, we can make sure that the whole community gets to use the full power of
-open-source models. Formatting mismatches have been haunting the field and silently harming performance for too long - 
-it's time to put an end to them!
-
-
-<Tip>
-
-The easiest way to get started with writing Jinja templates is to take a look at some existing ones. You can use
-`print(tokenizer.chat_template)` for any chat model to see what template it's using. In general, models that support tool use have 
-much more complex templates than other models - so when you're just getting started, they're probably a bad example
-to learn from! You can also take a look at the 
-[Jinja documentation](https://jinja.palletsprojects.com/en/3.1.x/templates/#synopsis) for details
-of general Jinja formatting and syntax.
-
-</Tip>
-
-Jinja templates in `transformers` are identical to Jinja templates elsewhere. The main thing to know is that 
-the conversation history will be accessible inside your template as a variable called `messages`.  
-You will be able to access `messages` in your template just like you can in Python, which means you can loop over 
-it with `{% for message in messages %}` or access individual messages with `{{ messages[0] }}`, for example.
-
-You can also use the following tips to write clean, efficient Jinja templates:
-
-### Trimming whitespace
-
-By default, Jinja will print any whitespace that comes before or after a block. This can be a problem for chat
-templates, which generally want to be very precise with whitespace! To avoid this, we strongly recommend writing
-your templates like this:
-
-```
-{%- for message in messages %}
-    {{- message['role'] + message['content'] }}
-{%- endfor %}
-```
-
-rather than like this:
-
-```
-{% for message in messages %}
-    {{ message['role'] + message['content'] }}
-{% endfor %}
-```
-
-Adding `-` will strip any whitespace that comes before the block. The second example looks innocent, but the newline
-and indentation may end up being included in the output, which is probably not what you want!
-
-### Special variables
-
-Inside your template, you will have access several special variables. The most important of these is `messages`, 
-which contains the chat history as a list of message dicts. However, there are several others. Not every
-variable will be used in every template. The most common other variables are:
-
-- `tools` contains a list of tools in JSON schema format. Will be `None` or undefined if no tools are passed.
-- `documents` contains a list of documents in the format `{"title": "Title", "contents": "Contents"}`, used for retrieval-augmented generation. Will be `None` or undefined if no documents are passed.
-- `add_generation_prompt` is a bool that is `True` if the user has requested a generation prompt, and `False` otherwise. If this is set, your template should add the header for an assistant message to the end of the conversation. If your model doesn't have a specific header for assistant messages, you can ignore this flag.
-- **Special tokens** like `bos_token` and `eos_token`. These are extracted from `tokenizer.special_tokens_map`. The exact tokens available inside each template will differ depending on the parent tokenizer.
-
-<Tip>
-
-You can actually pass any `kwarg` to `apply_chat_template`, and it will be accessible inside the template as a variable. In general,
-we recommend trying to stick to the core variables above, as it will make your model harder to use if users have
-to write custom code to pass model-specific `kwargs`. However, we're aware that this field moves quickly, so if you
-have a new use-case that doesn't fit in the core API, feel free to use a new `kwarg` for it! If a new `kwarg`
-becomes common we may promote it into the core API and create a standard, documented format for it.
-
-</Tip>
-
-### Callable functions
-
-There is also a short list of callable functions available to you inside your templates. These are:
-
-- `raise_exception(msg)`: Raises a `TemplateException`. This is useful for debugging, and for telling users when they're
-doing something that your template doesn't support.
-- `strftime_now(format_str)`: Equivalent to `datetime.now().strftime(format_str)` in Python. This is used for getting
-the current date/time in a specific format, which is sometimes included in system messages.
-
-### Compatibility with non-Python Jinja
-
-There are multiple implementations of Jinja in various languages. They generally have the same syntax,
-but a key difference is that when you're writing a template in Python you can use Python methods, such as
-`.lower()` on strings or `.items()` on dicts. This will break if someone tries to use your template on a non-Python
-implementation of Jinja. Non-Python implementations are particularly common in deployment environments, where JS
-and Rust are very popular. 
-
-Don't panic, though! There are a few easy changes you can make to your templates to ensure they're compatible across
-all implementations of Jinja:
-
-- Replace Python methods with Jinja filters. These usually have the same name, for example `string.lower()` becomes
-  `string|lower`, and `dict.items()` becomes `dict|items`. One notable change is that `string.strip()` becomes `string|trim`.
-  See the [list of built-in filters](https://jinja.palletsprojects.com/en/3.1.x/templates/#builtin-filters)
-  in the Jinja documentation for more.
-- Replace `True`, `False` and `None`, which are Python-specific, with `true`, `false` and `none`.
-- Directly rendering a dict or list may give different results in other implementations (for example, string entries
-  might change from single-quoted to double-quoted). Adding the `tojson` filter can help to ensure consistency here.
-
-### Writing generation prompts
-
-We mentioned above that `add_generation_prompt` is a special variable that will be accessible inside your template,
-and is controlled by the user setting the `add_generation_prompt` flag. If your model expects a header for
-assistant messages, then your template must support adding the header when `add_generation_prompt` is set.
-
-Here is an example of a template that formats messages ChatML-style, with generation prompt support:
-
-```text
-{{- bos_token }}
-{%- for message in messages %}
-    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|im_start|>assistant\n' }}
-{%- endif %}
-```
-
-The exact content of the assistant header will depend on your specific model, but it should always be **the string
-that represents the start of an assistant message**, so that if the user applies your template with 
-`add_generation_prompt=True` and then generates text, the model will write an assistant response. Also note that some
-models do not need a generation prompt, because assistant messages always begin immediately after user messages. 
-This is particularly common for LLaMA and Mistral models, where assistant messages begin immediately after the `[/INST]`
-token that ends user messages. In these cases, the template can ignore the `add_generation_prompt` flag.
-
-Generation prompts are important! If your model requires a generation prompt but it is not set in the template, then
-model generations will likely be severely degraded, or the model may display unusual behaviour like continuing 
-the final user message! 
-
-### Writing and debugging larger templates
-
-When this feature was introduced, most templates were quite small, the Jinja equivalent of a "one-liner" script. 
-However, with new models and features like tool-use and RAG, some templates can be 100 lines long or more. When
-writing templates like these, it's a good idea to write them in a separate file, using a text editor. You can easily 
-extract a chat template to a file:
-
-```python
-open("template.jinja", "w").write(tokenizer.chat_template)
-```
-
-Or load the edited template back into the tokenizer:
-
-```python
-tokenizer.chat_template = open("template.jinja").read()
-```
-
-As an added bonus, when you write a long, multi-line template in a separate file, line numbers in that file will
-exactly correspond to line numbers in template parsing or execution errors. This will make it much easier to
-identify the source of issues.
-
-
-
-## Writing templates for tools
-
-Although chat templates do not enforce a specific API for tools (or for anything, really), we recommend 
-template authors try to stick to a standard API where possible. The whole point of chat templates is to allow code
-to be transferable across models, so deviating from the standard tools API means users will have to write
-custom code to use tools with your model. Sometimes it's unavoidable, but often with clever templating you can
-make the standard API work!
-
-Below, we'll list the elements of the standard API, and give tips on writing templates that will work well with it.
-
-### Tool definitions
-
-Your template should expect that the variable `tools` will either be null (if no tools are passed), or is a list 
-of JSON schema dicts. Our chat template methods allow users to pass tools as either JSON schema or Python functions, but when
-functions are passed, we automatically generate JSON schema and pass that to your template. As a result, the 
-`tools` variable that your template receives will always be a list of JSON schema. Here is
-a sample tool JSON schema:
-
-```json
-{
-  "type": "function", 
-  "function": {
-    "name": "multiply", 
-    "description": "A function that multiplies two numbers", 
-    "parameters": {
-      "type": "object", 
-      "properties": {
-        "a": {
-          "type": "number", 
-          "description": "The first number to multiply"
-        }, 
-        "b": {
-          "type": "number",
-          "description": "The second number to multiply"
-        }
-      }, 
-      "required": ["a", "b"]
-    }
-  }
-}
-```
-
-And here is some example code for handling tools in your chat template. Remember, this is just an example for a
-specific format - your model will probably need different formatting!
-
-```text
-{%- if tools %}
-    {%- for tool in tools %}
-        {{- '<tool>' + tool['function']['name'] + '\n' }}
-        {%- for argument in tool['function']['parameters']['properties'] %}
-            {{- argument + ': ' + tool['function']['parameters']['properties'][argument]['description'] + '\n' }}
-        {%- endfor %}
-        {{- '\n</tool>' }}
-    {%- endif %}
-{%- endif %}
-```
-
-The specific tokens and tool descriptions your template renders should of course be chosen to match the ones your model
-was trained with. There is no requirement that your **model** understands JSON schema input, only that your template can translate
-JSON schema into your model's format. For example, [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024) 
-was trained with tools defined using Python function headers, but the Command-R tool template accepts JSON schema, 
-converts types internally and renders the input tools as Python headers. You can do a lot with templates!
-
-### Tool calls
-
-Tool calls, if present, will be a list attached to a message with the "assistant" role. Note that `tool_calls` is 
-always a list, even though most tool-calling models only support single tool calls at a time, which means
-the list will usually only have a single element. Here is a sample message dict containing a tool call:
-
-```json
-{
-  "role": "assistant",
-  "tool_calls": [
-    {
-      "type": "function",
-      "function": {
-        "name": "multiply",
-        "arguments": {
-          "a": 5,
-          "b": 6
-        }
-      }
-    }
-  ]
-}
-```
-
-And a common pattern for handling them would be something like this:
-
-```text
-{%- if message['role'] == 'assistant' and 'tool_calls' in message %}
-    {%- for tool_call in message['tool_calls'] %}
-            {{- '<tool_call>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments']|tojson + '\n</tool_call>' }}
-        {%- endif %}
-    {%- endfor %}
-{%- endif %}
-```
-
-Again, you should render the tool call with the formatting and special tokens that your model expects.
-
-### Tool responses
-
-Tool responses have a simple format: They are a message dict with the "tool" role, a "name" key giving the name
-of the called function, and a "content" key containing the result of the tool call. Here is a sample tool response:
-
-```json
-{
-  "role": "tool",
-  "name": "multiply",
-  "content": "30"
-}
-```
-
-You don't need to use all of the keys in the tool response. For example, if your model doesn't expect the function
-name to be included in the tool response, then rendering it can be as simple as:
-
-```text
-{%- if message['role'] == 'tool' %}
-    {{- "<tool_result>" + message['content'] + "</tool_result>" }}
-{%- endif %}
-```
-
-Again, remember that the actual formatting and special tokens are model-specific - you should take a lot of care
-to ensure that tokens, whitespace and everything else exactly match the format your model was trained with!

docs/source/en/chat_template_basics.md

@@ -1,287 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-# Getting Started with Chat Templates for Text LLMs
-
-An increasingly common use case for LLMs is **chat**. In a chat context, rather than continuing a single string
-of text (as is the case with a standard language model), the model instead continues a conversation that consists
-of one or more **messages**, each of which includes a **role**, like "user" or "assistant", as well as message text.
-
-Much like tokenization, different models expect very different input formats for chat. This is the reason we added
-**chat templates** as a feature. Chat templates are part of the tokenizer for text-only LLMs or processor for multimodal LLMs. They specify how to convert conversations, represented as lists of messages, into a single tokenizable string in the format that the model expects. 
-
-We'll explore the basic usage of chat templates with text-only LLMs in this page. For detailed guidance on multimodal models, we have a dedicated [documentation oage for multimodal models](./chat_template_multimodal), which covers how to work with image, video and audio inputs in your templates.
-
-Let's make this concrete with a quick example using the `mistralai/Mistral-7B-Instruct-v0.1` model:
-
-```python
->>> from transformers import AutoTokenizer
->>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
-
->>> chat = [
-...   {"role": "user", "content": "Hello, how are you?"},
-...   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-...   {"role": "user", "content": "I'd like to show off how chat templating works!"},
-... ]
-
->>> tokenizer.apply_chat_template(chat, tokenize=False)
-"<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]"
-```
-
-Notice how the tokenizer has added the control tokens [INST] and [/INST] to indicate the start and end of 
-user messages (but not assistant messages!), and the entire chat is condensed into a single string. 
-If we use `tokenize=True`, which is the default setting, that string will also be tokenized for us.
-
-Now, try the same code, but swap in the `HuggingFaceH4/zephyr-7b-beta` model instead, and you should get:
-
-```text
-<|user|>
-Hello, how are you?</s>
-<|assistant|>
-I'm doing great. How can I help you today?</s>
-<|user|>
-I'd like to show off how chat templating works!</s>
-```
-
-Both Zephyr and Mistral-Instruct were fine-tuned from the same base model, `Mistral-7B-v0.1`. However, they were trained
-with totally different chat formats. Without chat templates, you would have to write manual formatting code for each
-model, and it's very easy to make minor errors that hurt performance! Chat templates handle the details of formatting 
-for you, allowing you to write universal code that works for any model.
-
-
-## How do I use chat templates?
-
-As you can see in the example above, chat templates are easy to use. Simply build a list of messages, with `role`
-and `content` keys, and then pass it to the [`~PreTrainedTokenizer.apply_chat_template`] or [`~ProcessorMixin.apply_chat_template`] method
-depending on what type of model you are using. Once you do that,
-you'll get output that's ready to go! When using chat templates as input for model generation, it's also a good idea
-to use `add_generation_prompt=True` to add a [generation prompt](#what-are-generation-prompts). 
-
-Here's an example of preparing input for `model.generate()`, using `Zephyr` again:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-checkpoint = "HuggingFaceH4/zephyr-7b-beta"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint)  # You may want to use bfloat16 and/or move to GPU here
-
-messages = [
-    {
-        "role": "system",
-        "content": "You are a friendly chatbot who always responds in the style of a pirate",
-    },
-    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
- ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-print(tokenizer.decode(tokenized_chat[0]))
-```
-This will yield a string in the input format that Zephyr expects. 
-```text
-<|system|>
-You are a friendly chatbot who always responds in the style of a pirate</s> 
-<|user|>
-How many helicopters can a human eat in one sitting?</s> 
-<|assistant|>
-```
-
-Now that our input is formatted correctly for Zephyr, we can use the model to generate a response to the user's question:
-
-```python
-outputs = model.generate(tokenized_chat, max_new_tokens=128) 
-print(tokenizer.decode(outputs[0]))
-```
-
-This will yield:
-
-```text
-<|system|>
-You are a friendly chatbot who always responds in the style of a pirate</s> 
-<|user|>
-How many helicopters can a human eat in one sitting?</s> 
-<|assistant|>
-Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.
-```
-
-Arr, 'twas easy after all!
-
-
-## Is there an automated pipeline for chat?
-
-Yes, there is! Our text generation pipelines support chat inputs, which makes it easy to use chat models. In the past,
-we used to use a dedicated "ConversationalPipeline" class, but this has now been deprecated and its functionality
-has been merged into the [`TextGenerationPipeline`]. Let's try the `Zephyr` example again, but this time using 
-a pipeline:
-
-```python
-from transformers import pipeline
-
-pipe = pipeline("text-generation", "HuggingFaceH4/zephyr-7b-beta")
-messages = [
-    {
-        "role": "system",
-        "content": "You are a friendly chatbot who always responds in the style of a pirate",
-    },
-    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
-]
-print(pipe(messages, max_new_tokens=128)[0]['generated_text'][-1])  # Print the assistant's response
-```
-
-```text
-{'role': 'assistant', 'content': "Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all."}
-```
-
-The pipeline will take care of all the details of tokenization and calling `apply_chat_template` for you -
-once the model has a chat template, all you need to do is initialize the pipeline and pass it the list of messages!
-
-
-## What are "generation prompts"?
-
-You may have noticed that the `apply_chat_template` method has an `add_generation_prompt` argument. This argument tells
-the template to add tokens that indicate the start of a bot response. For example, consider the following chat:
-
-```python
-messages = [
-    {"role": "user", "content": "Hi there!"},
-    {"role": "assistant", "content": "Nice to meet you!"},
-    {"role": "user", "content": "Can I ask a question?"}
-]
-```
-
-Here's what this will look like without a generation prompt, for a model that uses standard "ChatML" formatting:
-
-```python
-tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-"""
-```
-
-And here's what it looks like **with** a generation prompt:
-
-```python
-tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-"""<|im_start|>user
-Hi there!<|im_end|>
-<|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-"""
-```
-
-Note that this time, we've added the tokens that indicate the start of a bot response. This ensures that when the model
-generates text it will write a bot response instead of doing something unexpected, like continuing the user's 
-message. Remember, chat models are still just language models - they're trained to continue text, and chat is just a 
-special kind of text to them! You need to guide them with appropriate control tokens, so they know what they're 
-supposed to be doing.
-
-Not all models require generation prompts. Some models, like LLaMA, don't have any
-special tokens before bot responses. In these cases, the `add_generation_prompt` argument will have no effect. The exact
-effect that `add_generation_prompt` has will depend on the template being used.
-
-
-## What does "continue_final_message" do?
-
-When passing a list of messages to `apply_chat_template` or `TextGenerationPipeline`, you can choose
-to format the chat so the model will continue the final message in the chat instead of starting a new one. This is done
-by removing any end-of-sequence tokens that indicate the end of the final message, so that the model will simply
-extend the final message when it begins to generate text. This is useful for "prefilling" the model's response. 
-
-Here's an example:
-
-```python
-chat = [
-    {"role": "user", "content": "Can you format the answer in JSON?"},
-    {"role": "assistant", "content": '{"name": "'},
-]
-
-formatted_chat = tokenizer.apply_chat_template(chat, tokenize=True, return_dict=True, continue_final_message=True)
-model.generate(**formatted_chat)
-```
-
-The model will generate text that continues the JSON string, rather than starting a new message. This approach
-can be very useful for improving the accuracy of the model's instruction-following when you know how you want
-it to start its replies.
-
-Because `add_generation_prompt` adds the tokens that start a new message, and `continue_final_message` removes any
-end-of-message tokens from the final message, it does not make sense to use them together. As a result, you'll
-get an error if you try!
-
-<Tip>
-
-The default behaviour of `TextGenerationPipeline` is to set `add_generation_prompt=True` so that it starts a new
-message. However, if the final message in the input chat has the "assistant" role, it will assume that this message is 
-a prefill and switch to `continue_final_message=True` instead, because most models do not support multiple 
-consecutive assistant messages. You can override this behaviour by explicitly passing the `continue_final_message` 
-argument when calling the pipeline.
-
-</Tip>
-
-
-## Can I use chat templates in training?
-
-Yes! This is a good way to ensure that the chat template matches the tokens the model sees during training.
-We recommend that you apply the chat template as a preprocessing step for your dataset. After this, you
-can simply continue like any other language model training task. When training, you should usually set 
-`add_generation_prompt=False`, because the added tokens to prompt an assistant response will not be helpful during 
-training. Let's see an example:
-
-```python
-from transformers import AutoTokenizer
-from datasets import Dataset
-
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-
-chat1 = [
-    {"role": "user", "content": "Which is bigger, the moon or the sun?"},
-    {"role": "assistant", "content": "The sun."}
-]
-chat2 = [
-    {"role": "user", "content": "Which is bigger, a virus or a bacterium?"},
-    {"role": "assistant", "content": "A bacterium."}
-]
-
-dataset = Dataset.from_dict({"chat": [chat1, chat2]})
-dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)})
-print(dataset['formatted_chat'][0])
-```
-And we get:
-```text
-<|user|>
-Which is bigger, the moon or the sun?</s>
-<|assistant|>
-The sun.</s>
-```
-
-From here, just continue training like you would with a standard language modelling task, using the `formatted_chat` column.
-
-<Tip>
-
-By default, some tokenizers add special tokens like `<bos>` and `<eos>` to text they tokenize. Chat templates should 
-already include all the special tokens they need, and so additional special tokens will often be incorrect or 
-duplicated, which will hurt model performance.
-
-Therefore, if you format text with `apply_chat_template(tokenize=False)`, you should set the argument
-`add_special_tokens=False` when you tokenize that text later. If you use `apply_chat_template(tokenize=True)`, you don't need to worry about this!
-
-</Tip>
-

docs/source/en/chat_template_multimodal.md

@@ -1,289 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-# Multimodal Chat Templates for Vision and Audio LLMs
-
-In this section, we'll explore how to use chat templates with multimodal models, enabling your templates to handle a variety of inputs such as text, images, and audio. Multimodal models provide richer, more interactive experiences, and understanding how to effectively combine these inputs within your templates is key. We’ll walk through how to work with different modalities, configure your templates for optimal performance, and tackle common challenges along the way.
-
-Just like with text-only LLMs, multimodal models expect a chat with **messages**, each of which includes a **role** and **content**. However, for multimodal models, chat templates are a part of the [Processor](./main_cllasses/processors) class. Let's see how we can format our prompts when there are images or videos in the input along with text.
-
-
-## Image inputs
-
-For models such as [LLaVA](https://huggingface.co/llava-hf) the prompts can be formatted as below. Notice that the only difference from text-only models is that we need to also pass a placeholder for input images. To accommodate for extra modalities, each **content** is a list containing either a text or an image **type**.
-
-Let's make this concrete with a quick example using the `llava-hf/llava-onevision-qwen2-0.5b-ov-hf` model:
-
-```python
-from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
-
-model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-processor = AutoProcessor.from_pretrained(model_id)
-
-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": "image"},
-            {"type": "text", "text": "What are these?"},
-        ],
-    },
-]
-
-formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
-print(formatted_prompt)
-```
-
-This yields a string in LLaVA's expected input format with many `<image>` tokens prepended before the text.
-```text
-'<|im_start|>system 
-<|im_start|>system 
-You are a friendly chatbot who always responds in the style of a pirate<|im_end|><|im_start|>user <image>
-What are these?<|im_end|>
-```
-
-
-### Image paths or URLs
-
-To incorporate images into your chat templates, you can pass them as file paths or URLs. This method automatically loads the image, processes it, and prepares the necessary pixel values to create ready-to-use inputs for the model. This approach simplifies the integration of images, enabling seamless multimodal functionality.
-
-Let's see how it works with an example using the same model as above. This time we'll indicate an image URL with `"url"` key in the message's **content** and ask the chat template to `tokenize` and `return_dict`. Currently, "base64", "url", and "path" are supported image sources.
-
-```python
-from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
-
-model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id)
-processor = AutoProcessor.from_pretrained(model_id)
-
-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": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
-            {"type": "text", "text": "What are these?"},
-        ],
-    },
-]
-
-processed_chat = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
-print(processed_chat.keys())
-```
-
-This yields a dictionary with inputs processed and ready to be further passed into [`~GenerationMixin.generate`] to generate text.
-```text
-dict_keys(["input_ids", "attention_mask", "pixel_values", "image_sizes"])
-```
-
-
-## Video inputs
-
-Some vision models support videos as inputs as well as images. The message format is very similar to the image-only models with tiny differences to handle loading videos from a URL. We can continue using the same model as before since it supports videos.
-
-### Sampling with fixed number of frames
-
-Here's an example of how to set up a conversation with video inputs. Notice the extra `kwargs` passed to `processor.apply_chat_template()`. The key parameter here is `num_frames`, which controls how many frames to sample uniformly from the video. Each model checkpoint has a maximum frame count it was trained with, and exceeding this limit can significantly impact generation quality. So, it’s important to choose a frame count that fits both the model's capacity and your computational resources. If you don't specify `num_frames`, the entire video will be loaded without any frame sampling.
-
-You also have the option to choose a specific framework to load the video, depending on your preferences or needs. Currently, we support `decord`, `pyav` (the default), `opencv`, and `torchvision`. For this example, we’ll use `decord`, as it's a bit faster than `pyav`.
-
-
-<Tip>
-
-Note that if you are trying to load a video from URL, you can decode the video only with `pyav` or `decord` as backend.
-
-</Tip>
-
-
-```python
-from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
-
-model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
-model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id)
-processor = AutoProcessor.from_pretrained(model_id)
-
-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", "url": "https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_10MB.mp4"},
-            {"type": "text", "text": "What do you see in this video?"},
-        ],
-    },
-]
-
-processed_chat = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-    num_frames=32,
-    video_load_backend="decord",
-)
-print(processed_chat.keys())
-```
-
-### Sampling with FPS
-
-When working with long videos, you might want to sample more frames for better representation. Instead of a fixed number of frames, you can specify `video_fps`, which determines how many frames per second to extract. For example, if a video is **10 seconds long** and you set `video_fps=2`, the model will sample **20 frames** (2 per second, uniformly spaced). 
-
-Using the above model, we need to apply chat template as follows to sample 2 frames per second.
-
-```python
-processed_chat = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    video_fps=32,
-    video_load_backend="decord",
-)
-print(processed_chat.keys())
-```
-
-
-### Custom Frame Sampling with a Function  
-
-Not all models sample frames **uniformly** — some require more complex logic to determine which frames to use. If your model follows a different sampling strategy, you can **customize** frame selection by providing a function:  
-
-🔹 Use the `sample_indices_fn` argument to pass a **callable function** for sampling.  
-🔹 If provided, this function **overrides** standard `num_frames` and `fps` methods.  
-🔹 It receives all the arguments passed to `load_video` and must return **valid frame indices** to sample.  
-
-You should use `sample_indices_fn` when:
-
-- If you need a custom sampling strategy (e.g., **adaptive frame selection** instead of uniform sampling).  
-- If your model prioritizes **key moments** in a video rather than evenly spaced frames.  
-
-Here’s an example of how to implement it:  
-
-
-```python
-
-def sample_indices_fn(metadata, **kwargs):
-    # samples only the first and the second frame
-    return [0, 1]
-
-processed_chat = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    sample_indices_fn=sample_indices_fn,
-    video_load_backend="decord",
-)
-print(processed_chat.keys())
-```
-
-By using `sample_indices_fn`, you gain **full control** over frame selection, making your model **more adaptable** to different video scenarios. 🚀  
-
-
-### List of image frames as video
-
-Sometimes, instead of having a full video file, you might only have a set of sampled frames stored as images.
-
-You can pass a list of image file paths, and the processor will automatically concatenate them into a video. Just make sure that all images have the same size, as they are assumed to be from the same video.
-
-
-```python
-frames_paths = ["/path/to/frame0.png", "/path/to/frame5.png", "/path/to/frame10.png"]
-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", "path": frames_paths},
-            {"type": "text", "text": "What do you see in this video?"},
-        ],
-    },
-]
-
-processed_chat = processor.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-)
-print(processed_chat.keys())
-```
-
-
-## Multimodal conversational pipeline
-
-[`ImageTextToTextPipeline`] currently accepts images as inputs but we are planning to add support for video inputs in the future. The pipeline supports chat inputs in the same format as we have seen above. Apart from that, the pipeline will accept chats in OpenAI format. This format is supported exclusively within the pipeline to make inference easier and more accessible. 
-
-Here is how the OpenAI conversation format looks:
-
-```python
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "text",
-                "text": "What is in this image?",
-            },
-            {
-                "type": "image_url",
-                "image_url": {"url": f"http://images.cocodataset.org/val2017/000000039769.jpg"},
-            },
-        ],
-    }
-]
-```
-
-## Best Practices for Multimodal Template Configuration
-
-
-To add a custom chat template for your multimodal LLM, simply create your template using [Jinja](https://jinja.palletsprojects.com/en/3.1.x/templates/) and set it with `processor.chat_template`. If you're new to writing chat templates or need some tips, check out our [tutorial here](./chat_template_advanced) for helpful guidance.
-
-In some cases, you may want your template to handle a **list of content** from multiple modalities, while still supporting a plain string for text-only inference. Here's an example of how you can achieve that, using the [Llama-Vision](https://huggingface.co/collections/meta-llama/metas-llama-32-multimodal-models-675bfd70e574a62dd0e4059b) chat template.
-
-
-```
-{% 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 %}
-```

docs/source/en/chat_template_tools_and_documents.md

@@ -1,410 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-
-# Expanding Chat Templates with Tools and Documents
-
-The only argument that `apply_chat_template` requires is `messages`. However, you can pass any keyword
-argument to `apply_chat_template` and it will be accessible inside the template. This gives you a lot of freedom to use
-chat templates for many things. There are no restrictions on the names or the format of these arguments - you can pass
-strings, lists, dicts or whatever else you want. 
-
-That said, there are some common use-cases for these extra arguments,
-such as passing tools for function calling, or documents for retrieval-augmented generation. In these common cases,
-we have some opinionated recommendations about what the names and formats of these arguments should be, which are
-described in the sections below. We encourage model authors to make their chat templates compatible with this format,
-to make it easy to transfer tool-calling code between models.
-
-## Tool use / function calling
-
-"Tool use" LLMs can choose to call functions as external tools before generating an answer. When passing tools
-to a tool-use model, you can simply pass a list of functions to the `tools` argument:
-
-```python
-import datetime
-
-def current_time():
-    """Get the current local time as a string."""
-    return str(datetime.now())
-
-def multiply(a: float, b: float):
-    """
-    A function that multiplies two numbers
-    
-    Args:
-        a: The first number to multiply
-        b: The second number to multiply
-    """
-    return a * b
-
-tools = [current_time, multiply]
-
-model_input = tokenizer.apply_chat_template(
-    messages,
-    tools=tools
-)
-```
-
-In order for this to work correctly, you should write your functions in the format above, so that they can be parsed
-correctly as tools. Specifically, you should follow these rules:
-
-- The function should have a descriptive name
-- Every argument must have a type hint
-- The function must have a docstring in the standard Google style (in other words, an initial function description  
-  followed by an `Args:` block that describes the arguments, unless the function does not have any arguments.) 
-- Do not include types in the `Args:` block. In other words, write `a: The first number to multiply`, not
-  `a (int): The first number to multiply`. Type hints should go in the function header instead.
-- The function can have a return type and a `Returns:` block in the docstring. However, these are optional
-  because most tool-use models ignore them.
-
-### Passing tool results to the model
-
-The sample code above is enough to list the available tools for your model, but what happens if it wants to actually use
-one? If that happens, you should:
-
-1. Parse the model's output to get the tool name(s) and arguments.
-2. Add the model's tool call(s) to the conversation.
-3. Call the corresponding function(s) with those arguments.
-4. Add the result(s) to the conversation
-
-### A complete tool use example
-
-Let's walk through a tool use example, step by step. For this example, we will use an 8B `Hermes-2-Pro` model,
-as it is one of the highest-performing tool-use models in its size category at the time of writing. If you have the
-memory, you can consider using a larger model instead like [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
-or [Mixtral-8x22B](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1), both of which also support tool use
-and offer even stronger performance.
-
-First, let's load our model and tokenizer:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
-
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
-```
-
-Next, let's define a list of tools:
-
-```python
-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) -> float:
-    """
-    Get the current wind speed in km/h at a given location.
-    
-    Args:
-        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]
-```
-
-Now, let's set up a conversation for our bot:
-
-```python
-messages = [
-  {"role": "system", "content": "You are a bot that responds to weather queries. You should reply with the unit used in the queried location."},
-  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
-]
-```
-
-Now, let's apply the chat template and generate a response:
-
-```python
-inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-out = model.generate(**inputs, max_new_tokens=128)
-print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
-```
-
-And we get:
-
-```text
-<tool_call>
-{"arguments": {"location": "Paris, France", "unit": "celsius"}, "name": "get_current_temperature"}
-</tool_call><|im_end|>
-```
-
-The model has called the function with valid arguments, in the format requested by the function docstring. It has
-inferred that we're most likely referring to the Paris in France, and it remembered that, as the home of SI units,
-the temperature in France should certainly be displayed in Celsius.
-
-<Tip>
-
-The output format above is specific to the `Hermes-2-Pro` model we're using in this example. Other models may emit different
-tool call formats, and you may need to do some manual parsing at this step. For example, `Llama-3.1` models will emit
-slightly different JSON, with `parameters` instead of `arguments`. Regardless of the format the model outputs, you 
-should add the tool call to the conversation in the format below, with `tool_calls`, `function` and `arguments` keys. 
-
-</Tip>
-
-Next, let's append the model's tool call to the conversation.
-
-```python
-tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
-messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
-```
-
-<Tip warning={true}>
-
-If you're familiar with the OpenAI API, you should pay attention to an important difference here - the `tool_call` is
-a dict, but in the OpenAI API it's a JSON string. Passing a string may cause errors or strange model behaviour!
-
-</Tip>
-
-Now that we've added the tool call to the conversation, we can call the function and append the result to the
-conversation. Since we're just using a dummy function for this example that always returns 22.0, we can just append 
-that result directly.
-
-```python
-messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
-```
-
-<Tip>
-
-Some model architectures, notably Mistral/Mixtral, also require a `tool_call_id` here, which should be
-9 randomly-generated alphanumeric characters, and assigned to the `id` key of the tool call
-dictionary. The same key should also be assigned to the `tool_call_id` key of the tool response dictionary below, so 
-that tool calls can be matched to tool responses. So, for Mistral/Mixtral models, the code above would be:
-
-```python
-tool_call_id = "9Ae3bDc2F"  # Random ID, 9 alphanumeric characters
-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}]})
-```
-
-and
-
-```python
-messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": "get_current_temperature", "content": "22.0"})
-```
-
-</Tip>
-
-Finally, let's let the assistant read the function outputs and continue chatting with the user:
-
-```python
-inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-out = model.generate(**inputs, max_new_tokens=128)
-print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
-```
-
-And we get:
-
-```text
-The current temperature in Paris, France is 22.0 ° Celsius.<|im_end|>
-```
-
-Although this was a simple demo with dummy tools and a single call, the same technique works with 
-multiple real tools and longer conversations. This can be a powerful way to extend the capabilities of conversational
-agents with real-time information, computational tools like calculators, or access to large databases.
-
-### Understanding tool schemas
-
-Each function you pass to the `tools` argument of `apply_chat_template` is converted into a 
-[JSON schema](https://json-schema.org/learn/getting-started-step-by-step). These schemas
-are then passed to the model chat template. In other words, tool-use models do not see your functions directly, and they
-never see the actual code inside them. What they care about is the function **definitions** and the **arguments** they
-need to pass to them - they care about what the tools do and how to use them, not how they work! It is up to you
-to read their outputs, detect if they have requested to use a tool, pass their arguments to the tool function, and
-return the response in the chat.
-
-Generating JSON schemas to pass to the template should be automatic and invisible as long as your functions
-follow the specification above, but if you encounter problems, or you simply want more control over the conversion, 
-you can handle the conversion manually. Here is an example of a manual schema conversion.
-
-```python
-from transformers.utils import get_json_schema
-
-def multiply(a: float, b: float):
-    """
-    A function that multiplies two numbers
-    
-    Args:
-        a: The first number to multiply
-        b: The second number to multiply
-    """
-    return a * b
-
-schema = get_json_schema(multiply)
-print(schema)
-```
-
-This will yield:
-
-```json
-{
-  "type": "function", 
-  "function": {
-    "name": "multiply", 
-    "description": "A function that multiplies two numbers", 
-    "parameters": {
-      "type": "object", 
-      "properties": {
-        "a": {
-          "type": "number", 
-          "description": "The first number to multiply"
-        }, 
-        "b": {
-          "type": "number",
-          "description": "The second number to multiply"
-        }
-      }, 
-      "required": ["a", "b"]
-    }
-  }
-}
-```
-
-If you wish, you can edit these schemas, or even write them from scratch yourself without using `get_json_schema` at 
-all. JSON schemas can be passed directly to the `tools` argument of 
-`apply_chat_template` - this gives you a lot of power to define precise schemas for more complex functions. Be careful,
-though - the more complex your schemas, the more likely the model is to get confused when dealing with them! We 
-recommend simple function signatures where possible, keeping arguments (and especially complex, nested arguments) 
-to a minimum.
-
-Here is an example of defining schemas by hand, and passing them directly to `apply_chat_template`:
-
-```python
-# A simple function that takes no arguments
-current_time = {
-  "type": "function", 
-  "function": {
-    "name": "current_time",
-    "description": "Get the current local time as a string.",
-    "parameters": {
-      'type': 'object',
-      'properties': {}
-    }
-  }
-}
-
-# A more complete function that takes two numerical arguments
-multiply = {
-  'type': 'function',
-  'function': {
-    'name': 'multiply',
-    'description': 'A function that multiplies two numbers', 
-    'parameters': {
-      'type': 'object', 
-      'properties': {
-        'a': {
-          'type': 'number',
-          'description': 'The first number to multiply'
-        }, 
-        'b': {
-          'type': 'number', 'description': 'The second number to multiply'
-        }
-      }, 
-      'required': ['a', 'b']
-    }
-  }
-}
-
-model_input = tokenizer.apply_chat_template(
-    messages,
-    tools = [current_time, multiply]
-)
-```
-
-## Retrieval-augmented generation
-
-"Retrieval-augmented generation" or "RAG" LLMs can search a corpus of documents for information before responding
-to a query. This allows models to vastly expand their knowledge base beyond their limited context size. Our 
-recommendation for RAG models is that their template
-should accept a `documents` argument. This should be a list of documents, where each "document"
-is a single dict with `title` and `contents` keys, both of which are strings. Because this format is much simpler
-than the JSON schemas used for tools, no helper functions are necessary.
-
-Here's an example of a RAG template in action:
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Load the model and tokenizer
-model_id = "CohereForAI/c4ai-command-r-v01-4bit"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
-device = model.device # Get the device the model is loaded on
-
-# Define conversation input
-conversation = [
-    {"role": "user", "content": "What has Man always dreamed of?"}
-]
-
-# Define documents for retrieval-based generation
-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..."
-    }
-]
-
-# Tokenize conversation and documents using a RAG template, returning PyTorch tensors.
-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 
-gen_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
-gen_text = tokenizer.decode(gen_tokens[0])
-print(gen_text)
-```
-
-<Tip>
-
-The `documents` input for retrieval-augmented generation is not widely supported, and many models have chat templates which simply ignore this input.
-
-To verify if a model supports the `documents` input, you can read its model card, or `print(tokenizer.chat_template)` to see if the `documents` key is used anywhere.
-
-One model class that does support it, though, is Cohere's [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024) and [Command-R+](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024), through their `rag` chat template. You can see additional examples of grounded generation using this feature in their model cards.
-
-</Tip>
-
-

docs/source/en/debugging.md

@@ -30,7 +30,7 @@ DeepSpeed compiles CUDA C++ code and it can be a potential source of errors when
 
 <Tip>
 
-For any other installation issues, please [open an issue](https://github.com/deepspeedai/DeepSpeed/issues) with the DeepSpeed team.
+For any other installation issues, please [open an issue](https://github.com/microsoft/DeepSpeed/issues) with the DeepSpeed team.
 
 </Tip>
 
@@ -89,7 +89,7 @@ sudo ln -s /usr/bin/g++-7  /usr/local/cuda-10.2/bin/g++
 If you're still having issues with installing DeepSpeed or if you're building DeepSpeed at run time, you can try to prebuild the DeepSpeed modules before installing them. To make a local build for DeepSpeed:
 
 ```bash
-git clone https://github.com/deepspeedai/DeepSpeed/
+git clone https://github.com/microsoft/DeepSpeed/
 cd DeepSpeed
 rm -rf build
 TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
@@ -141,7 +141,7 @@ It is also possible to not specify `TORCH_CUDA_ARCH_LIST` and the build program
 For training on multiple machines with the same setup, you'll need to make a binary wheel:
 
 ```bash
-git clone https://github.com/deepspeedai/DeepSpeed/
+git clone https://github.com/microsoft/DeepSpeed/
 cd DeepSpeed
 rm -rf build
 TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 \

docs/source/en/deepspeed.md

@@ -28,7 +28,7 @@ This guide will walk you through how to deploy DeepSpeed training, the features
 
 ## Installation
 
-DeepSpeed is available to install from PyPI or Transformers (for more detailed installation options, take a look at the DeepSpeed [installation details](https://www.deepspeed.ai/tutorials/advanced-install/) or the GitHub [README](https://github.com/deepspeedai/DeepSpeed#installation)).
+DeepSpeed is available to install from PyPI or Transformers (for more detailed installation options, take a look at the DeepSpeed [installation details](https://www.deepspeed.ai/tutorials/advanced-install/) or the GitHub [README](https://github.com/microsoft/deepspeed#installation)).
 
 <Tip>
 
@@ -114,10 +114,10 @@ DeepSpeed works with the [`Trainer`] class by way of a config file containing al
 
 <Tip>
 
-Find a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) repository. To quickly find specific examples, you can:
+Find a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/microsoft/DeepSpeed) repository. To quickly find specific examples, you can:
 
 ```bash
-git clone https://github.com/deepspeedai/DeepSpeedExamples
+git clone https://github.com/microsoft/DeepSpeedExamples
 cd DeepSpeedExamples
 find . -name '*json'
 # find examples with the Lamb optimizer
@@ -303,7 +303,7 @@ For more information about initializing large models with ZeRO-3 and accessing t
 
 [ZeRO-Infinity](https://hf.co/papers/2104.07857) allows offloading model states to the CPU and/or NVMe to save even more memory. Smart partitioning and tiling algorithms allow each GPU to send and receive very small amounts of data during offloading such that a modern NVMe can fit an even larger total memory pool than is available to your training process. ZeRO-Infinity requires ZeRO-3.
 
-Depending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/deepspeedai/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.
+Depending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/microsoft/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.
 
 The example ZeRO-3/Infinity configuration file below sets most of the parameter values to `auto`, but you could also manually add these values.
 
@@ -1157,7 +1157,7 @@ For Transformers>=4.28, if `synced_gpus` is automatically set to `True` if multi
 
 ## Troubleshoot
 
-When you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/deepspeedai/DeepSpeed).
+When you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/microsoft/DeepSpeed).
 
 For issues related to the Transformers integration, please provide the following information:
 
@@ -1227,7 +1227,7 @@ This means the DeepSpeed loss scaler is unable to find a scaling coefficient to
 
 ## Resources
 
-DeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/deepspeedai/DeepSpeed). 
+DeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/microsoft/deepspeed). 
 
 The following papers are also a great resource for learning more about ZeRO:
 

docs/source/en/generation_strategies.md

@@ -231,7 +231,7 @@ to check if the text is machine-generated (outputs `True` for machine-generated
 >>> detector = WatermarkDetector(model_config=model.config, device="cpu", watermarking_config=watermarking_config)
 >>> detection_out = detector(out, return_dict=True)
 >>> detection_out.prediction
-array([ True,  True])
+array([True, True])
 ```
 
 
@@ -269,7 +269,7 @@ dimension you can act upon, in addition to selecting a decoding strategy. Popula
 >>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
 >>> outputs = model.generate(**inputs)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n']
+['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n']
 ```
 
 ### Contrastive search
@@ -445,7 +445,7 @@ To enable assisted decoding, set the `assistant_model` argument with a model.
 >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
 >>> outputs = model.generate(**inputs, assistant_model=assistant_model)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a glass of wine.']
+['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']
 ```
 
 <Tip>
@@ -461,7 +461,7 @@ If you're using a `pipeline` object, all you need to do is to pass the assistant
 ...     model="meta-llama/Llama-3.1-8B",
 ...     assistant_model="meta-llama/Llama-3.2-1B",  # This extra line is all that's needed, also works with UAD
 ...     torch_dtype=torch.bfloat16
-... )
+>>> )
 >>> pipe_output = pipe("Once upon a time, ", max_new_tokens=50, do_sample=False)
 >>> pipe_output[0]["generated_text"]
 'Once upon a time, 3D printing was a niche technology that was only'
@@ -488,7 +488,7 @@ just like in multinomial sampling. However, in assisted decoding, reducing the t
 >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
 >>> outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Alice and Bob are two people who are very different, but they are both very good at what they do. Alice']
+['Alice and Bob, a couple of friends of mine, who are both in the same office as']
 ```
 
 We recommend to install `scikit-learn` library to enhance the candidate generation strategy and achieve additional speedup.
@@ -518,7 +518,7 @@ to ensure the new tokens include the correct prompt suffix.
 >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint)
 >>> outputs = model.generate(**inputs, assistant_model=assistant_model, tokenizer=tokenizer, assistant_tokenizer=assistant_tokenizer)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Alice and Bob are playing a game. Alice has a set of $n$ integers $a_1, a']
+['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']
 ```
 
 #### Prompt Lookup
@@ -547,7 +547,7 @@ If the model you're using was trained to do early exit, you can pass
 >>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
 >>> outputs = model.generate(**inputs, assistant_early_exit=4, do_sample=False, max_new_tokens=20)
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-['Alice and Bob are playing a game. Alice has a set of $n$ integers $a_1, a']
+['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']
 ```
 
 ### DoLa Decoding
@@ -571,9 +571,10 @@ See the following examples for DoLa decoding with the 32-layer LLaMA-7B model.
 >>> import torch
 >>> from accelerate.test_utils.testing import get_backend
 
->>> device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
 >>> tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
->>> model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", torch_dtype=torch.float16).to(device)
+>>> model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", torch_dtype=torch.float16)
+>>> device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
+>>> model.to(device)
 >>> set_seed(42)
 
 >>> text = "On what date was the Declaration of Independence officially signed?"
@@ -592,7 +593,7 @@ See the following examples for DoLa decoding with the 32-layer LLaMA-7B model.
 # DoLa decoding with contrasting specific layers (layers 28 and 30)
 >>> dola_custom_output = model.generate(**inputs, do_sample=False, max_new_tokens=50, dola_layers=[28,30], repetition_penalty=1.2)
 >>> tokenizer.batch_decode(dola_custom_output[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)
-['\nIn 1891, when he was 54 years old, John Jacob Astor founded his empire. He opened a one-man business and spent the next 27 years working 10-hour days. When']
+['\nIt was officially signed on 2 August 1776, when 56 members of the Second Continental Congress, representing the original 13 American colonies, voted unanimously for the resolution for independence. The 2']
 ```
 
 #### Understanding the `dola_layers` argument

docs/source/en/how_to_hack_models.md

@@ -24,37 +24,7 @@ You'll learn how to:
 - Modify a model's architecture by changing its attention mechanism.
 - Apply techniques like Low-Rank Adaptation (LoRA) to specific model components.
 
-We encourage you to contribute your own hacks and share them here with the community!
-
-## Efficient Development Workflow
-
-When modifying model code, you'll often need to test your changes without restarting your Python session. The `clear_import_cache()` utility helps with this workflow, especially during model development and contribution when you need to frequently test and compare model outputs:
-
-```python
-from transformers import AutoModel
-model = AutoModel.from_pretrained("bert-base-uncased")
-
-# Make modifications to the transformers code...
-
-# Clear the cache to reload the modified code
-from transformers.utils.import_utils import clear_import_cache
-clear_import_cache()
-
-# Reimport to get the changes
-from transformers import AutoModel
-model = AutoModel.from_pretrained("bert-base-uncased")  # Will use updated code
-```
-
-This is particularly useful when:
-- Iteratively modifying model architectures
-- Debugging model implementations 
-- Testing changes during model development
-- Comparing outputs between original and modified versions
-- Working on model contributions
-
-The `clear_import_cache()` function removes all cached Transformers modules and allows Python to reload the modified code. This enables rapid development cycles without constantly restarting your environment.
-
-This workflow is especially valuable when implementing new models, where you need to frequently compare outputs between the original implementation and your Transformers version (as described in the [Add New Model](https://huggingface.co/docs/transformers/add_new_model) guide).
+We encourage you to contribute your own hacks and share them here with the community1
 
 ## Example: Modifying the Attention Mechanism in the Segment Anything Model (SAM)
 

docs/source/en/index.md

@@ -110,7 +110,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                       [CPM-Ant](model_doc/cpmant)                        |       ✅        |         ❌         |      ❌      |
 |                          [CTRL](model_doc/ctrl)                          |       ✅        |         ✅         |      ❌      |
 |                           [CvT](model_doc/cvt)                           |       ✅        |         ✅         |      ❌      |
-|                      [DAB-DETR](model_doc/dab-detr)                      |       ✅        |         ❌         |      ❌      |
 |                           [DAC](model_doc/dac)                           |       ✅        |         ❌         |      ❌      |
 |                   [Data2VecAudio](model_doc/data2vec)                    |       ✅        |         ❌         |      ❌      |
 |                    [Data2VecText](model_doc/data2vec)                    |       ✅        |         ❌         |      ❌      |
@@ -123,7 +122,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                          [DeiT](model_doc/deit)                          |       ✅        |         ✅         |      ❌      |
 |                        [DePlot](model_doc/deplot)                        |       ✅        |         ❌         |      ❌      |
 |                [Depth Anything](model_doc/depth_anything)                |       ✅        |         ❌         |      ❌      |
-|                     [DepthPro](model_doc/depth_pro)                      |       ✅        |         ❌         |      ❌      |
 |                          [DETA](model_doc/deta)                          |       ✅        |         ❌         |      ❌      |
 |                          [DETR](model_doc/detr)                          |       ✅        |         ❌         |      ❌      |
 |                      [DialoGPT](model_doc/dialogpt)                      |       ✅        |         ✅         |      ✅      |
@@ -163,7 +161,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                           [GIT](model_doc/git)                           |       ✅        |         ❌         |      ❌      |
 |                           [GLM](model_doc/glm)                           |       ✅        |         ❌         |      ❌      |
 |                          [GLPN](model_doc/glpn)                          |       ✅        |         ❌         |      ❌      |
-|                      [GOT-OCR2](model_doc/got_ocr2)                      |       ✅        |         ❌         |      ❌      |
 |                       [GPT Neo](model_doc/gpt_neo)                       |       ✅        |         ❌         |      ✅      |
 |                      [GPT NeoX](model_doc/gpt_neox)                      |       ✅        |         ❌         |      ❌      |
 |             [GPT NeoX Japanese](model_doc/gpt_neox_japanese)             |       ✅        |         ❌         |      ❌      |
@@ -173,7 +170,6 @@ Flax), PyTorch, and/or TensorFlow.
 |               [GPTSAN-japanese](model_doc/gptsan-japanese)               |       ✅        |         ❌         |      ❌      |
 |                       [Granite](model_doc/granite)                       |       ✅        |         ❌         |      ❌      |
 |                  [GraniteMoeMoe](model_doc/granitemoe)                   |       ✅        |         ❌         |      ❌      |
-|            [GraniteMoeSharedMoe](model_doc/granitemoeshared)             |       ✅        |         ❌         |      ❌      |
 |                    [Graphormer](model_doc/graphormer)                    |       ✅        |         ❌         |      ❌      |
 |                [Grounding DINO](model_doc/grounding-dino)                |       ✅        |         ❌         |      ❌      |
 |                      [GroupViT](model_doc/groupvit)                      |       ✅        |         ✅         |      ❌      |
@@ -307,7 +303,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                      [RoFormer](model_doc/roformer)                      |       ✅        |         ✅         |      ✅      |
 |                       [RT-DETR](model_doc/rt_detr)                       |       ✅        |         ❌         |      ❌      |
 |                [RT-DETR-ResNet](model_doc/rt_detr_resnet)                |       ✅        |         ❌         |      ❌      |
-|                    [RT-DETRv2](model_doc/rt_detr_v2)                     |       ✅        |         ❌         |      ❌      |
 |                          [RWKV](model_doc/rwkv)                          |       ✅        |         ❌         |      ❌      |
 |                           [SAM](model_doc/sam)                           |       ✅        |         ✅         |      ❌      |
 |                  [SeamlessM4T](model_doc/seamless_m4t)                   |       ✅        |         ❌         |      ❌      |
@@ -317,8 +312,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                           [SEW](model_doc/sew)                           |       ✅        |         ❌         |      ❌      |
 |                         [SEW-D](model_doc/sew-d)                         |       ✅        |         ❌         |      ❌      |
 |                        [SigLIP](model_doc/siglip)                        |       ✅        |         ❌         |      ❌      |
-|                       [SigLIP2](model_doc/siglip2)                       |       ✅        |         ❌         |      ❌      |
-|                       [SmolVLM](model_doc/smolvlm)                       |       ✅        |         ❌         |      ❌      |
 |        [Speech Encoder decoder](model_doc/speech-encoder-decoder)        |       ✅        |         ❌         |      ✅      |
 |                 [Speech2Text](model_doc/speech_to_text)                  |       ✅        |         ✅         |      ❌      |
 |                      [SpeechT5](model_doc/speecht5)                      |       ✅        |         ❌         |      ❌      |
@@ -392,7 +385,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                         [YOLOS](model_doc/yolos)                         |       ✅        |         ❌         |      ❌      |
 |                          [YOSO](model_doc/yoso)                          |       ✅        |         ❌         |      ❌      |
 |                         [Zamba](model_doc/zamba)                         |       ✅        |         ❌         |      ❌      |
-|                        [Zamba2](model_doc/zamba2)                        |       ✅        |         ❌         |      ❌      |
 |                      [ZoeDepth](model_doc/zoedepth)                      |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->

docs/source/en/installation.md

@@ -32,32 +32,12 @@ Install 🤗 Transformers for whichever deep learning library you're working wit
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, take a look at this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies.
 
-Create a virtual environment with [uv](https://docs.astral.sh/uv/) (refer to [Installation](https://docs.astral.sh/uv/getting-started/installation/) for installation instructions), a fast Rust-based Python package and project manager.
-
-```bash
-uv venv my-env
-source my-env/bin/activate
-```
-
-Now you're ready to install 🤗 Transformers with pip or uv.
-
-<hfoptions id="install">
-<hfoption id="uv">
-
-```bash
-uv pip install transformers
-```
-
-</hfoption>
-<hfoption id="pip">
+Now you're ready to install 🤗 Transformers with the following command:
 
 ```bash
 pip install transformers
 ```
 
-</hfoption>
-</hfoptions>
-
 For GPU acceleration, install the appropriate CUDA drivers for [PyTorch](https://pytorch.org/get-started/locally) and TensorFlow(https://www.tensorflow.org/install/pip).
 
 Run the command below to check if your system detects an NVIDIA GPU.

docs/source/en/kv_cache.md

@@ -56,16 +56,16 @@ More concretely, key-value cache acts as a memory bank for these generative mode
   >>> import torch
   >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
 
-  >>> model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-  >>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+  >>> model_id = "meta-llama/Llama-2-7b-chat-hf"
+  >>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda:0")
   >>> tokenizer = AutoTokenizer.from_pretrained(model_id)
 
   >>> 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):
@@ -82,13 +82,7 @@ More concretely, key-value cache acts as a memory bank for these generative mode
   ...     cache_position = cache_position[-1:] + 1 # add one more position for the next token
 
   >>> print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
-  ```
-  ```txt
-  <|user|>
-  Hello, what's your name. 
-  <|assistant|>
-  My name is Sarah. 
-  <|
+  "[INST] Hello, what's your name. [/INST]  Hello! My name is LLaMA,"
   ```
 
 </details>
@@ -138,13 +132,17 @@ Cache quantization can be detrimental in terms of latency if the context length
 >>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
->>> tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
->>> model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
 >>> inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
 
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "backend": "quanto"})
 >>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
-I like rock music because it's a great way to express myself. I like the way it makes me feel, the
+I like rock music because it's loud and energetic. It's a great way to express myself and rel
+
+>>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+>>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
+I like rock music because it's loud and energetic. I like to listen to it when I'm feeling
 ```
 
 ### Offloaded Cache
@@ -168,7 +166,7 @@ Use `cache_implementation="offloaded_static"` for an offloaded static cache (see
 >>> ckpt = "microsoft/Phi-3-mini-4k-instruct"
 
 >>> tokenizer = AutoTokenizer.from_pretrained(ckpt)
->>> model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16, device_map="auto")
+>>> model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16).to("cuda:0")
 >>> inputs = tokenizer("Fun fact: The shortest", return_tensors="pt").to(model.device)
 
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=23, cache_implementation="offloaded")
@@ -233,14 +231,14 @@ For more examples with Static Cache and JIT compilation, take a look at [StaticC
 >>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
->>> tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
->>> model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> 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)
 
 >>> # simply pass the cache implementation="static"
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="static")
 >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
-"Hello, my name is [Your Name] and I am a [Your Position] at [Your Company]. I am writing"
+"Hello, my name is [Your Name], and I am a [Your Profession] with [Number of Years] of"
 ```
 
 
@@ -258,7 +256,7 @@ This will use the [`~OffloadedStaticCache`] implementation instead.
 >>> 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)
 
->>> # simply pass the cache implementation="offloaded_static"
+>>> # simply pass the cache implementation="static"
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="offloaded_static")
 >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
 "Hello, my name is [Your Name], and I am a [Your Profession] with [Number of Years] of"
@@ -277,14 +275,14 @@ Note that you can use this cache only for models that support sliding window, e.
 >>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache
 
->>> tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
->>> model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B", torch_dtype=torch.float16, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16).to("cuda:0")
 >>> inputs = tokenizer("Yesterday I was on a rock concert and.", return_tensors="pt").to(model.device)
 
 >>> # can be used by passing in cache implementation
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=30, cache_implementation="sliding_window")
 >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
-"Yesterday I was on a rock concert and. I was so excited to see my favorite band perform live. I was so happy that I could hardly contain myself. I was jumping up and down and"
+"Yesterday I was on a rock concert and. I was so excited to see my favorite band. I was so excited that I was jumping up and down and screaming. I was so excited that I"
 ```
 
 ### Sink Cache
@@ -297,8 +295,8 @@ Unlike other cache classes, this one can't be used directly by indicating a `cac
 >>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache
 
->>> tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
->>> model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
 >>> inputs = tokenizer("This is a long story about unicorns, fairies and magic.", return_tensors="pt").to(model.device)
 
 >>> # get our cache, specify number of sink tokens and window size
@@ -306,7 +304,7 @@ Unlike other cache classes, this one can't be used directly by indicating a `cac
 >>> past_key_values = SinkCache(window_length=256, num_sink_tokens=4)
 >>> out = model.generate(**inputs, do_sample=False, max_new_tokens=30, past_key_values=past_key_values)
 >>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
-"This is a long story about unicorns, fairies and magic. It is a story about a young girl named Lily who discovers that she has the power to control the elements. She learns that she can"
+"This is a long story about unicorns, fairies and magic. It is a fantasy world where unicorns and fairies live together in harmony. The story follows a young girl named Lily"
 ```
 
 ### Encoder-Decoder Cache
@@ -334,22 +332,22 @@ In case you are using Sink Cache, you have to crop your inputs to that maximum l
 >>> import torch
 >>> from transformers import AutoTokenizer,AutoModelForCausalLM
 >>> from transformers.cache_utils import (
-...    DynamicCache,
-...    SinkCache,
-...    StaticCache,
-...    SlidingWindowCache,
-...    QuantoQuantizedCache,
-...    QuantizedCacheConfig,
-... )
+>>>     DynamicCache,
+>>>     SinkCache,
+>>>     StaticCache,
+>>>     SlidingWindowCache,
+>>>     QuantoQuantizedCache,
+>>>     QuantizedCacheConfig,
+>>> )
 
->>> model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+>>> model_id = "meta-llama/Llama-2-7b-chat-hf"
 >>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map='auto')
 >>> tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 >>> user_prompts = ["Hello, what's your name?", "Btw, yesterday I was on a rock concert."]
 
 >>> past_key_values = DynamicCache()
->>> max_cache_length = past_key_values.get_max_cache_shape()
+>>> max_cache_length = past_key_values.get_max_length()
 
 >>> messages = []
 >>> for prompt in user_prompts:
@@ -365,7 +363,7 @@ In case you are using Sink Cache, you have to crop your inputs to that maximum l
 ...     messages.append({"role": "assistant", "content": completion})
 
 print(messages)
-[{'role': 'user', 'content': "Hello, what's your name?"}, {'role': 'assistant', 'content': "Hello, I'm AI."}, {'role': 'user', 'content': 'Btw, yesterday I was on a rock concert.'}, {'role': 'assistant', 'content': "I'm sorry to hear that you were on a rock concert yesterday. It sounds like a fun experience, but I'm not capable of experiencing music or concerts. However, I can provide you with some information about rock music and its history. Rock music emerged in the 1950s and 1960s in the United States and Britain, and it quickly gained popularity around the world. Some of the most famous rock bands of all time include The Beatles, The Rolling Stones, Led Zeppelin, and Pink Floyd. Rock music has a distinct sound and style, with elements of blues, country, and folk music. It often features guitar solos, heavy bass lines, and drums. Rock music has had a significant impact on popular culture, influencing genres such as punk rock, heavy metal, and alternative rock."}]
+[{'role': 'user', 'content': "Hello, what's your name?"}, {'role': 'assistant', 'content': " Hello! My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. 😊"}, {'role': 'user', 'content': 'Btw, yesterday I was on a rock concert.'}, {'role': 'assistant', 'content': ' Oh, cool! That sounds like a lot of fun! 🎉 Did you enjoy the concert? What was the band like? 🤔'}]
 ```
 
 
@@ -377,19 +375,17 @@ Sometimes you would want to first fill-in cache object with key/values for certa
 >>> import copy
 >>> import torch
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache, StaticCache
->>> from accelerate.test_utils.testing import get_backend
 
->>> DEVICE, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
->>> model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
->>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map=DEVICE)
+>>> model_id = "meta-llama/Llama-2-7b-chat-hf"
+>>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda")
 >>> tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 >>> # Init StaticCache with big enough max-length (1024 tokens for the below example)
 >>> # You can also init a DynamicCache, if that suits you better
->>> prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device=DEVICE, dtype=torch.bfloat16)
+>>> prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16)
 
 >>> INITIAL_PROMPT = "You are a helpful assistant. "
->>> inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to(DEVICE)
+>>> inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda")
 >>> # This is the common prompt cached, we need to run forward without grad to be abel to copy
 >>> with torch.no_grad():
 ...      prompt_cache = model(**inputs_initial_prompt, past_key_values = prompt_cache).past_key_values
@@ -397,14 +393,14 @@ Sometimes you would want to first fill-in cache object with key/values for certa
 >>> prompts = ["Help me to write a blogpost about travelling.", "What is the capital of France?"]
 >>> responses = []
 >>> for prompt in prompts:
-...     new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to(DEVICE)
+...     new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
 ...     past_key_values = copy.deepcopy(prompt_cache)
 ...     outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20)
 ...     response = tokenizer.batch_decode(outputs)[0]
 ...     responses.append(response)
 
 >>> print(responses)
-['<s> You are a helpful assistant. Help me to write a blogpost about travelling.  I am excited to share my experiences with you.  I have been traveling for the past', '<s> You are a helpful assistant. What is the capital of France? \n\nAnswer: Paris is the capital of France.</s>']
+['<s> You are a helpful assistant. Help me to write a blogpost about travelling.\n\nTitle: The Ultimate Guide to Travelling: Tips, Tricks, and', '<s> You are a helpful assistant. What is the capital of France?\n\nYes, the capital of France is Paris.</s>']
 ```
 
 
@@ -418,8 +414,8 @@ this legacy format, you can seamlessly convert it to a `DynamicCache` and back.
 >>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
 
->>> tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
->>> model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16, device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> 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. `return_legacy_cache` forces the returned cache

docs/source/en/llm_tutorial.md

@@ -40,7 +40,6 @@ Before you begin, make sure you have all the necessary libraries installed:
 ```bash
 pip install transformers bitsandbytes>=0.39.0 -q
 ```
-Bitsandbytes supports multiple backends in addition to CUDA-based GPUs. Refer to the multi-backend installation [guide](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend) to learn more.
 
 
 ## Generate text
@@ -102,11 +101,9 @@ Next, you need to preprocess your text input with a [tokenizer](tokenizer_summar
 
 ```py
 >>> from transformers import AutoTokenizer
->>> from accelerate.test_utils.testing import get_backend
 
->>> DEVICE, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
 >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
->>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to(DEVICE)
+>>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to("cuda")
 ```
 
 The `model_inputs` variable holds the tokenized text input, as well as the attention mask. While [`~generation.GenerationMixin.generate`] does its best effort to infer the attention mask when it is not passed, we recommend passing it whenever possible for optimal results.
@@ -125,7 +122,7 @@ Finally, you don't need to do it one sequence at a time! You can batch your inpu
 >>> tokenizer.pad_token = tokenizer.eos_token  # Most LLMs don't have a pad token by default
 >>> model_inputs = tokenizer(
 ...     ["A list of colors: red, blue", "Portugal is"], return_tensors="pt", padding=True
-... ).to(DEVICE)
+... ).to("cuda")
 >>> generated_ids = model.generate(**model_inputs)
 >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
 ['A list of colors: red, blue, green, yellow, orange, purple, pink,',
@@ -155,7 +152,7 @@ If not specified in the [`~generation.GenerationConfig`] file, `generate` return
 
 
 ```py
->>> model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to(DEVICE)
+>>> model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to("cuda")
 
 >>> # By default, the output will contain up to 20 tokens
 >>> generated_ids = model.generate(**model_inputs)
@@ -177,7 +174,7 @@ By default, and unless specified in the [`~generation.GenerationConfig`] file, `
 >>> from transformers import set_seed
 >>> set_seed(42)
 
->>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to(DEVICE)
+>>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to("cuda")
 
 >>> # LLM + greedy decoding = repetitive, boring output
 >>> generated_ids = model.generate(**model_inputs)
@@ -199,7 +196,7 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
 >>> # which is shorter, has padding on the right side. Generation fails to capture the logic.
 >>> model_inputs = tokenizer(
 ...     ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
-... ).to(DEVICE)
+... ).to("cuda")
 >>> generated_ids = model.generate(**model_inputs)
 >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 '1, 2, 33333333333'
@@ -209,7 +206,7 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
 >>> tokenizer.pad_token = tokenizer.eos_token  # Most LLMs don't have a pad token by default
 >>> model_inputs = tokenizer(
 ...     ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
-... ).to(DEVICE)
+... ).to("cuda")
 >>> generated_ids = model.generate(**model_inputs)
 >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 '1, 2, 3, 4, 5, 6,'
@@ -226,7 +223,7 @@ Some models and tasks expect a certain input prompt format to work properly. Whe
 ... )
 >>> set_seed(0)
 >>> prompt = """How many helicopters can a human eat in one sitting? Reply as a thug."""
->>> model_inputs = tokenizer([prompt], return_tensors="pt").to(DEVICE)
+>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 >>> input_length = model_inputs.input_ids.shape[1]
 >>> generated_ids = model.generate(**model_inputs, max_new_tokens=20)
 >>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
@@ -242,7 +239,7 @@ Some models and tasks expect a certain input prompt format to work properly. Whe
 ...     },
 ...     {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
 ... ]
->>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(DEVICE)
+>>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
 >>> input_length = model_inputs.shape[1]
 >>> generated_ids = model.generate(model_inputs, do_sample=True, max_new_tokens=20)
 >>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])

docs/source/en/llm_tutorial_optimization.md

@@ -55,7 +55,7 @@ To give some examples of how much VRAM it roughly takes to load a model in bfloa
 
 As of writing this document, the largest GPU chip on the market is the A100 & H100 offering 80GB of VRAM. Most of the models listed before require more than 80GB just to be loaded and therefore necessarily require [tensor parallelism](https://huggingface.co/docs/transformers/perf_train_gpu_many#tensor-parallelism) and/or [pipeline parallelism](https://huggingface.co/docs/transformers/perf_train_gpu_many#naive-model-parallelism-vertical-and-pipeline-parallelism).
 
-🤗 Transformers now supports tensor parallelism for supported models having `base_tp_plan` in their respecitve config classes. Learn more about Tensor Parallelism [here](perf_train_gpu_many#tensor-parallelism). Furthermore, if you're interested in writing models in a tensor-parallelism-friendly way, feel free to have a look at [the text-generation-inference library](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models/custom_modeling).
+🤗 Transformers does not support tensor parallelism out of the box as it requires the model architecture to be written in a specific way. If you're interested in writing models in a tensor-parallelism-friendly way, feel free to have a look at [the text-generation-inference library](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models/custom_modeling).
 
 Naive pipeline parallelism is supported out of the box. For this, simply load the model with `device="auto"` which will automatically place the different layers on the available GPUs as explained [here](https://huggingface.co/docs/accelerate/v0.22.0/en/concept_guides/big_model_inference).
 Note, however that while very effective, this naive pipeline parallelism does not tackle the issues of GPU idling. For this more advanced pipeline parallelism is required as explained [here](https://huggingface.co/docs/transformers/en/perf_train_gpu_many#naive-model-parallelism-vertical-and-pipeline-parallelism).

docs/source/en/main_classes/data_collator.md

@@ -71,6 +71,3 @@ Examples of use can be found in the [example scripts](../examples) or [example n
 
 [[autodoc]] data.data_collator.DataCollatorWithFlattening
 
-# DataCollatorForMultipleChoice
-
-[[autodoc]] data.data_collator.DataCollatorForMultipleChoice

docs/source/en/main_classes/deepspeed.md

@@ -16,7 +16,7 @@ rendered properly in your Markdown viewer.
 
 # DeepSpeed
 
-[DeepSpeed](https://github.com/deepspeedai/DeepSpeed), powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients, parameters, and enabling offloading to a CPU or NVMe. DeepSpeed is integrated with the [`Trainer`] class and most of the setup is automatically taken care of for you. 
+[DeepSpeed](https://github.com/microsoft/DeepSpeed), powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients, parameters, and enabling offloading to a CPU or NVMe. DeepSpeed is integrated with the [`Trainer`] class and most of the setup is automatically taken care of for you. 
 
 However, if you want to use DeepSpeed without the [`Trainer`], Transformers provides a [`HfDeepSpeedConfig`] class.
 

docs/source/en/main_classes/quantization.md

@@ -80,11 +80,3 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
 ## BitNetConfig
 
 [[autodoc]] BitNetConfig
-
-## SpQRConfig
-
-[[autodoc]] SpQRConfig
-
-## FineGrainedFP8Config
-
-[[autodoc]] FineGrainedFP8Config

docs/source/en/model_doc/blip.md

@@ -61,11 +61,6 @@ The original code can be found [here](https://github.com/salesforce/BLIP).
 [[autodoc]] BlipImageProcessor
     - preprocess
 
-## BlipImageProcessorFast
-
-[[autodoc]] BlipImageProcessorFast
-    - preprocess
-
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/clip.md

@@ -251,11 +251,6 @@ The resource should ideally demonstrate something new instead of duplicating an
 [[autodoc]] CLIPImageProcessor
     - preprocess
 
-## CLIPImageProcessorFast
-
-[[autodoc]] CLIPImageProcessorFast
-    - preprocess
-
 ## CLIPFeatureExtractor
 
 [[autodoc]] CLIPFeatureExtractor

docs/source/en/model_doc/convnext.md

@@ -64,11 +64,6 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] ConvNextImageProcessor
     - preprocess
 
-## ConvNextImageProcessorFast
-
-[[autodoc]] ConvNextImageProcessorFast
-    - preprocess
-
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/dab-detr.md

@@ -1,119 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-# DAB-DETR
-
-## Overview
-
-The DAB-DETR model was proposed in [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://arxiv.org/abs/2201.12329) by Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang.
-DAB-DETR is an enhanced variant of Conditional DETR. It utilizes dynamically updated anchor boxes to provide both a reference query point (x, y) and a reference anchor size (w, h), improving cross-attention computation. This new approach achieves 45.7% AP when trained for 50 epochs with a single ResNet-50 model as the backbone.
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dab_detr_convergence_plot.png"
-alt="drawing" width="600"/>
-
-The abstract from the paper is the following:
-
-*We present in this paper a novel query formulation using dynamic anchor boxes
-for DETR (DEtection TRansformer) and offer a deeper understanding of the role
-of queries in DETR. This new formulation directly uses box coordinates as queries
-in Transformer decoders and dynamically updates them layer-by-layer. Using box
-coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR,
-but also allows us to modulate the positional attention map using the box width
-and height information. Such a design makes it clear that queries in DETR can be
-implemented as performing soft ROI pooling layer-by-layer in a cascade manner.
-As a result, it leads to the best performance on MS-COCO benchmark among
-the DETR-like detection models under the same setting, e.g., AP 45.7% using
-ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive
-experiments to confirm our analysis and verify the effectiveness of our methods.*
-
-This model was contributed by [davidhajdu](https://huggingface.co/davidhajdu).
-The original code can be found [here](https://github.com/IDEA-Research/DAB-DETR).
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```python
-import torch
-import requests
-
-from PIL import Image
-from transformers import AutoModelForObjectDetection, AutoImageProcessor
-
-url = 'http://images.cocodataset.org/val2017/000000039769.jpg' 
-image = Image.open(requests.get(url, stream=True).raw)
-
-image_processor = AutoImageProcessor.from_pretrained("IDEA-Research/dab-detr-resnet-50")
-model = AutoModelForObjectDetection.from_pretrained("IDEA-Research/dab-detr-resnet-50")
-
-inputs = image_processor(images=image, return_tensors="pt")
-
-with torch.no_grad():
-    outputs = model(**inputs)
-
-results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
-
-for result in results:
-    for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
-        score, label = score.item(), label_id.item()
-        box = [round(i, 2) for i in box.tolist()]
-        print(f"{model.config.id2label[label]}: {score:.2f} {box}")
-```
-This should output
-```
-cat: 0.87 [14.7, 49.39, 320.52, 469.28]
-remote: 0.86 [41.08, 72.37, 173.39, 117.2]
-cat: 0.86 [344.45, 19.43, 639.85, 367.86]
-remote: 0.61 [334.27, 75.93, 367.92, 188.81]
-couch: 0.59 [-0.04, 1.34, 639.9, 477.09]
-```
-
-There are three other ways to instantiate a DAB-DETR model (depending on what you prefer):
-
-Option 1: Instantiate DAB-DETR with pre-trained weights for entire model
-```py
->>> from transformers import DabDetrForObjectDetection
-
->>> model = DabDetrForObjectDetection.from_pretrained("IDEA-Research/dab-detr-resnet-50")
-```
-
-Option 2: Instantiate DAB-DETR with randomly initialized weights for Transformer, but pre-trained weights for backbone
-```py
->>> from transformers import DabDetrConfig, DabDetrForObjectDetection
-
->>> config = DabDetrConfig()
->>> model = DabDetrForObjectDetection(config)
-```
-Option 3: Instantiate DAB-DETR with randomly initialized weights for backbone + Transformer
-```py
->>> config = DabDetrConfig(use_pretrained_backbone=False)
->>> model = DabDetrForObjectDetection(config)
-```
-
-
-## DabDetrConfig
-
-[[autodoc]] DabDetrConfig
-
-## DabDetrModel
-
-[[autodoc]] DabDetrModel
-    - forward
-
-## DabDetrForObjectDetection
-
-[[autodoc]] DabDetrForObjectDetection
-    - forward

docs/source/en/model_doc/deit.md

@@ -125,11 +125,6 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] DeiTImageProcessor
     - preprocess
 
-## DeiTImageProcessorFast
-
-[[autodoc]] DeiTImageProcessorFast
-    - preprocess
-
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/depth_pro.md

@@ -1,183 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-
-# DepthPro
-
-## Overview
-
-The DepthPro model was proposed in [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073) by Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun.
-
-DepthPro is a foundation model for zero-shot metric monocular depth estimation, designed to generate high-resolution depth maps with remarkable sharpness and fine-grained details. It employs a multi-scale Vision Transformer (ViT)-based architecture, where images are downsampled, divided into patches, and processed using a shared Dinov2 encoder. The extracted patch-level features are merged, upsampled, and refined using a DPT-like fusion stage, enabling precise depth estimation.
-
-The abstract from the paper is the following:
-
-*We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image. Extensive experiments analyze specific design choices and demonstrate that Depth Pro outperforms prior work along multiple dimensions.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_pro_teaser.png"
-alt="drawing" width="600"/>
-
-<small> DepthPro Outputs. Taken from the <a href="https://github.com/apple/ml-depth-pro" target="_blank">official code</a>. </small>
-
-This model was contributed by [geetu040](https://github.com/geetu040). The original code can be found [here](https://github.com/apple/ml-depth-pro).
-
-## Usage Tips
-
-The DepthPro model processes an input image by first downsampling it at multiple scales and splitting each scaled version into patches. These patches are then encoded using a shared Vision Transformer (ViT)-based Dinov2 patch encoder, while the full image is processed by a separate image encoder. The extracted patch features are merged into feature maps, upsampled, and fused using a DPT-like decoder to generate the final depth estimation. If enabled, an additional Field of View (FOV) encoder processes the image for estimating the camera's field of view, aiding in depth accuracy.
-
-```py
->>> import requests
->>> from PIL import Image
->>> import torch
->>> from transformers import DepthProImageProcessorFast, DepthProForDepthEstimation
-
->>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
->>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> image_processor = DepthProImageProcessorFast.from_pretrained("apple/DepthPro-hf")
->>> model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device)
-
->>> inputs = image_processor(images=image, return_tensors="pt").to(device)
-
->>> with torch.no_grad():
-...     outputs = model(**inputs)
-
->>> post_processed_output = image_processor.post_process_depth_estimation(
-...     outputs, target_sizes=[(image.height, image.width)],
-... )
-
->>> field_of_view = post_processed_output[0]["field_of_view"]
->>> focal_length = post_processed_output[0]["focal_length"]
->>> depth = post_processed_output[0]["predicted_depth"]
->>> depth = (depth - depth.min()) / depth.max()
->>> depth = depth * 255.
->>> depth = depth.detach().cpu().numpy()
->>> depth = Image.fromarray(depth.astype("uint8"))
-```
-
-### Architecture and Configuration
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_pro_architecture.png"
-alt="drawing" width="600"/>
-
-<small> DepthPro architecture. Taken from the <a href="https://arxiv.org/abs/2410.02073" target="_blank">original paper</a>. </small>
-
-The `DepthProForDepthEstimation` model uses a `DepthProEncoder`, for encoding the input image and a `FeatureFusionStage` for fusing the output features from encoder.
-
-The `DepthProEncoder` further uses two encoders:
-- `patch_encoder`
-   - Input image is scaled with multiple ratios, as specified in the `scaled_images_ratios` configuration.
-   - Each scaled image is split into smaller **patches** of size `patch_size` with overlapping areas determined by `scaled_images_overlap_ratios`.
-   - These patches are processed by the **`patch_encoder`**
-- `image_encoder`
-   - Input image is also rescaled to `patch_size` and processed by the **`image_encoder`**
-
-Both these encoders can be configured via `patch_model_config` and `image_model_config` respectively, both of which are seperate `Dinov2Model` by default.
-
-Outputs from both encoders (`last_hidden_state`) and selected intermediate states (`hidden_states`) from **`patch_encoder`** are fused by a `DPT`-based `FeatureFusionStage` for depth estimation.
-
-### Field-of-View (FOV) Prediction
-
-The network is supplemented with a focal length estimation head. A small convolutional head ingests frozen features from the depth estimation network and task-specific features from a separate ViT image encoder to predict the horizontal angular field-of-view.
-
-The `use_fov_model` parameter in `DepthProConfig` controls whether **FOV prediction** is enabled. By default, it is set to `False` to conserve memory and computation. When enabled, the **FOV encoder** is instantiated based on the `fov_model_config` parameter, which defaults to a `Dinov2Model`. The `use_fov_model` parameter can also be passed when initializing the `DepthProForDepthEstimation` model.
-
-The pretrained model at checkpoint `apple/DepthPro-hf` uses the FOV encoder. To use the pretrained-model without FOV encoder, set `use_fov_model=False` when loading the model, which saves computation.
-```py
->>> from transformers import DepthProForDepthEstimation
->>> model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf", use_fov_model=False)
-```
-
-To instantiate a new model with FOV encoder, set `use_fov_model=True` in the config.
-```py
->>> from transformers import DepthProConfig, DepthProForDepthEstimation
->>> config = DepthProConfig(use_fov_model=True)
->>> model = DepthProForDepthEstimation(config)
-```
-
-Or set `use_fov_model=True` when initializing the model, which overrides the value in config.
-```py
->>> from transformers import DepthProConfig, DepthProForDepthEstimation
->>> config = DepthProConfig()
->>> model = DepthProForDepthEstimation(config, use_fov_model=True)
-```
-
-### Using Scaled Dot Product Attention (SDPA)
-
-PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function 
-encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the 
-[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) 
-or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
-page for more information.
-
-SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set 
-`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
-
-```py
-from transformers import DepthProForDepthEstimation
-model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf", attn_implementation="sdpa", torch_dtype=torch.float16)
-```
-
-For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
-
-On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32` and `google/vit-base-patch16-224` model, we saw the following speedups during inference.
-
-|   Batch size |   Average inference time (ms), eager mode |   Average inference time (ms), sdpa model |   Speed up, Sdpa / Eager (x) |
-|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
-|            1 |                                         7 |                                         6 |                      1.17 |
-|            2 |                                         8 |                                         6 |                      1.33 |
-|            4 |                                         8 |                                         6 |                      1.33 |
-|            8 |                                         8 |                                         6 |                      1.33 |
-
-## Resources
-
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DepthPro:
-
-- Research Paper: [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/pdf/2410.02073)
-- Official Implementation: [apple/ml-depth-pro](https://github.com/apple/ml-depth-pro)
-- DepthPro Inference Notebook: [DepthPro Inference](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DepthPro_inference.ipynb)
-- DepthPro for Super Resolution and Image Segmentation
-    - Read blog on Medium: [Depth Pro: Beyond Depth](https://medium.com/@raoarmaghanshakir040/depth-pro-beyond-depth-9d822fc557ba)
-    - Code on Github: [geetu040/depthpro-beyond-depth](https://github.com/geetu040/depthpro-beyond-depth)
-
-If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
-
-## DepthProConfig
-
-[[autodoc]] DepthProConfig
-
-## DepthProImageProcessor
-
-[[autodoc]] DepthProImageProcessor
-    - preprocess
-    - post_process_depth_estimation
-
-## DepthProImageProcessorFast
-
-[[autodoc]] DepthProImageProcessorFast
-    - preprocess
-    - post_process_depth_estimation
-
-## DepthProModel
-
-[[autodoc]] DepthProModel
-    - forward
-
-## DepthProForDepthEstimation
-
-[[autodoc]] DepthProForDepthEstimation
-    - forward

docs/source/en/model_doc/glm.md

@@ -56,7 +56,7 @@ In the following, we demonstrate how to use `glm-4-9b-chat` for the inference. N
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
 >>> device = "cuda" # the device to load the model onto
 
->>> model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto")
 >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat")
 
 >>> prompt = "Give me a short introduction to large language model."

docs/source/en/model_doc/got_ocr2.md

@@ -1,269 +0,0 @@
-<!--Copyright 2024 StepFun and 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.
-
--->
-
-# GOT-OCR2
-
-## Overview
-
-The GOT-OCR2 model was proposed in [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://arxiv.org/abs/2409.01704) by Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, Chunrui Han, Xiangyu Zhang.
-
-The abstract from the paper is the following:
-
-*Traditional OCR systems (OCR-1.0) are increasingly unable to meet people’snusage due to the growing demand for intelligent processing of man-made opticalncharacters. In this paper, we collectively refer to all artificial optical signals (e.g., plain texts, math/molecular formulas, tables, charts, sheet music, and even geometric shapes) as "characters" and propose the General OCR Theory along with an excellent model, namely GOT, to promote the arrival of OCR-2.0. The GOT, with 580M parameters, is a unified, elegant, and end-to-end model, consisting of a high-compression encoder and a long-contexts decoder. As an OCR-2.0 model, GOT can handle all the above "characters" under various OCR tasks. On the input side, the model supports commonly used scene- and document-style images in slice and whole-page styles. On the output side, GOT can generate plain or formatted results (markdown/tikz/smiles/kern) via an easy prompt. Besides, the model enjoys interactive OCR features, i.e., region-level recognition guided by coordinates or colors. Furthermore, we also adapt dynamic resolution and multipage OCR technologies to GOT for better practicality. In experiments, we provide sufficient results to prove the superiority of our model.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/got_ocr_overview.png"
-alt="drawing" width="600"/>
-
-<small> GOT-OCR2 training stages. Taken from the <a href="https://arxiv.org/abs/2409.01704">original paper.</a> </small>
-
-
-Tips:
-
-GOT-OCR2 works on a wide range of tasks, including plain document OCR, scene text OCR, formatted document OCR, and even OCR for tables, charts, mathematical formulas, geometric shapes, molecular formulas and sheet music. While this implementation of the model will only output plain text, the outputs can be further processed to render the desired format, with packages like `pdftex`, `mathpix`, `matplotlib`, `tikz`, `verovio` or `pyecharts`.
-The model can also be used for interactive OCR, where the user can specify the region to be recognized by providing the coordinates or the color of the region's bounding box.
-
-This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan).
-The original code can be found [here](https://github.com/Ucas-HaoranWei/GOT-OCR2.0).
-
-## Usage example
-
-### Plain text inference
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
->>> inputs = processor(image, return_tensors="pt").to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-"R&D QUALITY IMPROVEMENT\nSUGGESTION/SOLUTION FORM\nName/Phone Ext. : (...)"
-```
-
-### Plain text inference batched
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
->>> image2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
-
->>> inputs = processor([image1, image2], return_tensors="pt").to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4,
-... )
-
->>> processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
-["Reducing the number", "R&D QUALITY"]
-```
-
-### Formatted text inference
-
-GOT-OCR2 can also generate formatted text, such as markdown or LaTeX. Here is an example of how to generate formatted text:
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/latex.png"
->>> inputs = processor(image, return_tensors="pt", format=True).to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-"\\author{\nHanwen Jiang* \\(\\quad\\) Arjun Karpur \\({ }^{\\dagger} \\quad\\) Bingyi Cao \\({ }^{\\dagger} \\quad\\) (...)"
-```
-
-### Inference on multiple pages
-
-Although it might be reasonable in most cases to use a “for loop” for multi-page processing, some text data with formatting across several pages make it necessary to process all pages at once. GOT introduces a multi-page OCR (without “for loop”) feature, where multiple pages can be processed by the model at once, whith the output being one continuous text.
-Here is an example of how to process multiple pages at once:
-
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/page1.png"
->>> image2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/page2.png"
->>> inputs = processor([image1, image2], return_tensors="pt", multi_page=True, format=True).to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-"\\title{\nGeneral OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model\n}\n\\author{\nHaoran Wei (...)"
-```
-
-### Inference on cropped patches
-
-GOT supports a 1024×1024 input resolution, which is sufficient for most OCR tasks, such as scene OCR or processing A4-sized PDF pages. However, certain scenarios, like horizontally stitched two-page PDFs commonly found in academic papers or images with unusual aspect ratios, can lead to accuracy issues when processed as a single image. To address this, GOT can dynamically crop an image into patches, process them all at once, and merge the results for better accuracy with such inputs.
-Here is an example of how to process cropped patches:
-
-```python
->>> import torch
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", torch_dtype=torch.bfloat16, device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png"
->>> inputs = processor(image, return_tensors="pt", format=True, crop_to_patches=True, max_patches=3).to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-"on developing architectural improvements to make learnable matching methods generalize.\nMotivated by the above observations, (...)"
-```
-
-### Inference on a specific region
-
-GOT supports interactive OCR, where the user can specify the region to be recognized by providing the coordinates or the color of the region's bounding box. Here is an example of how to process a specific region:
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
->>> inputs = processor(image, return_tensors="pt", color="green").to(device) # or box=[x1, y1, x2, y2] for coordinates (image pixels)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-"You should keep in mind what features from the module should be used, especially \nwhen you’re planning to sell a template."
-```
-
-### Inference on general OCR data example: sheet music
-
-Although this implementation of the model will only output plain text, the outputs can be further processed to render the desired format, with packages like `pdftex`, `mathpix`, `matplotlib`, `tikz`, `verovio` or `pyecharts`.
-Here is an example of how to process sheet music:
-
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
->>> import verovio
-
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
-
->>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/sheet_music.png"
->>> inputs = processor(image, return_tensors="pt", format=True).to(device)
-
->>> generate_ids = model.generate(
-...     **inputs,
-...     do_sample=False,
-...     tokenizer=processor.tokenizer,
-...     stop_strings="<|im_end|>",
-...     max_new_tokens=4096,
-... )
-
->>> outputs = processor.decode(generate_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
->>> tk = verovio.toolkit()
->>> tk.loadData(outputs)
->>> tk.setOptions(
-...     {
-...         "pageWidth": 2100,
-...         "pageHeight": 800,
-...         "footer": "none",
-...         "barLineWidth": 0.5,
-...         "beamMaxSlope": 15,
-...         "staffLineWidth": 0.2,
-...         "spacingStaff": 6,
-...     }
-... )
->>> tk.getPageCount()
->>> svg = tk.renderToSVG()
->>> svg = svg.replace('overflow="inherit"', 'overflow="visible"')
->>> with open("output.svg", "w") as f:
->>>     f.write(svg)
-```
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/sheet_music.svg"
-alt="drawing" width="600"/>
-
-## GotOcr2Config
-
-[[autodoc]] GotOcr2Config
-
-## GotOcr2VisionConfig
-
-[[autodoc]] GotOcr2VisionConfig
-
-## GotOcr2ImageProcessor
-
-[[autodoc]] GotOcr2ImageProcessor
-
-## GotOcr2Processor
-
-[[autodoc]] GotOcr2Processor
-
-## GotOcr2ForConditionalGeneration
-
-[[autodoc]] GotOcr2ForConditionalGeneration
-    - forward
-

docs/source/en/model_doc/granitemoeshared.md

@@ -1,66 +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.
-
-⚠️ 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.
-
--->
-
-# GraniteMoeShared
-
-## Overview
-
-
-The GraniteMoe model was proposed in [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://arxiv.org/abs/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.
-
-Additionally this class GraniteMoeSharedModel adds shared experts for Moe.
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_path = "ibm-research/moe-7b-1b-active-shared-experts"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-# drop device_map if running on CPU
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
-model.eval()
-
-# change input text as desired
-prompt = "Write a code to find the maximum value in a list of numbers."
-
-# tokenize the text
-input_tokens = tokenizer(prompt, return_tensors="pt")
-# generate output tokens
-output = model.generate(**input_tokens, max_new_tokens=100)
-# decode output tokens into text
-output = tokenizer.batch_decode(output)
-# loop over the batch to print, in this example the batch size is 1
-for i in output:
-    print(i)
-```
-
-This HF implementation is contributed by [Mayank Mishra](https://huggingface.co/mayank-mishra), [Shawn Tan](https://huggingface.co/shawntan) and [Sukriti Sharma](https://huggingface.co/SukritiSharma).
-
-
-## GraniteMoeSharedConfig
-
-[[autodoc]] GraniteMoeSharedConfig
-
-## GraniteMoeSharedModel
-
-[[autodoc]] GraniteMoeSharedModel
-    - forward
-
-## GraniteMoeSharedForCausalLM
-
-[[autodoc]] GraniteMoeSharedForCausalLM
-    - forward

docs/source/en/model_doc/granitevision.md

@@ -31,8 +31,13 @@ Tips:
 Sample inference:
 ```python
 from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+from PIL import Image
+import requests
 
-model_path = "ibm-granite/granite-vision-3.1-2b-preview"
+# Note: These docs were written prior to the public model release,
+# and this path is subject to change.
+# Please see https://huggingface.co/ibm-granite for the current model list.
+model_path = "ibm-granite/granite-3.1-2b-instruct-vision"
 processor = LlavaNextProcessor.from_pretrained(model_path)
 
 model = LlavaNextForConditionalGeneration.from_pretrained(model_path).to("cuda")

docs/source/en/model_doc/grounding-dino.md

@@ -64,19 +64,18 @@ Here's how to use the model for zero-shot object detection:
 
 >>> results = processor.post_process_grounded_object_detection(
 ...     outputs,
-...     inputs.input_ids,
-...     box_threshold=0.4,
+...     threshold=0.4,
 ...     text_threshold=0.3,
-...     target_sizes=[image.size[::-1]]
+...     target_sizes=[(image.height, image.width)]
 ... )
-
-# Retrieve the first image result
+>>> # Retrieve the first image result
 >>> result = results[0]
->>> for box, score, labels in zip(result["boxes"], result["scores"], result["labels"]):
+>>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
 ...     box = [round(x, 2) for x in box.tolist()]
-...     print(f"Detected {labels} with confidence {round(score.item(), 3)} at location {box}")
-Detected a cat with confidence 0.468 at location [344.78, 22.9, 637.3, 373.62]
-Detected a cat with confidence 0.426 at location [11.74, 51.55, 316.51, 473.22]
+...     print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
+Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
+Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
+Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
 ```
 
 ## Grounded SAM

docs/source/en/model_doc/helium.md

@@ -107,7 +107,7 @@ Tips:
   
 ## Usage tips
 
-`Helium` can be found on the [Huggingface Hub](https://huggingface.co/models?other=helium)
+`Helium` can be found on the [Huggingface Hub](https://huggingface.co/collections/kyutai/helium-1-preview)
 
 In the following, we demonstrate how to use `helium-1-preview` for the inference. 
 
@@ -115,12 +115,16 @@ In the following, we demonstrate how to use `helium-1-preview` for the inference
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
 >>> device = "cuda" # the device to load the model onto
 
->>> model = AutoModelForCausalLM.from_pretrained("kyutai/helium-1-preview-2b", device_map="auto")
->>> tokenizer = AutoTokenizer.from_pretrained("kyutai/helium-1-preview-2b")
+>>> model = AutoModelForCausalLM.from_pretrained("helium-1-preview", device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("helium-1-preview")
 
 >>> prompt = "Give me a short introduction to large language model."
 
->>> model_inputs = tokenizer(prompt, return_tensors="pt").to(device)
+>>> messages = [{"role": "user", "content": prompt}]
+
+>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+>>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
 
 >>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
 

docs/source/en/model_doc/llava.md

@@ -47,19 +47,9 @@ Adding these attributes means that LLaVA will try to infer the number of image t
 The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches.
 
 
-### Formatting Prompts with Chat Templates  
-
-Each **checkpoint** is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.  
-
-**Important:**  
-- You must construct a conversation history — passing a plain string won't work.  
-- Each message should be a dictionary with `"role"` and `"content"` keys.  
-- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.  
-
-
-Here’s an example of how to structure your input. 
-We will use [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) and a conversation history of text and image. Each content field has to be a list of dicts, as follows:
+### Single image inference
 
+For best results, we recommend users to use the processor's `apply_chat_template()` method to format your prompt correctly. For that you need to construct a conversation history, passing in a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities, as follows:
 
 ```python
 from transformers import AutoProcessor
@@ -94,6 +84,60 @@ print(text_prompt)
 >>> "USER: <image>\n<What’s shown in this image? ASSISTANT: This image shows a red stop sign.</s>USER: Describe the image in more details. ASSISTANT:"
 ```
 
+### Batched inference
+
+LLaVa also supports batched inference. Here is how you can do it:
+
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+# Load the model in half-precision
+model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto")
+processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+# Get two different images
+url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+image_stop = Image.open(requests.get(url, stream=True).raw)
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image_cats = Image.open(requests.get(url, stream=True).raw)
+
+# Prepare a batch of two prompts
+conversation_1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image?"},
+        ],
+    },
+]
+
+conversation_2 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image?"},
+        ],
+    },
+]
+
+prompt_1 = processor.apply_chat_template(conversation_1, add_generation_prompt=True)
+prompt_2 = processor.apply_chat_template(conversation_2, add_generation_prompt=True)
+prompts = [prompt_1, prompt_2]
+
+# We can simply feed images in the order they have to be used in the text prompt
+inputs = processor(images=[image_stop, image_cats], text=prompts, padding=True, return_tensors="pt").to(model.device, torch.float16)
+
+# Generate
+generate_ids = model.generate(**inputs, max_new_tokens=30)
+processor.batch_decode(generate_ids, skip_special_tokens=True)
+```
+
 - If you want to construct a chat prompt yourself, below is a list of prompt formats accepted by each llava checkpoint:
 
 [llava-interleave models](https://huggingface.co/collections/llava-hf/llava-interleave-668e19a97da0036aad4a2f19) requires the following format:
@@ -118,96 +162,6 @@ For multiple turns conversation:
 "USER: <image>\n<prompt1> ASSISTANT: <answer1></s>USER: <prompt2> ASSISTANT: <answer2></s>USER: <prompt3> ASSISTANT:"
 ```
 
-🚀 **Bonus:** If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
-
-## Usage examples
-
-### Single input inference
-
-
-```python
-import torch
-from transformers import AutoProcessor, LlavaForConditionalGeneration
-
-# Load the model in half-precision
-model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto")
-processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
-
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
-            {"type": "text", "text": "What is shown in this image?"},
-        ],
-    },
-]
-
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device, torch.float16)
-
-# Generate
-generate_ids = model.generate(**inputs, max_new_tokens=30)
-processor.batch_decode(generate_ids, skip_special_tokens=True)
-```
-
-
-### Batched inference
-
-LLaVa also supports batched inference. Here is how you can do it:
-
-```python
-import torch
-from transformers import AutoProcessor, LlavaForConditionalGeneration
-
-# Load the model in half-precision
-model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto")
-processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
-
-
-# Prepare a batch of two prompts
-conversation_1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
-            {"type": "text", "text": "What is shown in this image?"},
-        ],
-    },
-]
-
-conversation_2 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
-            {"type": "text", "text": "What is shown in this image?"},
-        ],
-    },
-]
-
-inputs = processor.apply_chat_template(
-    [conversation_1, conversation_2],
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    padding=True,
-    return_tensors="pt"
-).to(model.device, torch.float16)
-
-
-# Generate
-generate_ids = model.generate(**inputs, max_new_tokens=30)
-processor.batch_decode(generate_ids, skip_special_tokens=True)
-```
-
-
 ## Note regarding reproducing original implementation
 
 In order to match the logits of the [original implementation](https://github.com/haotian-liu/LLaVA/tree/main), one needs to additionally specify `do_pad=True` when instantiating `LLavaImageProcessor`:
@@ -241,11 +195,6 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] LlavaImageProcessor
     - preprocess
 
-## LlavaImageProcessorFast
-
-[[autodoc]] LlavaImageProcessorFast
-    - preprocess
-
 ## LlavaProcessor
 
 [[autodoc]] LlavaProcessor

docs/source/en/model_doc/llava_next.md

@@ -59,17 +59,9 @@ Adding these attributes means that LLaVA will try to infer the number of image t
 The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches.
 
 
-### Formatting Prompts with Chat Templates  
+- Note that each checkpoint has been trained with a specific prompt format, depending on which large language model (LLM) was used. You can use the processor's `apply_chat_template` to format your prompts correctly. For that you have to construct a conversation history, passing a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities. Below is an example of how to do that and the list of formats accepted by each checkpoint.
 
-Each **checkpoint** is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.  
-
-**Important:**  
-- You must construct a conversation history — passing a plain string won't work.  
-- Each message should be a dictionary with `"role"` and `"content"` keys.  
-- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.  
-
-
-Here’s an example of how to structure your input. We will use [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) and a conversation history of text and image.
+We will use [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) and a conversation history of text and image. Each content field has to be a list of dicts, as follows:
 
 ```python
 from transformers import LlavaNextProcessor
@@ -133,10 +125,6 @@ print(text_prompt)
 "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
 ```
 
-🚀 **Bonus:** If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
-
-
 ## Usage example
 
 ### Single image inference
@@ -300,11 +288,6 @@ model = AutoModelForImageTextToText.from_pretrained(
 [[autodoc]] LlavaNextImageProcessor
     - preprocess
 
-## LlavaNextImageProcessorFast
-
-[[autodoc]] LlavaNextImageProcessorFast
-    - preprocess
-
 ## LlavaNextProcessor
 
 [[autodoc]] LlavaNextProcessor

docs/source/en/model_doc/llava_next_video.md

@@ -56,17 +56,9 @@ Adding these attributes means that LLaVA will try to infer the number of image t
 The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches.
 
 
-### Formatting Prompts with Chat Templates  
+- Note that each checkpoint has been trained with a specific prompt format, depending on which large language model (LLM) was used. You can use tokenizer's `apply_chat_template` to format your prompts correctly. Below is an example of how to do that.
 
-Each **checkpoint** is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.  
-
-**Important:**  
-- You must construct a conversation history — passing a plain string won't work.  
-- Each message should be a dictionary with `"role"` and `"content"` keys.  
-- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.  
-
-
-Here’s an example of how to structure your input. We will use [LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf) and a conversation history of videos and images.
+We will use [LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf) and a conversation history of videos and images. Each content field has to be a list of dicts, as follows:
 
 ```python
 from transformers import LlavaNextVideoProcessor
@@ -107,10 +99,6 @@ text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=
 print(text_prompt)
 ```
 
-🚀 **Bonus:** If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
-
-
 ## Usage example
 
 ### Single Media Mode
@@ -118,16 +106,41 @@ print(text_prompt)
 The model can accept both images and videos as input. Here's an example code for inference in half-precision (`torch.float16`):
 
 ```python
-from huggingface_hub import hf_hub_download
+import av
 import torch
+import numpy as np
 from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoProcessor
 
+def read_video_pyav(container, indices):
+    '''
+    Decode the video with PyAV decoder.
+    Args:
+        container (`av.container.input.InputContainer`): PyAV container.
+        indices (`List[int]`): List of frame indices to decode.
+    Returns:
+        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    container.seek(0)
+    start_index = indices[0]
+    end_index = indices[-1]
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > end_index:
+            break
+        if i >= start_index and i in indices:
+            frames.append(frame)
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
 # Load the model in half-precision
 model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", torch_dtype=torch.float16, device_map="auto")
 processor = LlavaNextVideoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")
 
 # Load the video as an np.array, sampling uniformly 8 frames (can sample more for longer videos)
 video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
+container = av.open(video_path)
+total_frames = container.streams.video[0].frames
+indices = np.arange(0, total_frames, total_frames / 8).astype(int)
+video = read_video_pyav(container, indices)
 
 conversation = [
     {
@@ -135,12 +148,13 @@ conversation = [
         "role": "user",
         "content": [
             {"type": "text", "text": "Why is this video funny?"},
-            {"type": "video", "path": video_path},
+            {"type": "video"},
             ],
     },
 ]
 
-inputs = processor.apply_chat_template(conversation, num_frames=8, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+inputs = processor(text=prompt, videos=video, return_tensors="pt")
 
 out = model.generate(**inputs, max_new_tokens=60)
 processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
@@ -152,15 +166,20 @@ processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spac
 The model can also generate from an interleaved image-video inputs. However note, that it was not trained in interleaved image-video setting which might affect the performance. Below is an example usage for mixed media input, add the following lines to the above code snippet: 
 
 ```python
+from PIL import Image
+import requests
 
 # Generate from image and video mixed inputs
+# Load and image and write a new prompt
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
 conversation = [
     {
 
         "role": "user",
         "content": [
             {"type": "text", "text": "How many cats are there in the image?"},
-            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+            {"type": "image"},
             ],
     },
     {
@@ -173,11 +192,12 @@ conversation = [
         "role": "user",
         "content": [
             {"type": "text", "text": "Why is this video funny?"},
-            {"type": "video", "path": video_path},
+            {"type": "video"},
             ],
     },
 ]
-inputs = processor.apply_chat_template(conversation, num_frames=8, add_generation_prompt=True, tokenize=True, return_dict=True, padding=True, return_tensors="pt")
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")
 
 # Generate
 generate_ids = model.generate(**inputs, max_length=50)

docs/source/en/model_doc/llava_onevision.md

@@ -47,18 +47,8 @@ Tips:
 
 </Tip>
 
+- Note that the model should use a specific prompt format, on which the large language model (LLM) was trained. You can use the processor's `apply_chat_template` to format your prompts correctly. For that you have to construct a conversation history, passing a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities.
 
-### Formatting Prompts with Chat Templates  
-
-Each **checkpoint** is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.  
-
-**Important:**  
-- You must construct a conversation history — passing a plain string won't work.  
-- Each message should be a dictionary with `"role"` and `"content"` keys.  
-- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.  
-
-
-Here’s an example of how to structure your input. 
 We will use [llava-onevision-qwen2-7b-si-hf](https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-si-hf) and a conversation history of text and image. Each content field has to be a list of dicts, as follows:
 
 ```python
@@ -91,12 +81,9 @@ text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=
 
 # Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your images
 print(text_prompt)
-'<|im_start|>user\n<image>What is shown in this image?<|im_end|>\n<|im_start|>assistant\nPage showing the list of options.<|im_end|>'
+>>> "<|im_start|>user\n<image>What is shown in this image?<|im_end|>\n<|im_start|>assistant\nPage showing the list of options.<|im_end|>"
 ```
 
-🚀 **Bonus:** If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
-
 This model was contributed by [RaushanTurganbay](https://huggingface.co/RaushanTurganbay).
 The original code can be found [here](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main).
 
@@ -110,28 +97,28 @@ Here's how to load the model and perform inference in half-precision (`torch.flo
 ```python
 from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 import torch
+from PIL import Image
+import requests
 
 processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") 
-model = LlavaOnevisionForConditionalGeneration.from_pretrained(
-    "llava-hf/llava-onevision-qwen2-7b-ov-hf",
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True,
-    device_map="cuda:0"
-)
+model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) 
+model.to("cuda:0")
 
 # prepare image and text prompt, using the appropriate prompt template
 url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+
 conversation = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "url": url},
+            {"type": "image"},
             {"type": "text", "text": "What is shown in this image?"},
         ],
     },
 ]
-inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
-inputs = inputs.to("cuda:0", torch.float16)
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+inputs = processor(images=image, text=prompt, return_tensors="pt").to("cuda:0", torch.float16)
 
 # autoregressively complete prompt
 output = model.generate(**inputs, max_new_tokens=100)
@@ -153,12 +140,22 @@ from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
 processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
 
+# Get three different images
+url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+image_stop = Image.open(requests.get(url, stream=True).raw)
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image_cats = Image.open(requests.get(url, stream=True).raw)
+
+url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
+image_snowman = Image.open(requests.get(url, stream=True).raw)
+
 # Prepare a batch of two prompts, where the first one is a multi-turn conversation and the second is not
 conversation_1 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
+            {"type": "image"},
             {"type": "text", "text": "What is shown in this image?"},
             ],
     },
@@ -171,7 +168,7 @@ conversation_1 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+            {"type": "image"},
             {"type": "text", "text": "What about this image? How many cats do you see?"},
             ],
     },
@@ -181,20 +178,18 @@ conversation_2 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
+            {"type": "image"},
             {"type": "text", "text": "What is shown in this image?"},
             ],
     },
 ]
 
-inputs = processor.apply_chat_template(
-    [conversation_1, conversation_2],
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    padding=True,
-    return_tensors="pt"
-).to(model.device, torch.float16)
+prompt_1 = processor.apply_chat_template(conversation_1, add_generation_prompt=True)
+prompt_2 = processor.apply_chat_template(conversation_2, add_generation_prompt=True)
+prompts = [prompt_1, prompt_2]
+
+# We can simply feed images in the order they have to be used in the text prompt
+inputs = processor(images=[image_stop, image_cats, image_snowman], text=prompts, padding=True, return_tensors="pt").to(model.device, torch.float16)
 
 # Generate
 generate_ids = model.generate(**inputs, max_new_tokens=30)
@@ -207,7 +202,10 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
 LLaVa-OneVision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:
 
 ```python
+import av
+import numpy as np
 from huggingface_hub import hf_hub_download
+
 import torch
 from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 
@@ -215,26 +213,48 @@ from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
 model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
 processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
 
+
+def read_video_pyav(container, indices):
+    '''
+    Decode the video with PyAV decoder.
+    Args:
+        container (`av.container.input.InputContainer`): PyAV container.
+        indices (`List[int]`): List of frame indices to decode.
+    Returns:
+        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    container.seek(0)
+    start_index = indices[0]
+    end_index = indices[-1]
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > end_index:
+            break
+        if i >= start_index and i in indices:
+            frames.append(frame)
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+# Load the video as an np.array, sampling uniformly 8 frames (can sample more for longer videos, up to 32 frames)
 video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
+container = av.open(video_path)
+total_frames = container.streams.video[0].frames
+indices = np.arange(0, total_frames, total_frames / 8).astype(int)
+video = read_video_pyav(container, indices)
+
+# For videos we have to feed a "video" type instead of "image"
 conversation = [
     {
 
         "role": "user",
         "content": [
-            {"type": "video", "path": video_path},
+            {"type": "video"},
             {"type": "text", "text": "Why is this video funny?"},
             ],
     },
 ]
 
-inputs = processor.apply_chat_template(
-    conversation,
-    num_frames=8
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device, torch.float16)
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+inputs = processor(videos=list(video), text=prompt, return_tensors="pt").to("cuda:0", torch.float16)
 
 out = model.generate(**inputs, max_new_tokens=60)
 processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
@@ -278,8 +298,8 @@ First make sure to install flash-attn. Refer to the [original repository of Flas
 from transformers import LlavaOnevisionForConditionalGeneration
 
 model = LlavaOnevisionForConditionalGeneration.from_pretrained(
-    model_id,
-    torch_dtype=torch.float16,
+    model_id, 
+    torch_dtype=torch.float16, 
     low_cpu_mem_usage=True,
     use_flash_attention_2=True
 ).to(0)
@@ -298,11 +318,6 @@ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
 
 [[autodoc]] LlavaOnevisionImageProcessor
 
-## LlavaOnevisionImageProcessorFast
-
-[[autodoc]] LlavaOnevisionImageProcessorFast
-    - preprocess
-
 ## LlavaOnevisionVideoProcessor
 
 [[autodoc]] LlavaOnevisionVideoProcessor

docs/source/en/model_doc/mllama.md

@@ -28,8 +28,7 @@ The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a
 - For text-only inputs use `MllamaForCausalLM` for generation to avoid loading vision tower.
 - Each sample can contain multiple images, and the number of images can vary between samples. The processor will pad the inputs to the maximum number of images across samples and to a maximum number of tiles within each image.
 - The text passed to the processor should have the `"<|image|>"` tokens where the images should be inserted.
-- The processor has its own `apply_chat_template` method to convert chat messages to text that can then be passed as text to the processor. If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
+- The processor has its own `apply_chat_template` method to convert chat messages to text that can then be passed as text to the processor.
 
 
 <Tip warning={true}>
@@ -54,7 +53,9 @@ model.set_output_embeddings(resized_embeddings)
 
 #### Instruct model
 ```python
+import requests
 import torch
+from PIL import Image
 from transformers import MllamaForConditionalGeneration, AutoProcessor
 
 model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
@@ -66,13 +67,18 @@ messages = [
         {
             "role": "user", 
             "content": [
-                {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                {"type": "image"},
                 {"type": "text", "text": "What does the image show?"}
             ]
         }
     ],
 ]
-inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+url = "https://llava-vl.github.io/static/images/view.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)
 output = model.generate(**inputs, max_new_tokens=25)
 print(processor.decode(output[0]))
 ```

docs/source/en/model_doc/moshi.md

@@ -110,13 +110,8 @@ To follow the example of the following image, `"Hello, I'm Moshi"` could be tran
 >>> from datasets import load_dataset, Audio
 >>> import torch, math
 >>> from transformers import MoshiForConditionalGeneration, AutoFeatureExtractor, AutoTokenizer
-
-
 >>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("kyutai/moshiko-pytorch-bf16")
->>> tokenizer = AutoTokenizer.from_pretrained("kyutai/moshiko-pytorch-bf16")
->>> device = "cuda"
->>> dtype = torch.bfloat16
+
 
 >>> # prepare user input audio 
 >>> librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))

docs/source/en/model_doc/phi3.md

@@ -57,7 +57,10 @@ Phi-3 has been integrated in the development version (4.40.0.dev) of `transforme
 >>> outputs = model.generate(inputs, max_new_tokens=32)
 >>> text = tokenizer.batch_decode(outputs)[0]
 >>> print(text)
-<|user|> Can you provide ways to eat combinations of bananas and dragonfruits?<|end|><|assistant|> Certainly! Bananas and dragonfruits can be combined in various delicious ways. Here are some creative ideas for incorporating both fruits
+<s><|user|> 
+Can you provide ways to eat combinations of bananas and dragonfruits?<|end|> 
+<|assistant|> 
+Certainly! Bananas and dragonfruits can be combined in various delicious ways. Here are some ideas for eating combinations of bananas and
 ```
 
 ## Phi3Config

docs/source/en/model_doc/pixtral.md

@@ -38,42 +38,38 @@ Tips:
 ```
 "<s>[INST][IMG]\nWhat are the things I should be cautious about when I visit this place?[/INST]"
 ```
-Then, the processor will replace each `[IMG]` token with a number of `[IMG]` tokens that depend on the height and the width of each image. Each *row* of the image is separated by an `[IMG_BREAK]` token, and each image is separated by an `[IMG_END]` token. It's advised to use the `apply_chat_template` method of the processor, which takes care of all of this and formats the text for you. If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the [usage section](#usage) for more info.
-
+Then, the processor will replace each `[IMG]` token with a number of `[IMG]` tokens that depend on the height and the width of each image. Each *row* of the image is separated by an `[IMG_BREAK]` token, and each image is separated by an `[IMG_END]` token. It's advised to use the `apply_chat_template` method of the processor, which takes care of all of this. See the [usage section](#usage) for more info.
 
 This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts) and [ArthurZ](https://huggingface.co/ArthurZ). The original code can be found [here](https://github.com/vllm-project/vllm/pull/8377).
 
-
 ## Usage
 
 At inference time, it's advised to use the processor's `apply_chat_template` method, which correctly formats the prompt for the model:
 
 ```python
 from transformers import AutoProcessor, LlavaForConditionalGeneration
+from PIL import Image
 
 model_id = "mistral-community/pixtral-12b"
 processor = AutoProcessor.from_pretrained(model_id)
-model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="cuda")
+model = LlavaForConditionalGeneration.from_pretrained(model_id).to("cuda")
+
+url_dog = "https://picsum.photos/id/237/200/300"
+url_mountain = "https://picsum.photos/seed/picsum/200/300"
 
 chat = [
     {
       "role": "user", "content": [
         {"type": "text", "content": "Can this animal"}, 
-        {"type": "image", "url": "https://picsum.photos/id/237/200/300"}, 
+        {"type": "image"}, 
         {"type": "text", "content": "live here?"}, 
-        {"type": "image", "url": "https://picsum.photos/seed/picsum/200/300"}
+        {"type": "image"}
       ]
     }
 ]
 
-inputs = processor.apply_chat_template(
-    chat,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
-
+prompt = processor.apply_chat_template(chat)
+inputs = processor(text=prompt, images=[url_dog, url_mountain], return_tensors="pt").to(model.device)
 generate_ids = model.generate(**inputs, max_new_tokens=500)
 output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 ```

docs/source/en/model_doc/qwen2_5_vl.md

@@ -32,13 +32,21 @@ The model can accept both images and videos as input. Here's an example code for
 
 ```python
 
+from PIL import Image
+import requests
 import torch
+from torchvision import io
+from typing import Dict
+from transformers.image_utils import load_images, load_video
 from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
 
 # Load the model in half-precision on the available device(s)
 model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto")
 processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
 
+# Image
+url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
 
 conversation = [
     {
@@ -46,7 +54,6 @@ conversation = [
         "content":[
             {
                 "type":"image",
-                "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
             },
             {
                 "type":"text",
@@ -56,14 +63,13 @@ conversation = [
     }
 ]
 
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
 
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
+
+inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
 
 # Inference: Generation of the output
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -72,24 +78,25 @@ output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, cl
 print(output_text)
 
 # Video
+video = load_video(video="/path/to/video.mp4")
 conversation = [
     {
         "role": "user",
         "content": [
-            {"type": "video", "path": "/path/to/video.mp4"},
+            {"type": "video"},
             {"type": "text", "text": "What happened in the video?"},
         ],
     }
 ]
 
-inputs = processor.apply_chat_template(
-    conversation,
-    video_fps=1,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>What happened in the video?<|im_end|>\n<|im_start|>assistant\n'
+
+# Qwen2.5VL modifies the time positional encoding (MRoPE) according to the video's frame rate (FPS).
+# Therefore, the video's FPS information needs to be provided as input.
+inputs = processor(text=[text_prompt], videos=[video], fps=[1.0], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
 
 # Inference: Generation of the output
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -103,12 +110,21 @@ print(output_text)
 The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
 
 ```python
+images = load_images([
+    "/path/to/image1.jpg",
+    "/path/to/image2.jpg",
+    "/path/to/image3.jpg",
+    "/path/to/image4.jpg",
+    "/path/to/image5.jpg",
+])
+video = load_video(video="/path/to/video.mp4")
+
 # Conversation for the first image
 conversation1 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image1.jpg"},
+            {"type": "image"},
             {"type": "text", "text": "Describe this image."}
         ]
     }
@@ -119,8 +135,8 @@ conversation2 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image2.jpg"},
-            {"type": "image", "path": "/path/to/image3.jpg"},
+            {"type": "image"},
+            {"type": "image"},
             {"type": "text", "text": "What is written in the pictures?"}
         ]
     }
@@ -140,9 +156,9 @@ conversation4 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image3.jpg"},
-            {"type": "image", "path": "/path/to/image4.jpg"},
-            {"type": "video", "path": "/path/to/video.jpg"},
+            {"type": "image"},
+            {"type": "image"},
+            {"type": "video"},
             {"type": "text", "text": "What are the common elements in these medias?"},
         ],
     }
@@ -150,15 +166,15 @@ conversation4 = [
 
 conversations = [conversation1, conversation2, conversation3, conversation4]
 # Preparation for batch inference
-ipnuts = processor.apply_chat_template(
-    conversations,
-    video_fps=1,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
-
+texts = [processor.apply_chat_template(msg, add_generation_prompt=True) for msg in conversations]
+inputs = processor(
+    text=texts,
+    images=images,
+    videos=[video],
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to('cuda')
 
 # Batch Inference
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -264,6 +280,11 @@ model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 
 [[autodoc]] Qwen2_5_VLConfig
 
+## Qwen2_5_VLImageProcessor
+
+[[autodoc]] Qwen2_5_VLImageProcessor
+    - preprocess
+
 ## Qwen2_5_VLProcessor
 
 [[autodoc]] Qwen2_5_VLProcessor

docs/source/en/model_doc/qwen2_vl.md

@@ -39,13 +39,20 @@ The model can accept both images and videos as input. Here's an example code for
 
 ```python
 
+from PIL import Image
+import requests
 import torch
+from torchvision import io
+from typing import Dict
 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
 
 # Load the model in half-precision on the available device(s)
 model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", device_map="auto")
 processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
 
+# Image
+url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
+image = Image.open(requests.get(url, stream=True).raw)
 
 conversation = [
     {
@@ -53,7 +60,6 @@ conversation = [
         "content":[
             {
                 "type":"image",
-                "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
             },
             {
                 "type":"text",
@@ -63,13 +69,13 @@ conversation = [
     }
 ]
 
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
+
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
+
+inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
 
 # Inference: Generation of the output
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -77,28 +83,50 @@ generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(in
 output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
 print(output_text)
 
-
-
 # Video
+def fetch_video(ele: Dict, nframe_factor=2):
+    if isinstance(ele['video'], str):
+        def round_by_factor(number: int, factor: int) -> int:
+            return round(number / factor) * factor
+
+        video = ele["video"]
+        if video.startswith("file://"):
+            video = video[7:]
+
+        video, _, info = io.read_video(
+            video,
+            start_pts=ele.get("video_start", 0.0),
+            end_pts=ele.get("video_end", None),
+            pts_unit="sec",
+            output_format="TCHW",
+        )
+        assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`"
+        if "nframes" in ele:
+            nframes = round_by_factor(ele["nframes"], nframe_factor)
+        else:
+            fps = ele.get("fps", 1.0)
+            nframes = round_by_factor(video.size(0) / info["video_fps"] * fps, nframe_factor)
+        idx = torch.linspace(0, video.size(0) - 1, nframes, dtype=torch.int64)
+        return video[idx]
+
+video_info = {"type": "video", "video": "/path/to/video.mp4", "fps": 1.0}
+video = fetch_video(video_info)
 conversation = [
     {
         "role": "user",
         "content": [
-            {"type": "video", "path": "/path/to/video.mp4"},
+            {"type": "video"},
             {"type": "text", "text": "What happened in the video?"},
         ],
     }
 ]
 
-inputs = processor.apply_chat_template(
-    conversation,
-    video_fps=1,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
+# Preprocess the inputs
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>What happened in the video?<|im_end|>\n<|im_start|>assistant\n'
 
+inputs = processor(text=[text_prompt], videos=[video], padding=True, return_tensors="pt")
+inputs = inputs.to('cuda')
 
 # Inference: Generation of the output
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -112,13 +140,23 @@ print(output_text)
 The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
 
 ```python
+image1 = Image.open("/path/to/image1.jpg")
+image2 = Image.open("/path/to/image2.jpg")
+image3 = Image.open("/path/to/image3.jpg")
+image4 = Image.open("/path/to/image4.jpg")
+image5 = Image.open("/path/to/image5.jpg")
+video = fetch_video({
+    "type": "video",
+    "video": "/path/to/video.mp4",
+    "fps": 1.0
+})
 
 # Conversation for the first image
 conversation1 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image1.jpg"},
+            {"type": "image"},
             {"type": "text", "text": "Describe this image."}
         ]
     }
@@ -129,8 +167,8 @@ conversation2 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image2.jpg"},
-            {"type": "image", "path": "/path/to/image3.jpg"},
+            {"type": "image"},
+            {"type": "image"},
             {"type": "text", "text": "What is written in the pictures?"}
         ]
     }
@@ -150,9 +188,9 @@ conversation4 = [
     {
         "role": "user",
         "content": [
-            {"type": "image", "path": "/path/to/image3.jpg"},
-            {"type": "image", "path": "/path/to/image4.jpg"},
-            {"type": "video", "path": "/path/to/video.jpg"},
+            {"type": "image"},
+            {"type": "image"},
+            {"type": "video"},
             {"type": "text", "text": "What are the common elements in these medias?"},
         ],
     }
@@ -160,15 +198,15 @@ conversation4 = [
 
 conversations = [conversation1, conversation2, conversation3, conversation4]
 # Preparation for batch inference
-ipnuts = processor.apply_chat_template(
-    conversations,
-    video_fps=1,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
-
+texts = [processor.apply_chat_template(msg, add_generation_prompt=True) for msg in conversations]
+inputs = processor(
+    text=texts,
+    images=[image1, image2, image3, image4, image5],
+    videos=[video],
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to('cuda')
 
 # Batch Inference
 output_ids = model.generate(**inputs, max_new_tokens=128)
@@ -198,7 +236,6 @@ processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixel
 ```
 This ensures each image gets encoded using a number between 256-1024 tokens. The 28 comes from the fact that the model uses a patch size of 14 and a temporal patch size of 2 (14 x 2 = 28).
 
-
 #### Multiple Image Inputs
 
 By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:

docs/source/en/model_doc/rt_detr_v2.md

@@ -1,97 +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.
-
-⚠️ 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.
-
--->
-
-# RT-DETRv2
-
-## Overview
-
-The RT-DETRv2 model was proposed in [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://arxiv.org/abs/2407.17140) by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu.
-
-RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance.
-
-The abstract from the paper is the following:
-
-*In this report, we present RT-DETRv2, an improved Real-Time DEtection TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR, and opens up a set of bag-of-freebies for flexibility and practicality, as well as optimizing the training strategy to achieve enhanced performance. To improve the flexibility, we suggest setting a distinct number of sampling points for features at different scales in the deformable attention to achieve selective multi-scale feature extraction by the decoder. To enhance practicality, we propose an optional discrete sampling operator to replace the grid_sample operator that is specific to RT-DETR compared to YOLOs. This removes the deployment constraints typically associated with DETRs. For the training strategy, we propose dynamic data augmentation and scale-adaptive hyperparameters customization to improve performance without loss of speed.*
-
-This model was contributed by [jadechoghari](https://huggingface.co/jadechoghari).
-The original code can be found [here](https://github.com/lyuwenyu/RT-DETR).
-
-## Usage tips 
-
-This second version of RT-DETR improves how the decoder finds objects in an image. 
-
-- **better sampling** – adjusts offsets so the model looks at the right areas
-- **flexible attention** – can use smooth (bilinear) or fixed (discrete) sampling
-- **optimized processing** – improves how attention weights mix information
-
-```py
->>> import torch
->>> import requests
-
->>> from PIL import Image
->>> from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
-
->>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
->>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
-
->>> inputs = image_processor(images=image, return_tensors="pt")
-
->>> with torch.no_grad():
-...     outputs = model(**inputs)
-
->>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)
-
->>> for result in results:
-...     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
-...         score, label = score.item(), label_id.item()
-...         box = [round(i, 2) for i in box.tolist()]
-...         print(f"{model.config.id2label[label]}: {score:.2f} {box}")
-cat: 0.97 [341.14, 25.11, 639.98, 372.89]
-cat: 0.96 [12.78, 56.35, 317.67, 471.34]
-remote: 0.95 [39.96, 73.12, 175.65, 117.44]
-sofa: 0.86 [-0.11, 2.97, 639.89, 473.62]
-sofa: 0.82 [-0.12, 1.78, 639.87, 473.52]
-remote: 0.79 [333.65, 76.38, 370.69, 187.48]
-```
-
-## Resources
-
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RT-DETRv2.
-
-<PipelineTag pipeline="object-detection"/>
-
-- Scripts for finetuning [`RTDetrV2ForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
-- See also: [Object detection task guide](../tasks/object_detection).
-- Notebooks for [inference](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_inference.ipynb) and [fine-tuning](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_finetune_on_a_custom_dataset.ipynb) RT-DETRv2 on a custom dataset (🌎).
-
-
-## RTDetrV2Config
-
-[[autodoc]] RTDetrV2Config
-
-
-## RTDetrV2Model
-
-[[autodoc]] RTDetrV2Model
-    - forward
- 
-## RTDetrV2ForObjectDetection
-
-[[autodoc]] RTDetrV2ForObjectDetection
-    - forward

docs/source/en/model_doc/seamless_m4t.md

@@ -52,7 +52,7 @@ Here is how to use the processor to process text and audio:
 ```python
 >>> # let's load an audio sample from an Arabic speech corpus
 >>> from datasets import load_dataset
->>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True, trust_remote_code=True)
+>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
 >>> audio_sample = next(iter(dataset))["audio"]
 
 >>> # now, process it

docs/source/en/model_doc/seamless_m4t_v2.md

@@ -52,7 +52,7 @@ Here is how to use the processor to process text and audio:
 ```python
 >>> # let's load an audio sample from an Arabic speech corpus
 >>> from datasets import load_dataset
->>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True, trust_remote_code=True)
+>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
 >>> audio_sample = next(iter(dataset))["audio"]
 
 >>> # now, process it

docs/source/en/model_doc/siglip.md

@@ -86,7 +86,7 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 >>> candidate_labels = ["2 cats", "2 dogs"]
 # follows the pipeline prompt template to get same results
 >>> texts = [f'This is a photo of {label}.' for label in candidate_labels]
-# important: we pass `padding=max_length` since the model was trained with this
+>>> # important: we pass `padding=max_length` since the model was trained with this
 >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
 
 >>> with torch.no_grad():
@@ -95,7 +95,7 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 >>> logits_per_image = outputs.logits_per_image
 >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
 >>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
-19.8% that image 0 is '2 cats'
+31.9% that image 0 is 'a photo of 2 cats'
 ```
 
 ## Resources
@@ -142,7 +142,8 @@ To load and run a model using Flash Attention 2, refer to the snippet below:
 # follows the pipeline prompt template to get same results
 >>> texts = [f'This is a photo of {label}.' for label in candidate_labels]
 # important: we pass `padding=max_length` since the model was trained with this
->>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(device)
+>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+>>> inputs.to(device)
 
 >>> with torch.no_grad():
 ...     with torch.autocast(device):
@@ -151,7 +152,7 @@ To load and run a model using Flash Attention 2, refer to the snippet below:
 >>> logits_per_image = outputs.logits_per_image
 >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
 >>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
-19.8% that image 0 is '2 cats'
+51.3% that image 0 is 'This is a photo of 2 cats.'
 ```
 
 
@@ -214,11 +215,6 @@ Below is an expected speedup diagram that compares inference time between the na
 [[autodoc]] SiglipImageProcessor
     - preprocess
 
-## SiglipImageProcessorFast
-
-[[autodoc]] SiglipImageProcessorFast
-    - preprocess
-
 ## SiglipProcessor
 
 [[autodoc]] SiglipProcessor

docs/source/en/model_doc/siglip2.md

@@ -1,276 +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.
-
-⚠️ 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.
-
--->
-
-# SigLIP2
-
-## Overview
-
-The SigLIP2 model was proposed in [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://huggingface.co/papers/2502.14786) by Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin,
-Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier Hénaff, Jeremiah Harmsen,
-Andreas Steiner and Xiaohua Zhai.
-
-The model comes in two variants
-
- 1) FixRes - model works with fixed resolution images (backward compatible with SigLIP v1)
- 2) NaFlex - model works with variable image aspect ratios and resolutions (SigLIP2 in `transformers`)
-
-The abstract from the paper is the following:
-
-*We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success
-of the original SigLIP. In this second iteration, we extend the original image-text training objective with
-several prior, independently developed techniques into a unified recipe—this includes decoder-based
-pretraining, self-supervised losses (self-distillation, masked prediction) and online data curation. With
-these changes, SigLIP 2 models outperform their SigLIP counterparts at all model scales in core capabilities, 
-including zero-shot classification (best SigLIP 2 ViT-g/16 achieves 85.0% ImageNet zero-shot
-accuracy), image-text retrieval, and transfer performance when extracting visual representations for
-Vision-Language Models (VLMs). Furthermore, the new training recipe leads to significant improvements 
-on localization and dense prediction tasks. We also train variants which support multiple resolutions 
-and preserve the input’s native aspect ratio. Finally, we train on a more diverse data-mixture that
-includes de-biasing techniques, leading to much better multilingual understanding and improved fair-
-ness. To provide users with the ability to trade-off inference cost with performance, we release model
-checkpoints at four sizes (ViT-B/86M, L/303M, So400m/400M, and g/1B).*
-
-## Usage tips
-
-- Usage of SigLIP2 is similar to [SigLIP](siglip) and [CLIP](clip). The main difference from CLIP is the training loss, which does not require a global view of all the pairwise similarities of images and texts within a batch. One needs to apply the sigmoid activation function to the logits, rather than the softmax.
-- Training is supported but does not use `torch.distributed` utilities which may limit the scalability of batch size. However, DDP and FDSP works on single-node multi-gpu setup.
-- When using the standalone [`GemmaTokenizerFast`] make sure to pass `padding="max_length"` and `max_length=64` as that's how the model was trained.
-- Model was trained with *lowercased* text, make sure you make the same preprocessing for your text labels.
-- To get the same results as the pipeline, a prompt template of "this is a photo of {label}" should be used.
-- The NaFlex variant supports processing images at higher resolutions by adjusting the `max_num_patches` parameter in the `Processor`. The default value is `max_num_patches=256`. Increasing `max_num_patches` to 1024 (4x) will approximately double processed image height and width, while preserving the aspect ratio.
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip2_metrics_table.png"
-alt="drawing" width="600"/>
-
-This model was contributed by [qubvel](https://huggingface.co/qubvel-hf).
-The original code can be found [here](https://github.com/google-research/big_vision/tree/main).
-
-## Usage example
-
-There are 2 main ways to use SigLIP2: either using the pipeline API, which abstracts away all the complexity for you, or by using the `Siglip2Model` class yourself.
-
-### FixRes variant
-
-**Pipeline API**
-
-The pipeline allows to use the model in a few lines of code:
-
-```python
->>> from transformers import pipeline
->>> from PIL import Image
->>> import requests
-
->>> # load pipe
->>> image_classifier = pipeline(
-...     task="zero-shot-image-classification",
-...     model="google/siglip2-base-patch16-224",
-... )
-
->>> # load image
->>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> # inference
->>> candidate_labels = ["2 cats", "a plane", "a remote"]
->>> outputs = image_classifier(image, candidate_labels=candidate_labels)
->>> outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
->>> print(outputs)
-[{'score': 0.1499, 'label': '2 cats'}, {'score': 0.0008, 'label': 'a remote'}, {'score': 0.0, 'label': 'a plane'}]
-```
-
-**Using the model yourself**
-
-If you want to do the pre- and postprocessing yourself, here's how to do that:
-
-```python
->>> from PIL import Image
->>> import requests
->>> from transformers import AutoProcessor, AutoModel
->>> import torch
-
->>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
->>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
-
->>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> candidate_labels = ["2 cats", "2 dogs"]
-# follows the pipeline prompt template to get same results
->>> texts = [f"This is a photo of {label}." for label in candidate_labels]
-
-# IMPORTANT: we pass `padding=max_length` and `max_length=64` since the model was trained with this
->>> inputs = processor(text=texts, images=image, padding="max_length", max_length=64, return_tensors="pt")
-
->>> with torch.no_grad():
-...     outputs = model(**inputs)
-
->>> logits_per_image = outputs.logits_per_image
->>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
->>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
-15.0% that image 0 is '2 cats'
-```
-
-### NaFlex variant
-
-NaFlex combines ideas from FlexiViT, i.e. supporting multiple, predefined sequence lengths 
-with a single ViT model, and NaViT, namely processing images at their native aspect ratio.
-This enables processing different types of images at appropriate resolution, e.g. using a
-larger resolution to process document images, while at the same time minimizing the impact 
-of aspect ratio distortion on certain inference tasks, e.g. on OCR.
-
-Given a patch size and target sequence length, NaFlex preprocesses the data by first resizing 
-the input image such that the height and width after resizing are multiples of the patch size,
-while 
-    
-    1. keeping the aspect ratio distortion as small as possible
-    2. producing a sequence length of at most the desired target sequence length (`max_num_patches`)
-    
-The resulting distortion in width and height is at most `(patch_size - 1) / width` and
-`(patch_size - 1) / height`, respectively, which tends to be small for common resolutions and aspect ratios. 
-After resizing, the image is split into a sequence of patches, and a mask with padding information is added.
-
-```python
->>> from PIL import Image
->>> import requests
->>> from transformers import AutoProcessor, AutoModel
->>> import torch
-
->>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-naflex")
->>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-naflex")
-
->>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> candidate_labels = ["2 cats", "2 dogs"]
-# follows the pipeline prompt template to get same results
->>> texts = [f"This is a photo of {label}." for label in candidate_labels]
-
-# default value for `max_num_patches` is 256, but you can increase resulted image resolution providing
-# higher values e.g. `max_num_patches=512`
->>> inputs = processor(text=texts, images=image, max_num_patches=256, return_tensors="pt")
-
->>> with torch.no_grad():
-...     outputs = model(**inputs)
-
->>> logits_per_image = outputs.logits_per_image
->>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
->>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
-21.1% that image 0 is '2 cats'
-```
-
-## Resources
-
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP2.
-
-- [Zero-shot image classification task guide](../tasks/zero_shot_image_classification)
-- Demo notebook for SigLIP2 can be found [here](https://github.com/qubvel/transformers-notebooks/tree/master/notebooks/SigLIP2_inference.ipynb). 🌎
-
-If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
-
-
-## Combining SigLIP2 and Flash Attention 2
-
-First, make sure to install the latest version of Flash Attention 2.
-
-```bash
-pip install -U flash-attn --no-build-isolation
-```
-
-Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)
-
-To load and run a model using Flash Attention 2, refer to the snippet below:
-
-```python
->>> import torch
->>> import requests
->>> from PIL import Image
->>> from transformers import AutoProcessor, AutoModel
->>> device = "cuda" # the device to load the model onto
-
->>> model = AutoModel.from_pretrained(
-...     "google/siglip2-so400m-patch14-384",
-...     attn_implementation="flash_attention_2",
-...     torch_dtype=torch.float16,
-...     device_map=device,
-... )
->>> processor = AutoProcessor.from_pretrained("google/siglip2-so400m-patch14-384")
-
->>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
->>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> candidate_labels = ["2 cats", "2 dogs"]
-# follows the pipeline prompt template to get same results
->>> texts = [f'This is a photo of {label}.' for label in candidate_labels]
-# important: we pass `padding=max_length` since the model was trained with this
->>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(device)
-
->>> with torch.no_grad():
-...     with torch.autocast(device):
-...         outputs = model(**inputs)
-
->>> logits_per_image = outputs.logits_per_image
->>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
->>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
-19.8% that image 0 is '2 cats'
-```
-
-## Siglip2Config
-
-[[autodoc]] Siglip2Config
-
-## Siglip2TextConfig
-
-[[autodoc]] Siglip2TextConfig
-
-## Siglip2VisionConfig
-
-[[autodoc]] Siglip2VisionConfig
-
-## Siglip2ImageProcessor
-
-[[autodoc]] Siglip2ImageProcessor
-    - preprocess
-
-## Siglip2ImageProcessorFast
-
-[[autodoc]] Siglip2ImageProcessorFast
-    - preprocess
-
-## Siglip2Processor
-
-[[autodoc]] Siglip2Processor
-
-## Siglip2Model
-
-[[autodoc]] Siglip2Model
-    - forward
-    - get_text_features
-    - get_image_features
-
-## Siglip2TextModel
-
-[[autodoc]] Siglip2TextModel
-    - forward
-
-## Siglip2VisionModel
-
-[[autodoc]] Siglip2VisionModel
-    - forward
-
-## Siglip2ForImageClassification
-
-[[autodoc]] Siglip2ForImageClassification
-    - forward

docs/source/en/model_doc/smolvlm.md

@@ -1,197 +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.
-
-⚠️ 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.
-
--->
-
-# SmolVLM
-
-## Overview
-SmolVLM2 is an adaptation of the Idefics3 model with two main differences:
-
-- It uses SmolLM2 for the text model.
-- It supports multi-image and video inputs
-
-## Usage tips
-
-Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.
-
-Videos should not be upsampled. 
-
-If `do_resize` is set to `True`, the model resizes images so that the longest edge is 4*512 pixels by default.
-The default resizing behavior can be customized by passing a dictionary to the `size` parameter. For example, `{"longest_edge": 4 * 512}` is the default, but you can change it to a different value if needed.
-
-Here’s how to control resizing and set a custom size:
-```python
-image_processor = SmolVLMImageProcessor(do_resize=True, size={"longest_edge": 2 * 512}, max_image_size=512)
-```
-
-Additionally, the `max_image_size` parameter, which controls the size of each square patch the image is decomposed into, is set to 512 by default but can be adjusted as needed. After resizing (if applicable), the image processor decomposes the images into square patches based on the `max_image_size` parameter.
-
-This model was contributed by [orrzohar](https://huggingface.co/orrzohar).
-
-
-
-## Usage example
-
-### Single Media inference
-
-The model can accept both images and videos as input, but you should use only one of the modalities at a time. Here's an example code for that.
-
-```python
-import torch
-from transformers import AutoProcessor, AutoModelForImageTextToText
-
-processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
-model = AutoModelForImageTextToText.from_pretrained(
-    "HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
-    torch_dtype=torch.bfloat16,
-    device_map="cuda"
-)
-
-conversation = [
-    {
-        "role": "user",
-        "content":[
-            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
-            {"type": "text", "text": "Describe this image."}
-        ]
-    }
-]
-
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-).to(model.device, dtype=torch.bfloat16)
-
-output_ids = model.generate(**inputs, max_new_tokens=128)
-generated_texts = processor.batch_decode(output_ids, skip_special_tokens=True)
-print(generated_texts)
-
-
-# Video
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "video", "path": "/path/to/video.mp4"},
-            {"type": "text", "text": "Describe this video in detail"}
-        ]
-    },
-]
-
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-).to(model.device, dtype=torch.bfloat16)
-
-generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
-generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
-print(generated_texts[0])
-```
-
-### Batch Mixed Media Inference
-
-The model can batch inputs composed of several images/videos and text. Here is an example.
-
-```python
-import torch
-from transformers import AutoProcessor, AutoModelForImageTextToText
-
-processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
-model = AutoModelForImageTextToText.from_pretrained(
-    "HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
-    torch_dtype=torch.bfloat16,
-    device_map="cuda"
-)
-
-# Conversation for the first image
-conversation1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "path": "/path/to/image.jpg"},
-            {"type": "text", "text": "Describe this image."}
-        ]
-    }
-]
-
-# Conversation with two images
-conversation2 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image", "path": "/path/to/image.jpg"},
-            {"type": "image", "path": "/path/to/image.jpg"},
-            {"type": "text", "text": "What is written in the pictures?"}
-        ]
-    }
-]
-
-# Conversation with pure text
-conversation3 = [
-    {"role": "user","content": "who are you?"}
-]
-
-
-conversations = [conversation1, conversation2, conversation3]
-inputs = processor.apply_chat_template(
-    conversation,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-).to(model.device, dtype=torch.bfloat16)
-
-generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
-generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
-print(generated_texts[0])
-```
-
-## SmolVLMConfig
-
-[[autodoc]] SmolVLMConfig
-
-## SmolVLMVisionConfig
-
-[[autodoc]] SmolVLMVisionConfig
-
-## Idefics3VisionTransformer
-
-[[autodoc]] SmolVLMVisionTransformer
-
-## SmolVLMModel
-
-[[autodoc]] SmolVLMModel
-    - forward
-
-## SmolVLMForConditionalGeneration
-
-[[autodoc]] SmolVLMForConditionalGeneration
-    - forward
-
-
-## SmolVLMImageProcessor
-[[autodoc]] SmolVLMImageProcessor
-    - preprocess
-
-
-## SmolVLMProcessor
-[[autodoc]] SmolVLMProcessor
-    - __call__

docs/source/en/model_doc/zamba2.md

@@ -1,93 +0,0 @@
-<!--Copyright 2024 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.
-
--->
-# Zamba2
-
-Zamba2 is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights.
-
-This model was contributed by [pglo](https://huggingface.co/pglo).
-
-
-## Model details
-
-Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B are hybrid models combining state-space models (Specifically [Mamba](https://github.com/state-spaces/mamba)) and transformer, and were trained using next-token prediction. Zamba2 uses shared transformer layers after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7B-v0.1). We came to this architecture after a series of ablations at small scales. Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B were pre-trained on 2T and 3T tokens, respectively.
-
-<img src=https://github.com/user-attachments/assets/c2cff209-b901-483c-87aa-774b82a0769f width=30% height=40% />
-
-## Quick start
-
-
-### Presequities
-
-Zamba2 requires you use `transformers` version 4.48.0 or higher:
-```bash
-pip install transformers>=4.48.0
-```
-
-## Inference
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
-model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B", device_map="cuda", torch_dtype=torch.bfloat16)
-
-input_text = "What factors contributed to the fall of the Roman Empire?"
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
-
-outputs = model.generate(**input_ids, max_new_tokens=100)
-print(tokenizer.decode(outputs[0]))
-```
-
-
-## Model card
-
-The model cards can be found at:
-* [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B)
-* [Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
-* [Zamba2-7B](https://huggingface.co/Zyphra/Zamba2-7B)
-
-
-## Issues
-For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/Zyphra/Zamba2-7B/discussions)
-
-
-## License
-
-The model weights are open-sourced via an Apache 2.0 license.
-
-
-## Zamba2Config
-
-[[autodoc]] Zamba2Config
-
-
-## Zamba2Model
-
-[[autodoc]] Zamba2Model
-    - forward
-
-
-## Zamba2ForCausalLM
-
-[[autodoc]] Zamba2ForCausalLM
-    - forward
-
-
-## Zamba2ForSequenceClassification
-
-[[autodoc]] transformers.Zamba2ForSequenceClassification
-    - forward

docs/source/en/model_doc/zoedepth.md

@@ -70,7 +70,7 @@ Alternatively, one can also perform inference using the classes:
 >>> inputs = image_processor(images=image, return_tensors="pt")
 
 >>> with torch.no_grad():   
-...     outputs = model(inputs)
+...     outputs = model(pixel_values)
 
 >>> # interpolate to original size and visualize the prediction
 >>> ## ZoeDepth dynamically pads the input image. Thus we pass the original image size as argument

docs/source/en/perf_infer_gpu_multi.md

@@ -54,16 +54,6 @@ torchrun --nproc-per-node 4 demo.py
 
 PyTorch tensor parallel is currently supported for the following models:
 * [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
-* [Gemma](https://huggingface.co/docs/transformers/en/model_doc/gemma), [Gemma2](https://huggingface.co/docs/transformers/en/model_doc/gemma2)
-* [Granite](https://huggingface.co/docs/transformers/en/model_doc/granite)
-* [Mistral](https://huggingface.co/docs/transformers/en/model_doc/mistral)
-* [Qwen2](https://huggingface.co/docs/transformers/en/model_doc/qwen2), [Qwen2MoE](https://huggingface.co/docs/transformers/en/model_doc/qwen2_moe), [Qwen2-VL](https://huggingface.co/docs/transformers/v4.48.0/en/model_doc/qwen2_vl)
-* [Starcoder2](https://huggingface.co/docs/transformers/en/model_doc/starcoder2)
-* [Cohere](https://huggingface.co/docs/transformers/en/model_doc/cohere), [Cohere2](https://huggingface.co/docs/transformers/en/model_doc/cohere2)
-* [GLM](https://huggingface.co/docs/transformers/en/model_doc/glm)
-* [Mixtral](https://huggingface.co/docs/transformers/en/model_doc/mixtral)
-* [OLMo](https://huggingface.co/docs/transformers/en/model_doc/olmo), [OLMo2](https://huggingface.co/docs/transformers/en/model_doc/olmo2)
-* [Phi](https://huggingface.co/docs/transformers/en/model_doc/phi), [Phi-3](https://huggingface.co/docs/transformers/en/model_doc/phi3)
 
 You can request to add tensor parallel support for another model by opening a GitHub Issue or Pull Request.
 

docs/source/en/perf_infer_gpu_one.md

@@ -52,7 +52,6 @@ FlashAttention-2 is currently supported for the following architectures:
 * [Emu3](https://huggingface.co/docs/transformers/model_doc/emu3)
 * [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
 * [Gemma2](https://huggingface.co/docs/transformers/model_doc/gemma2#transformers.Gemma2Model)
-* [GotOcr2](https://huggingface.co/docs/transformers/model_doc/got_ocr2#transformers.GotOcr2ForConditionalGeneration)
 * [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
 * [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
 * [GPTNeo](https://huggingface.co/docs/transformers/model_doc/gpt_neo#transformers.GPTNeoModel)
@@ -60,7 +59,6 @@ FlashAttention-2 is currently supported for the following architectures:
 * [GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj#transformers.GPTJModel)
 * [Granite](https://huggingface.co/docs/transformers/model_doc/granite#transformers.GraniteModel)
 * [GraniteMoe](https://huggingface.co/docs/transformers/model_doc/granitemoe#transformers.GraniteMoeModel)
-* [GraniteMoeShared](https://huggingface.co/docs/transformers/model_doc/granitemoeshared#transformers.GraniteMoeSharedModel)
 * [Idefics2](https://huggingface.co/docs/transformers/model_doc/idefics2#transformers.Idefics2Model)
 * [Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3#transformers.Idefics3Model)
 * [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
@@ -95,7 +93,6 @@ FlashAttention-2 is currently supported for the following architectures:
 * [PhiMoE](https://huggingface.co/docs/transformers/model_doc/phimoe#transformers.PhimoeModel)
 * [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
 * [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
-* [SmolVLM](https://huggingface.co/docs/transformers/model_doc/smolvlm#transformers.SmolVLMModel)
 * [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
 * [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
 * [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
@@ -111,11 +108,9 @@ FlashAttention-2 is currently supported for the following architectures:
 * [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
 * [Sew](https://huggingface.co/docs/transformers/main/en/model_doc/sew#transformers.SEWModel)
 * [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
-* [SigLIP2](https://huggingface.co/docs/transformers/model_doc/siglip2)
 * [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
 * [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
 * [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
-* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
 
 You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
 
@@ -247,7 +242,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [data2vec_vision](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionModel)
 * [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
 * [DeiT](https://huggingface.co/docs/transformers/model_doc/deit#transformers.DeiTModel)
-* [DepthPro](https://huggingface.co/docs/transformers/model_doc/depth_pro#transformers.DepthProModel)
 * [DiffLlama](https://huggingface.co/docs/transformers/model_doc/diffllama#transformers.DiffLlamaModel)
 * [Dinov2](https://huggingface.co/docs/transformers/en/model_doc/dinov2)
 * [Dinov2_with_registers](https://huggingface.co/docs/transformers/en/model_doc/dinov2)
@@ -258,7 +252,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
 * [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
 * [Gemma2](https://huggingface.co/docs/transformers/model_doc/gemma2#transformers.Gemma2Model)
-* [GotOcr2](https://huggingface.co/docs/transformers/model_doc/got_ocr2#transformers.GotOcr2ForConditionalGeneration)
 * [Granite](https://huggingface.co/docs/transformers/model_doc/granite#transformers.GraniteModel)
 * [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
 * [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
@@ -269,7 +262,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3#transformers.Idefics3Model)
 * [I-JEPA](https://huggingface.co/docs/transformers/model_doc/ijepa#transformers.IJepaModel)
 * [GraniteMoe](https://huggingface.co/docs/transformers/model_doc/granitemoe#transformers.GraniteMoeModel)
-* [GraniteMoeShared](https://huggingface.co/docs/transformers/model_doc/granitemoeshared#transformers.GraniteMoeSharedModel)
 * [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
 * [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
 * [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
@@ -303,7 +295,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
 * [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
 * [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
-* [SmolVLM](https://huggingface.co/docs/transformers/model_doc/smolvlm#transformers.SmolVLMModel)
 * [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
 * [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
 * [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
@@ -311,7 +302,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaModel)
 * [Sew](https://huggingface.co/docs/transformers/main/en/model_doc/sew#transformers.SEWModel)
 * [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
-* [SigLIP2](https://huggingface.co/docs/transformers/model_doc/siglip2)
 * [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
 * [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
 * [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
@@ -338,7 +328,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel)
 * [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos#transformers.YolosModel)
 * [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
-* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
 
 <Tip>
 
@@ -364,7 +353,7 @@ tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
 model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")
 
 input_text = "Hello my dog is cute and"
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
 
 + with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
     outputs = model.generate(**inputs)
@@ -438,14 +427,14 @@ To load a model in 4-bit for inference, use the `load_in_4bit` parameter. The `d
 ```py
 from transformers import AutoModelForCausalLM
 
-model_name = "bigscience/bloom-1b7"
+model_name = "bigscience/bloom-2b5"
 model_4bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True)
 ```
 
-To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:
+To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU:
 
 ```py
-max_memory_mapping = {0: "2GB", 1: "5GB"}
+max_memory_mapping = {0: "600MB", 1: "1GB"}
 model_name = "bigscience/bloom-3b"
 model_4bit = AutoModelForCausalLM.from_pretrained(
     model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
@@ -465,7 +454,7 @@ To load a model in 8-bit for inference, use the `load_in_8bit` parameter. The `d
 ```py
 from transformers import AutoModelForCausalLM, BitsAndBytesConfig
 
-model_name = "bigscience/bloom-1b7"
+model_name = "bigscience/bloom-2b5"
 model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))
 ```
 
@@ -474,20 +463,20 @@ If you're loading a model in 8-bit for text generation, you should use the [`~tr
 ```py
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 
-model_name = "bigscience/bloom-1b7"
+model_name = "bigscience/bloom-2b5"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))
 
 prompt = "Hello, my llama is cute"
-inputs = tokenizer(prompt, return_tensors="pt").to(model_8bit.device)
-generated_ids = model_8bit.generate(**inputs)
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+generated_ids = model.generate(**inputs)
 outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
 ```
 
-To load a model in 8-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:
+To load a model in 8-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 1GB of memory to the first GPU and 2GB of memory to the second GPU:
 
 ```py
-max_memory_mapping = {0: "2GB", 1: "5GB"}
+max_memory_mapping = {0: "1GB", 1: "2GB"}
 model_name = "bigscience/bloom-3b"
 model_8bit = AutoModelForCausalLM.from_pretrained(
     model_name, torch_dtype="auto", device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
@@ -552,8 +541,11 @@ quantization_config = BitsAndBytesConfig(
 tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
 model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype="auto", quantization_config=quantization_config)
 
+# enable BetterTransformer
+model = model.to_bettertransformer()
+
 input_text = "Hello my dog is cute and"
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
 
 # enable FlashAttention
 with sdpa_kernel(SDPBackend.FLASH_ATTENTION):

docs/source/en/perf_train_gpu_many.md

@@ -450,13 +450,12 @@ Implementations:
 - [parallelformers](https://github.com/tunib-ai/parallelformers) (only inference at the moment)
 - [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
 - [OSLO](https://github.com/tunib-ai/oslo) has the tensor parallelism implementation based on the Transformers.
-- [`transformers` integration](main_classes/trainer) tensor parallelism is available through tp_size attribute for models having `base_tp_plan`. Further you can look at [example usage](perf_infer_gpu_multi)
 
 SageMaker combines TP with DP for a more efficient processing.
 
 🤗 Transformers status:
-- core: uses PyTorch 2 APIs to support tensor parallelism to models having base_tp_plan in their respective config classes.
-- Alternatively, you can as well try [parallelformers](https://github.com/tunib-ai/parallelformers) that provides this support for most of our models. Training mode with TP is as well supported natively in transformers.
+- core: not yet implemented in the core
+- but if you want inference [parallelformers](https://github.com/tunib-ai/parallelformers) provides this support for most of our models. So until this is implemented in the core you can use theirs. And hopefully training mode will be supported too.
 - Deepspeed-Inference also supports our BERT, GPT-2, and GPT-Neo models in their super-fast CUDA-kernel-based inference mode, see more [here](https://www.deepspeed.ai/tutorials/inference-tutorial/)
 
 🤗 Accelerate integrates with [TP from Megatron-LM](https://huggingface.co/docs/accelerate/v0.23.0/en/usage_guides/megatron_lm).
@@ -477,7 +476,7 @@ And GPU1 does the same by enlisting GPU3 to its aid.
 Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
 
 Implementations:
-- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed)
+- [DeepSpeed](https://github.com/microsoft/DeepSpeed)
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
 - [Varuna](https://github.com/microsoft/varuna)
 - [SageMaker](https://arxiv.org/abs/2111.05972)
@@ -498,7 +497,7 @@ This diagram is from a blog post [3D parallelism: Scaling to trillion-parameter
 Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
 
 Implementations:
-- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
+- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
 - [Varuna](https://github.com/microsoft/varuna)
 - [SageMaker](https://arxiv.org/abs/2111.05972)
@@ -536,7 +535,7 @@ Important papers:
 - [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](
 https://arxiv.org/abs/2201.11990)
 
-🤗 Transformers status: not yet implemented, since we have no PP.
+🤗 Transformers status: not yet implemented, since we have no PP and TP.
 
 ## FlexFlow
 

docs/source/en/perf_train_gpu_one.md

@@ -298,7 +298,8 @@ from transformers.trainer_pt_utils import get_parameter_names
 
 training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
 
-decay_parameters = get_parameter_names(model, [nn.LayerNorm], ["bias", "layernorm", "rmsnorm"])
+decay_parameters = get_parameter_names(model, [nn.LayerNorm])
+decay_parameters = [name for name in decay_parameters if "bias" not in name]
 optimizer_grouped_parameters = [
     {
         "params": [p for n, p in model.named_parameters() if n in decay_parameters],

docs/source/en/quantization/awq.md

@@ -31,8 +31,6 @@ Make sure you have autoawq installed:
 ```bash
 pip install autoawq
 ```
-> [!WARNING]
-> AutoAWQ downgrades Transformers to version 4.47.1. If you want to do inference with AutoAWQ, you may need to reinstall your Transformers' version after installing AutoAWQ.
 
 AWQ-quantized models can be identified by checking the `quantization_config` attribute in the model's [config.json](https://huggingface.co/TheBloke/zephyr-7B-alpha-AWQ/blob/main/config.json) file:
 
@@ -61,14 +59,13 @@ A quantized model is loaded with the [`~PreTrainedModel.from_pretrained`] method
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_id = "TheBloke/zephyr-7B-alpha-AWQ"
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0")
 ```
 
 Loading an AWQ-quantized model automatically sets other weights to fp16 by default for performance reasons. If you want to load these other weights in a different format, use the `torch_dtype` parameter:
 
 ```py
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 
 model_id = "TheBloke/zephyr-7B-alpha-AWQ"
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
@@ -178,7 +175,7 @@ quantization_config = AwqConfig(
     }
 )
 
-model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config, trust_remote_code=True).to(0)
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config).to(0)
 ```
 
 The parameter `modules_to_fuse` should include:
@@ -235,12 +232,12 @@ Note this feature is supported on AMD GPUs.
 </Tip>
 
 
-## Intel CPU/GPU support
+## CPU support
 
-Recent versions of autoawq supports Intel CPU/GPU with IPEX op optimizations. To get started, install the latest version of autoawq.
+Recent versions of `autoawq` supports CPU with ipex op optimizations. To get started, first install the latest version of `autoawq` by running:
 
 ```bash
-pip install intel-extension-for-pytorch # for IPEX-GPU refer to https://intel.github.io/intel-extension-for-pytorch/xpu/2.5.10+xpu/ 
+pip install intel-extension-for-pytorch
 pip install git+https://github.com/casper-hansen/AutoAWQ.git
 ```
 
@@ -250,21 +247,20 @@ Get started by passing an `AwqConfig()` with `version="ipex"`.
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
 
-device = "cpu" # set to "xpu" for Intel GPU
 quantization_config = AwqConfig(version="ipex")
 
 model = AutoModelForCausalLM.from_pretrained(
     "TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ",
     quantization_config=quantization_config,
-    device_map=device,
+    device_map="cpu",
 )
 
-input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device=device)
+input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cpu")
 output = model(input_ids)
 print(output.logits)
 
 tokenizer = AutoTokenizer.from_pretrained("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ")
-input_ids = tokenizer.encode("How to make a cake", return_tensors="pt").to(device)
+input_ids = tokenizer.encode("How to make a cake", return_tensors="pt")
 pad_token_id = tokenizer.eos_token_id
 output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=pad_token_id)
 print(tokenizer.decode(output[0], skip_special_tokens=True))
@@ -272,6 +268,6 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 
 <Tip warning={true}>
 
-This feature is supported on Intel CPUs/GPUs.
+Note this feature is supported on Intel CPUs.
 
-</Tip>
+</Tip>