add other models

* Create push-important-models.yml

* feat: add falcon-h1

* fixup

* address comment

* fix

* fix copies

* fix copies

* fix

* fix

* fix

* fix

* fix copies

* fix

* fix copies

* fix test import to at least trigget the cis

* yups

* update

* fix make fix copies

* fix inits?

* fix style

* skip annoying test

* add integration test for Falcon H1

* fix copies

* fix

---------

Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: dhia.rhaiem <dhia.rhaiem@tii.ae>

.circleci/config.yml

@@ -7,6 +7,18 @@ parameters:
     nightly:
         type: boolean
         default: false
+    GHA_Actor:
+        type: string
+        default: ""
+    GHA_Action:
+        type: string
+        default: ""
+    GHA_Event:
+        type: string
+        default: ""
+    GHA_Meta:
+        type: string
+        default: ""
 
 jobs:
     # Ensure running with CircleCI/huggingface
@@ -31,8 +43,10 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: if [[ "$CIRCLE_PULL_REQUEST" == "" && "$CIRCLE_BRANCH" != "main" && "$CIRCLE_BRANCH" != *-release ]]; then echo "Not a PR, not the main branch and not a release branch, skip test!"; circleci-agent step halt; fi
-            - run: 'curl -L -H "Accept: application/vnd.github+json" -H "X-GitHub-Api-Version: 2022-11-28" https://api.github.com/repos/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pulls/${CIRCLE_PULL_REQUEST##*/} >> github.txt'
+            - run: python3 utils/extract_pr_number_from_circleci.py > pr_number.txt
+            - run: echo $(cat pr_number.txt)
+            - run: if [[ "$(cat pr_number.txt)" == "" && "$CIRCLE_BRANCH" != "main" && "$CIRCLE_BRANCH" != *-release ]]; then echo "Not a PR, not the main branch and not a release 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/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pulls/$(cat pr_number.txt) >> github.txt'
             - run: cat github.txt
             - run: (python3 -c 'import json; from datetime import datetime; fp = open("github.txt"); data = json.load(fp); fp.close(); f = "%Y-%m-%dT%H:%M:%SZ"; created = datetime.strptime(data["created_at"], f); updated = datetime.strptime(data["updated_at"], f); s = (updated - created).total_seconds(); print(int(s))' || true) > elapsed.txt
             - run: if [ "$(cat elapsed.txt)" == "" ]; then echo 60 > elapsed.txt; fi

.circleci/create_circleci_config.py

@@ -28,6 +28,8 @@ COMMON_ENV_VARIABLES = {
     "TRANSFORMERS_IS_CI": True,
     "PYTEST_TIMEOUT": 120,
     "RUN_PIPELINE_TESTS": False,
+    # will be adjust in `CircleCIJob.to_dict`.
+    "RUN_FLAKY": True,
 }
 # Disable the use of {"s": None} as the output is way too long, causing the navigation on CircleCI impractical
 COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "vvv": None, "rsfE":None}
@@ -108,6 +110,7 @@ class CircleCIJob:
             print(f"Using {self.docker_image} docker image")
         if self.install_steps is None:
             self.install_steps = ["uv venv && uv pip install ."]
+        self.install_steps.append("uv venv && uv pip install git+https://github.com/ydshieh/pytest.git@8.3.5-ydshieh git+https://github.com/ydshieh/pluggy.git@1.5.0-ydshieh")
         if self.pytest_options is None:
             self.pytest_options = {}
         if isinstance(self.tests_to_run, str):
@@ -126,6 +129,8 @@ class CircleCIJob:
 
     def to_dict(self):
         env = COMMON_ENV_VARIABLES.copy()
+        # Do not run tests decorated by @is_flaky on pull requests
+        env['RUN_FLAKY'] = os.environ.get("CIRCLE_PULL_REQUEST", "") == ""
         env.update(self.additional_env)
 
         job = {
@@ -393,7 +398,12 @@ def create_circleci_config(folder=None):
         "parameters": {
             # Only used to accept the parameters from the trigger
             "nightly": {"type": "boolean", "default": False},
-            "tests_to_run": {"type": "string", "default": ''},
+            # Only used to accept the parameters from GitHub Actions trigger
+            "GHA_Actor": {"type": "string", "default": ""},
+            "GHA_Action": {"type": "string", "default": ""},
+            "GHA_Event": {"type": "string", "default": ""},
+            "GHA_Meta": {"type": "string", "default": ""},
+            "tests_to_run": {"type": "string", "default": ""},
             **{j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs},
             **{j.job_name + "_parallelism":{"type":"integer", "default":1} for j in jobs},
         },

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
       placeholder: transformers version, platform, python version, ...
     validations:
       required: true
@@ -56,6 +56,12 @@ body:
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @SunMarc
           - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
+        
+        Devices/Backends:
+        
+          - AMD ROCm: @ivarflakstad
+          - Intel XPU: @IlyasMoutawwakil
+          - Ascend NPU: @ivarflakstad 
 
         Documentation: @stevhliu
 

.github/ISSUE_TEMPLATE/migration.yml

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

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

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

.github/workflows/build_pr_documentation.yml

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

.github/workflows/change_pr_to_draft.yml

@@ -1,25 +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
-    permissions:
-      pull-requests: write
-      contents: write
-    if: github.event.pull_request.draft == false
-    steps:
-      - name: Convert PR to draft
-        shell: bash
-        env:
-          PR_NUMBER: ${{ github.event.number }}
-          GH_TOKEN: ${{ secrets.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. The CI will be paused while the PR is in draft mode. When it is ready for review, please click the \`Ready for review\` button (at the bottom of the PR page). This will assign reviewers and trigger CI."

.github/workflows/check_failed_model_tests.yml

@@ -29,7 +29,7 @@ jobs:
   run_models_gpu:
     name: " "
     runs-on:
-      group: aws-g4dn-2xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -39,55 +39,100 @@ jobs:
           name: ci_results_run_models_gpu
           path: /transformers/ci_results_run_models_gpu
 
+      - name: Check file
+        working-directory: /transformers
+        run: |
+          if [ -f ci_results_run_models_gpu/new_model_failures.json ]; then
+            echo "`ci_results_run_models_gpu/new_model_failures.json` exists, continue ..."
+            echo "process=true" >> $GITHUB_ENV
+          else
+            echo "`ci_results_run_models_gpu/new_model_failures.json` doesn't exist, abort."
+            echo "process=false" >> $GITHUB_ENV
+          fi
+
+      - uses: actions/download-artifact@v4
+        if: ${{ env.process == 'true' }}
+        with:
+          pattern: setup_values*
+          path: setup_values
+          merge-multiple: true
+
+      - name: Prepare some setup values
+        if: ${{ env.process == 'true' }}
+        run: |
+          if [ -f setup_values/prev_workflow_run_id.txt ]; then
+            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            echo "PREV_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
+          fi
+
+          if [ -f setup_values/other_workflow_run_id.txt ]; then
+            echo "OTHER_WORKFLOW_RUN_ID=$(cat setup_values/other_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            echo "OTHER_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
+          fi
+
       - name: Update clone
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: git fetch && git checkout ${{ github.sha }}
 
       - name: Get target commit
         working-directory: /transformers/utils
+        if: ${{ env.process == 'true' }}
         run: |
-          echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"]); print(commit)')" >> $GITHUB_ENV
+          echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"], workflow_run_id=os.environ["PREV_WORKFLOW_RUN_ID"]); print(commit)')" >> $GITHUB_ENV
 
       - name: Checkout to `start_sha`
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: git fetch && git checkout ${{ inputs.start_sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
 
       - name: NVIDIA-SMI
+        if: ${{ env.process == 'true' }}
         run: |
           nvidia-smi
 
       - name: Environment
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: |
           python3 utils/print_env.py
 
       - name: Show installed libraries and their versions
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: pip freeze
 
       - name: Check failed tests
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_run_models_gpu/new_model_failures.json --output_file new_model_failures_with_bad_commit.json
 
       - name: Show results
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: |
           ls -l new_model_failures_with_bad_commit.json
           cat new_model_failures_with_bad_commit.json
 
       - name: Checkout back
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: |
           git checkout ${{ inputs.start_sha }}
 
       - name: Process report
         shell: bash
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         env:
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
         run: |
           python3 utils/process_bad_commit_report.py
@@ -95,7 +140,9 @@ jobs:
       - name: Process report
         shell: bash
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         env:
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
         run: |
           {
@@ -105,7 +152,7 @@ jobs:
           } >> "$GITHUB_ENV"
 
       - name: Send processed report
-        if: ${{ !endsWith(env.REPORT_TEXT, '{}') }}
+        if: ${{ env.process == 'true' && !endsWith(env.REPORT_TEXT, '{}') }}
         uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
         with:
           # Slack channel id, channel name, or user id to post message.

.github/workflows/doctest_job.yml

@@ -28,7 +28,7 @@ jobs:
       matrix:
         split_keys: ${{ fromJson(inputs.split_keys) }}
     runs-on: 
-      group: aws-g4dn-2xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

.github/workflows/doctests.yml

@@ -15,7 +15,7 @@ jobs:
   setup:
     name: Setup
     runs-on: 
-      group: aws-g4dn-2xlarge-cache
+      group: aws-g4dn-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

.github/workflows/model_jobs.yml

@@ -107,7 +107,7 @@ jobs:
         run: |
           echo "${{ inputs.machine_type }}"
 
-          if [ "${{ inputs.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ inputs.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ inputs.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu

.github/workflows/new_model_pr_merged_notification.yml

@@ -59,7 +59,7 @@ jobs:
                   "type": "section",
                   "text": {
                     "type": "mrkdwn",
-                    "text": "<https://github.com/huggingface/transformers/commit/${{ env.COMMIT_SHA }}|New model: ${{ env.NEW_MODEL }}> GH_ArthurZucker, GH_lysandrejik, GH_ydshieh"
+                    "text": "<https://github.com/huggingface/transformers/commit/${{ env.COMMIT_SHA }}|New model: ${{ env.NEW_MODEL }}> GH_ArthurZucker, GH_lysandrejik, GH_ydshieh\ncommit SHA: ${{ env.COMMIT_SHA }}"
                   }
                 }
               ]

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

@@ -0,0 +1,19 @@
+# To run this bot, comment "@bot /style" on a PR
+name: Style Bot
+
+on:
+  issue_comment:
+    types: [created]
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  style:
+    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
+    with:
+      python_quality_dependencies: "[quality]"
+      style_command_type: "default"
+    secrets:
+      bot_token: ${{ secrets.GITHUB_TOKEN }}

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

@@ -29,7 +29,7 @@ jobs:
     runs-on: ubuntu-22.04
     name: Get PR number
     # For security: only allow team members to run
-    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
+    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada"]'), 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:
@@ -145,7 +145,7 @@ 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 }}"
+          BODY: "\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}"
         run: |
           gh api \
             --method POST \
@@ -185,7 +185,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
        group: '${{ matrix.machine_type }}'
     container:
@@ -239,7 +239,7 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
@@ -292,7 +292,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -338,7 +338,7 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu

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

@@ -8,8 +8,43 @@ on:
   push:
     branches:
       - run_scheduled_ci*
+  workflow_dispatch:
+    inputs:
+      prev_workflow_run_id:
+        description: 'previous workflow run id to compare'
+        type: string
+        required: false
+        default: ""
+      other_workflow_run_id:
+        description: 'other workflow run id to compare'
+        type: string
+        required: false
+        default: ""
+
+
+# Used for `push` to easily modiffy the target workflow runs to compare against
+env:
+    prev_workflow_run_id: ""
+    other_workflow_run_id: ""
+
 
 jobs:
+  setup:
+    name: Setup
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Setup
+        run: |
+          mkdir "setup_values"
+          echo "${{ inputs.prev_workflow_run_id || env.prev_workflow_run_id }}" > "setup_values/prev_workflow_run_id.txt"
+          echo "${{ inputs.other_workflow_run_id || env.other_workflow_run_id }}" > "setup_values/other_workflow_run_id.txt"
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: setup_values
+          path: setup_values
+
   model-ci:
     name: Model CI
     uses: ./.github/workflows/self-scheduled.yml

.github/workflows/self-scheduled.yml

@@ -49,7 +49,7 @@ jobs:
     name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -107,7 +107,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
         slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
     uses: ./.github/workflows/model_jobs.yml
     with:
@@ -125,7 +125,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
         slice_id: [0, 1]
     uses: ./.github/workflows/model_jobs.yml
     with:
@@ -143,7 +143,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -177,7 +177,7 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
@@ -211,7 +211,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -246,7 +246,7 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
@@ -280,7 +280,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -314,7 +314,7 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
@@ -349,7 +349,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -411,7 +411,7 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu
@@ -448,7 +448,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -491,7 +491,7 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
             machine_type=single-gpu
           elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
             machine_type=multi-gpu

.github/workflows/slack-report.yml

@@ -39,6 +39,21 @@ jobs:
 
       - uses: actions/checkout@v4
       - uses: actions/download-artifact@v4
+
+      - name: Prepare some setup values
+        run: |
+          if [ -f setup_values/prev_workflow_run_id.txt ]; then
+            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            echo "PREV_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
+          fi
+
+          if [ -f setup_values/other_workflow_run_id.txt ]; then
+            echo "OTHER_WORKFLOW_RUN_ID=$(cat setup_values/other_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            echo "OTHER_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
+          fi
+
       - name: Send message to Slack
         if: ${{ inputs.job != 'run_quantization_torch_gpu' }}
         env:
@@ -50,7 +65,6 @@ jobs:
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           CI_EVENT: ${{ inputs.ci_event }}
           CI_SHA: ${{ github.sha }}
-          CI_WORKFLOW_REF: ${{ github.workflow_ref }}
           CI_TEST_JOB: ${{ inputs.job }}
           SETUP_STATUS: ${{ inputs.setup_status }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
@@ -58,7 +72,6 @@ jobs:
         # For a job that doesn't depend on (i.e. `needs`) `setup`, the value for `inputs.folder_slices` would be an
         # empty string, and the called script still get one argument (which is the emtpy string).
         run: |
-          sudo apt-get install -y curl
           pip install huggingface_hub
           pip install slack_sdk
           pip show slack_sdk
@@ -86,7 +99,6 @@ jobs:
         # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change
         # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`.
         run: |
-          sudo apt-get install -y curl
           pip install huggingface_hub
           pip install slack_sdk
           pip show slack_sdk

.github/workflows/ssh-runner.yml

@@ -35,7 +35,7 @@ jobs:
         shell: bash
         run: |
           if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
-            echo "RUNNER=aws-g4dn-2xlarge-cache" >> $GITHUB_ENV
+            echo "RUNNER=aws-g4dn-4xlarge-cache" >> $GITHUB_ENV
           elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
             echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV
           elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then

CONTRIBUTING.md

@@ -78,7 +78,7 @@ Once you've confirmed the bug hasn't already been reported, please include the f
 To get the OS and software versions automatically, run the following command:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 You can also run the same command from the root of the repository:

ISSUES.md

@@ -26,7 +26,7 @@ There are two main venues to receive support: [the forums](https://discuss.huggi
 
 [The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed.
 
-If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
+If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystallized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
 
 In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions:
 

Makefile

@@ -79,7 +79,7 @@ fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
 
 fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
-	python utils/check_modular_conversion.py  --fix_and_overwrite
+	python utils/check_modular_conversion.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
 	python utils/check_doctest_list.py --fix_and_overwrite
 	python utils/check_docstrings.py --fix_and_overwrite

README.md

@@ -78,7 +78,6 @@ Create and activate a virtual environment with [venv](https://docs.python.org/3/
 # venv
 python -m venv .my-env
 source .my-env/bin/activate
-
 # uv
 uv venv .my-env
 source .my-env/bin/activate
@@ -88,10 +87,10 @@ Install Transformers in your virtual environment.
 
 ```py
 # pip
-pip install transformers
+pip install "transformers[torch]"
 
 # uv
-uv pip install transformers
+uv pip install "transformers[torch]"
 ```
 
 Install Transformers from source if you want the latest changes in the library or are interested in contributing. However, the *latest* version may not be stable. Feel free to open an [issue](https://github.com/huggingface/transformers/issues) if you encounter an error.
@@ -99,7 +98,12 @@ Install Transformers from source if you want the latest changes in the library o
 ```shell
 git clone https://github.com/huggingface/transformers.git
 cd transformers
-pip install .
+
+# pip
+pip install .[torch]
+
+# uv
+uv pip install .[torch]
 ```
 
 ## Quickstart
@@ -121,7 +125,7 @@ To chat with a model, the usage pattern is the same. The only difference is you
 > [!TIP]
 > You can also chat with a model directly from the command line.
 > ```shell
-> transformers-cli chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
+> transformers chat Qwen/Qwen2.5-0.5B-Instruct
 > ```
 
 ```py

benchmark/benchmark.py

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

benchmark/llama.py

@@ -293,7 +293,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
                 max_cache_len=seq_length + 128,
             )
 
-            # 3nd call
+            # 3rd call
             start = perf_counter()
             output = model.generate(**inputs, past_key_values=past_key_values)
             end = perf_counter()

docker/consistency.dockerfile

@@ -5,7 +5,7 @@ ARG REF=main
 RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython
-RUN uv pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
 # tensorflow pin matching setup.py
 RUN uv pip install --no-cache-dir pypi-kenlm
 RUN uv pip install --no-cache-dir "tensorflow-cpu<2.16" "tf-keras<2.16"

docker/custom-tokenizers.dockerfile

@@ -16,7 +16,7 @@ RUN cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local
 RUN make install -j 10
 
 
-RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache --upgrade 'torch==2.6.0' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
 # spacy is not used so not tested. Causes to failures. TODO fix later

docker/examples-torch.dockerfile

@@ -5,7 +5,7 @@ USER root
 RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
 RUN uv pip uninstall transformers

docker/exotic-models.dockerfile

@@ -5,7 +5,7 @@ USER root
 RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv &&  uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps timm accelerate
 RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
 # RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels

docker/pipeline-torch.dockerfile

@@ -5,7 +5,7 @@ USER root
 RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' '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 --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
 RUN uv pip uninstall transformers

docker/torch-light.dockerfile

@@ -5,7 +5,7 @@ USER root
 RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words,video]"
 RUN uv pip uninstall transformers

docker/torch-tf-light.dockerfile

@@ -7,7 +7,7 @@ RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-de
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
 RUN git lfs install
 
 RUN uv pip install --no-cache-dir pypi-kenlm

docker/transformers-all-latest-gpu/Dockerfile

@@ -71,6 +71,9 @@ RUN python3 -m pip install --no-cache-dir g2p-en
 # For Some bitsandbytes tests
 RUN python3 -m pip install --no-cache-dir einops
 
+# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
+RUN python3 -m pip uninstall -y kernels
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

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

@@ -45,6 +45,9 @@ RUN python3 -m pip uninstall -y deepspeed
 # TODO: Find out why test fail.
 RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
+# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
+RUN python3 -m pip uninstall -y kernels
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

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

@@ -57,6 +57,9 @@ RUN python3 -m pip uninstall -y deepspeed
 #RUN git clone https://github.com/pytorch/TensorRT.git
 #RUN cd TensorRT/py && python3 setup.py install --fx-only
 
+# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
+RUN python3 -m pip uninstall -y kernels
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-gpu/Dockerfile

@@ -28,6 +28,9 @@ RUN python3 -m pip uninstall -y tensorflow flax
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
+# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
+RUN python3 -m pip uninstall -y kernels
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -84,9 +84,15 @@ RUN python3 -m pip install --no-cache-dir compressed-tensors
 # Add AMD Quark for quantization testing
 RUN python3 -m pip install --no-cache-dir amd-quark
 
+# Add AutoRound for quantization testing
+RUN python3 -m pip install --no-cache-dir "auto-round>=0.5.0"
+
 # Add transformers in editable mode
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
 
+# `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
+RUN python3 -m pip uninstall -y kernels
+
 # 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/de/add_new_model.md

@@ -95,7 +95,7 @@ wie der Code geschrieben werden sollte :-)
 1. Der Vorwärtsdurchlauf Ihres Modells sollte vollständig in die Modellierungsdatei geschrieben werden und dabei völlig unabhängig von anderen
    Modellen in der Bibliothek. Wenn Sie einen Block aus einem anderen Modell wiederverwenden möchten, kopieren Sie den Code und fügen ihn mit einem
    `# Kopiert von` ein (siehe [hier](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)
-   für ein gutes Beispiel und [hier](pr_checks#check-copies) für weitere Dokumentation zu Copied from). 
+   für ein gutes Beispiel und [hier](pr_checks#check-copies) für weitere Dokumentation zu Copied from).
 2. Der Code sollte vollständig verständlich sein, auch für einen Nicht-Muttersprachler. Das heißt, Sie sollten
    beschreibende Variablennamen wählen und Abkürzungen vermeiden. Ein Beispiel: `activation` ist `act` vorzuziehen.
    Von Variablennamen mit nur einem Buchstaben wird dringend abgeraten, es sei denn, es handelt sich um einen Index in einer for-Schleife.
@@ -402,7 +402,7 @@ Andernfalls beginnen wir mit der Erstellung eines neuen Modells. Wir empfehlen d
 ein bestehendes Modell:
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 Sie werden mit einem Fragebogen aufgefordert, die grundlegenden Informationen Ihres Modells einzugeben.

docs/source/de/contributing.md

@@ -63,7 +63,7 @@ Wenn Sie sich vergewissert haben, dass der Fehler noch nicht gemeldet wurde, geb
 Um das Betriebssystem und die Softwareversionen automatisch auszugeben, führen Sie den folgenden Befehl aus:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 Sie können denselben Befehl auch im Hauptverzeichnis des Repositorys ausführen:

docs/source/en/_toctree.yml

@@ -21,6 +21,8 @@
       title: Adding a new model to Transformers
     - local: modular_transformers
       title: Modular Transformers
+    - local: auto_docstring
+      title: Document your models
     - local: task_summary
       title: What 🤗 Transformers can do
     - local: tasks_explained
@@ -37,6 +39,8 @@
       title: Tokenizers
     - local: image_processors
       title: Image processors
+    - local: video_processors
+      title: Video processors
     - local: backbones
       title: Backbones
     - local: feature_extractors
@@ -149,6 +153,8 @@
       title: TPU
     - local: perf_train_special
       title: Apple Silicon
+    - local: perf_train_gaudi
+      title: Intel Gaudi
     - local: perf_hardware
       title: Build your own machine
     title: Hardware
@@ -163,8 +169,12 @@
     title: Overview
   - local: quantization/selecting
     title: Selecting a quantization method
+  - local: quantization/concept_guide
+    title: Quantization concepts
   - local: quantization/aqlm
     title: AQLM
+  - local: quantization/auto_round
+    title: AutoRound
   - local: quantization/awq
     title: AWQ
   - local: quantization/bitnet
@@ -281,6 +291,8 @@
         title: Image-text-to-text
       - local: tasks/video_text_to_text
         title: Video-text-to-text
+      - local: tasks/visual_document_retrieval
+        title: Visual Document Retrieval
       title: Multimodal
     title: Task recipes
   - local: run_scripts
@@ -352,7 +364,9 @@
       title: Feature Extractor
     - local: main_classes/image_processor
       title: Image Processor
-    title: Main classes
+    - local: main_classes/video_processor
+      title: Video Processor
+    title: Main Classes
   - sections:
     - sections:
       - local: model_doc/albert
@@ -379,6 +393,8 @@
         title: BigBirdPegasus
       - local: model_doc/biogpt
         title: BioGpt
+      - local: model_doc/bitnet
+        title: BitNet
       - local: model_doc/blenderbot
         title: Blenderbot
       - local: model_doc/blenderbot-small
@@ -439,6 +455,8 @@
         title: Falcon
       - local: model_doc/falcon3
         title: Falcon3
+      - local: model_doc/falcon_h1
+        title: FalconH1
       - local: model_doc/falcon_mamba
         title: FalconMamba
       - local: model_doc/flan-t5
@@ -485,14 +503,16 @@
         title: Granite
       - local: model_doc/granitemoe
         title: GraniteMoe
+      - local: model_doc/granitemoehybrid
+        title: GraniteMoeHybrid
       - local: model_doc/granitemoeshared
         title: GraniteMoeShared
-      - local: model_doc/granitevision
-        title: GraniteVision
       - local: model_doc/helium
         title: Helium
       - local: model_doc/herbert
         title: HerBERT
+      - local: model_doc/hgnet_v2
+        title: HGNet-V2
       - local: model_doc/ibert
         title: I-BERT
       - local: model_doc/jamba
@@ -509,8 +529,6 @@
         title: Llama2
       - local: model_doc/llama3
         title: Llama3
-      - local: model_doc/llama4
-        title: Llama4
       - local: model_doc/longformer
         title: Longformer
       - local: model_doc/longt5
@@ -539,8 +557,6 @@
         title: MegatronGPT2
       - local: model_doc/mistral
         title: Mistral
-      - local: model_doc/mistral3
-        title: Mistral3
       - local: model_doc/mixtral
         title: Mixtral
       - local: model_doc/mluke
@@ -591,8 +607,6 @@
         title: Phi
       - local: model_doc/phi3
         title: Phi-3
-      - local: model_doc/phi4_multimodal
-        title: Phi4 Multimodal
       - local: model_doc/phimoe
         title: PhiMoE
       - local: model_doc/phobert
@@ -691,6 +705,8 @@
         title: ConvNeXTV2
       - local: model_doc/cvt
         title: CvT
+      - local: model_doc/d_fine
+        title: D-FINE
       - local: model_doc/dab-detr
         title: DAB-DETR
       - local: model_doc/deformable_detr
@@ -737,6 +753,8 @@
         title: Mask2Former
       - local: model_doc/maskformer
         title: MaskFormer
+      - local: model_doc/mlcd
+        title: MLCD
       - local: model_doc/mobilenet_v1
         title: MobileNetV1
       - local: model_doc/mobilenet_v2
@@ -815,6 +833,8 @@
         title: Bark
       - local: model_doc/clap
         title: CLAP
+      - local: model_doc/csm
+        title: CSM
       - local: model_doc/dac
         title: dac
       - local: model_doc/encodec
@@ -933,6 +953,8 @@
         title: GIT
       - local: model_doc/got_ocr2
         title: GOT-OCR2
+      - local: model_doc/granitevision
+        title: GraniteVision
       - local: model_doc/grounding-dino
         title: Grounding DINO
       - local: model_doc/groupvit
@@ -947,6 +969,10 @@
         title: InstructBLIP
       - local: model_doc/instructblipvideo
         title: InstructBlipVideo
+      - local: model_doc/internvl
+        title: InternVL
+      - local: model_doc/janus
+        title: Janus
       - local: model_doc/kosmos-2
         title: KOSMOS-2
       - local: model_doc/layoutlm
@@ -959,6 +985,8 @@
         title: LayoutXLM
       - local: model_doc/lilt
         title: LiLT
+      - local: model_doc/llama4
+        title: Llama4
       - local: model_doc/llava
         title: Llava
       - local: model_doc/llava_next
@@ -973,6 +1001,8 @@
         title: MatCha
       - local: model_doc/mgp-str
         title: MGP-STR
+      - local: model_doc/mistral3
+        title: Mistral3
       - local: model_doc/mllama
         title: mllama
       - local: model_doc/nougat
@@ -989,6 +1019,8 @@
         title: PaliGemma
       - local: model_doc/perceiver
         title: Perceiver
+      - local: model_doc/phi4_multimodal
+        title: Phi4 Multimodal
       - local: model_doc/pix2struct
         title: Pix2Struct
       - local: model_doc/pixtral
@@ -1003,6 +1035,8 @@
         title: Qwen2VL
       - local: model_doc/sam
         title: Segment Anything
+      - local: model_doc/sam_hq
+        title: Segment Anything High Quality
       - local: model_doc/shieldgemma2
         title: ShieldGemma2
       - local: model_doc/siglip
@@ -1055,6 +1089,8 @@
         title: PatchTST
       - local: model_doc/time_series_transformer
         title: Time Series Transformer
+      - local: model_doc/timesfm
+        title: TimesFM
       title: Time series models
     - sections:
       - local: model_doc/graphormer

docs/source/en/add_new_model.md

@@ -161,7 +161,7 @@ The downside is that if you aren't used to them, it may take some time to get us
 Run the command below to start and complete the questionnaire with some basic information about the new model. This command jumpstarts the process by automatically generating some model code that you'll need to adapt.
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 ## Create a pull request
@@ -292,7 +292,7 @@ Once you're able to run the original checkpoint, you're ready to start adapting
 
 ## Adapt the model code
 
-The `transformers-cli add-new-model-like` command should have generated a model and configuration file.
+The `transformers add-new-model-like` command should have generated a model and configuration file.
 
 - `src/transformers/models/brand_new_llama/modeling_brand_new_llama.py`
 - `src/transformers/models/brand_new_llama/configuration_brand_new_llama.py`
@@ -551,10 +551,10 @@ While this example doesn't include an image processor, you may need to implement
 
 If you do need to implement a new image processor, refer to an existing image processor to understand the expected structure. Slow image processors ([`BaseImageProcessor`]) and fast image processors ([`BaseImageProcessorFast`]) are designed differently, so make sure you follow the correct structure based on the processor type you're implementing.
 
-Run the following command (only if you haven't already created the fast image processor with the `transformers-cli add-new-model-like` command) to generate the necessary imports and to create a prefilled template for the fast image processor. Modify the template to fit your model.
+Run the following command (only if you haven't already created the fast image processor with the `transformers add-new-model-like` command) to generate the necessary imports and to create a prefilled template for the fast image processor. Modify the template to fit your model.
 
 ```bash
-transformers-cli add-fast-image-processor --model-name your_model_name
+transformers add-fast-image-processor --model-name your_model_name
 ```
 
 This command will generate the necessary imports and provide a pre-filled template for the fast image processor. You can then modify it to fit your model's needs.

docs/source/en/attention_interface.md

@@ -108,7 +108,7 @@ If in doubt about what args/kwargs a given model sends to the attention function
 ## Accessing current available implementations
 
 Most of the time, you will simply need to `register` a new function. If, however, you need to access an existing one,
-and/or perform a few checks, the prefered way is to use the global `ALL_ATTENTION_FUNCTIONS`. It behaves the same way you
+and/or perform a few checks, the preferred way is to use the global `ALL_ATTENTION_FUNCTIONS`. It behaves the same way you
 would expect from a usual Python dictionary:
 
 ```python

docs/source/en/auto_docstring.md

@@ -0,0 +1,279 @@
+<!--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.
+
+-->
+
+# Utilizing the @auto_docstring Decorator
+
+The `@auto_docstring` decorator in the Hugging Face Transformers library helps generate docstrings for model classes and their methods, which will be used to build the documentation for the library. It aims to improve consistency and reduce boilerplate by automatically including standard argument descriptions and allowing for targeted overrides and additions.
+
+---
+
+## 📜 How it Works
+
+The `@auto_docstring` decorator constructs docstrings by:
+
+1.  **Signature Inspection:** It inspects the signature (arguments, types, defaults) of the decorated class's `__init__` method or the decorated function.
+2.  **Centralized Docstring Fetching:** It retrieves predefined docstrings for common arguments (e.g., `input_ids`, `attention_mask`) from internal library sources (like `ModelArgs` or `ImageProcessorArgs` in `utils/args_doc.py`).
+3.  **Overriding or Adding Arguments Descriptions:**
+    * **Direct Docstring Block:** It incorporates custom docstring content from an `r""" """` (or `""" """`) block below the method signature or within the `__init__` docstring. This is for documenting new arguments or overriding standard descriptions.
+    * **Decorator Arguments (`custom_args`):** A `custom_args` docstring block can be passed to the decorator to provide docstrings for specific arguments directly in the decorator call. This can be used to define the docstring block for new arguments once if they are repeated in multiple places in the modeling file.
+4.  **Adding Classes and Functions Introduction:**
+    * **`custom_intro` argument:** Allows prepending a custom introductory paragraph to a class or function docstring.
+    * **Automatic Introduction Generation:** For model classes with standard naming patterns (like `ModelForCausalLM`) or belonging to a pipeline, the decorator automatically generates an appropriate introductory paragraph using `ClassDocstring` in `utils/args_doc.py` as the source.
+5.  **Templating:** The decorator uses a templating system, allowing predefined docstrings to include dynamic information deduced from the `auto_modules` of the library, such as `{{processor_class}}` or `{{config_class}}`.
+6.  **Deducing Relevant Examples:** The decorator attempts to find appropriate usage examples based on the model's task or pipeline compatibility. It extracts checkpoint information from the model's configuration class to provide concrete examples with real model identifiers.
+7.  **Adding Return Value Documentation:** For methods like `forward`, the decorator can automatically generate the "Returns" section based on the method's return type annotation. For example, for a method returning a `ModelOutput` subclass, it will extracts field descriptions from that class's docstring to create a comprehensive return value description. A custom `Returns` section can also be manually specified in the function docstring block.
+8.  **Unrolling Kwargs Typed With Unpack Operator:** For specific methods (defined in `UNROLL_KWARGS_METHODS`) or classes (defined in `UNROLL_KWARGS_CLASSES`), the decorator processes `**kwargs` parameters that are typed with `Unpack[KwargsTypedDict]`. It extracts the documentation from the TypedDict and adds each parameter to the function's docstring. Currently, this functionality is only supported for `FastImageProcessorKwargs`.
+
+
+---
+
+## 🚀 How to Use @auto_docstring
+
+### 1. Importing the Decorator
+Import the decorator into your modeling file:
+
+```python
+from ...utils import auto_docstring
+```
+
+### 2. Applying to Classes
+Place `@auto_docstring` directly above the class definition. It uses the `__init__` method's signature and its docstring for parameter descriptions.
+
+```python
+from transformers.modeling_utils import PreTrainedModel
+from ...utils import auto_docstring
+
+@auto_docstring
+class MyAwesomeModel(PreTrainedModel):
+    def __init__(self, config, custom_parameter: int = 10, another_custom_arg: str = "default"):
+        r"""
+        custom_parameter (`int`, *optional*, defaults to 10):
+            Description of the custom_parameter for MyAwesomeModel.
+        another_custom_arg (`str`, *optional*, defaults to "default"):
+            Documentation for another unique argument.
+        """
+        super().__init__(config)
+        self.custom_parameter = custom_parameter
+        self.another_custom_arg = another_custom_arg
+        # ... rest of your init
+
+    # ... other methods
+```
+
+#### Advanced Class Decoration:
+
+Arguments can be passed directly to `@auto_docstring` for more control:
+
+```python
+@auto_docstring(
+    custom_intro="""This model performs specific synergistic operations.
+    It builds upon the standard Transformer architecture with unique modifications.""",
+    custom_args="""
+    custom_parameter (`type`, *optional*, defaults to `default_value`):
+        A concise description for custom_parameter if not defined or overriding the description in `args_doc.py`.
+    internal_helper_arg (`type`, *optional*, defaults to `default_value`):
+        A concise description for internal_helper_arg if not defined or overriding the description in `args_doc.py`.
+    """
+)
+class MySpecialModel(PreTrainedModel):
+    def __init__(self, config: ConfigType, custom_parameter: "type" = "default_value", internal_helper_arg=None):
+        # ...
+```
+
+Or:
+
+```python
+@auto_docstring(
+    custom_intro="""This model performs specific synergistic operations.
+    It builds upon the standard Transformer architecture with unique modifications.""",
+)
+class MySpecialModel(PreTrainedModel):
+    def __init__(self, config: ConfigType, custom_parameter: "type" = "default_value", internal_helper_arg=None):
+        r"""
+        custom_parameter (`type`, *optional*, defaults to `default_value`):
+            A concise description for custom_parameter if not defined or overriding the description in `args_doc.py`.
+        internal_helper_arg (`type`, *optional*, defaults to `default_value`):
+            A concise description for internal_helper_arg if not defined or overriding the description in `args_doc.py`.
+        """
+        # ...
+```
+
+### 3. Applying to Functions (e.g., `forward` method)
+Apply the decorator above method definitions, such as the `forward` method.
+
+```python
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        new_custom_argument: Optional[torch.Tensor] = None,
+        arg_documented_in_args_doc: Optional[torch.Tensor] = None,
+        # ... other arguments
+    ) -> Union[Tuple, ModelOutput]: # The description of the return value will automatically be generated from the ModelOutput class docstring.
+        r"""
+        new_custom_argument (`torch.Tensor`, *optional*):
+            Description of this new custom argument and its expected shape or type.
+        """
+        # ...
+```
+
+#### Advanced Function Decoration:
+
+Arguments can be passed directly to `@auto_docstring` for more control. `Returns` and `Examples` sections can also be manually specified:
+
+```python
+MODEL_COMMON_CUSTOM_ARGS = r"""
+    common_arg_1 (`torch.Tensor`, *optional*, defaults to `default_value`):
+        Description of common_arg_1
+    common_arg_2 (`torch.Tensor`, *optional*, defaults to `default_value`):
+        Description of common_arg_2
+    ...
+"""
+
+class MyModel(PreTrainedModel):
+    # ...
+    @auto_docstring(
+        custom_intro="""
+        This is a custom introduction for the function.
+        """
+        custom_args=MODEL_COMMON_CUSTOM_ARGS
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        common_arg_1: Optional[torch.Tensor] = None,
+        common_arg_2: Optional[torch.Tensor] = None,
+        #...
+        function_specific_argument: Optional[torch.Tensor] = None,
+        # ... other arguments
+    ) -> torch.Tensor:
+        r"""
+        function_specific_argument (`torch.Tensor`, *optional*):
+            Description of an argument specific to this function
+
+        Returns:
+            `torch.Tensor`: For a function returning a generic type, a custom "Returns" section can be specified.
+
+        Example:
+
+        (To override the default example with a custom one or to add an example for a model class that does not have a pipeline)
+
+        ```python
+        ...
+        ```
+        """
+        # ...
+```
+
+---
+
+### ✍️ Documenting Arguments: Approach & Priority
+
+1.  **Standard Arguments (e.g., `input_ids`, `attention_mask`, `pixel_values`, `encoder_hidden_states` etc.):**
+    * `@auto_docstring` retrieves descriptions from a central source. Do not redefine these locally if their description and shape are the same as in `args_doc.py`.
+
+2.  **New or Custom Arguments:**
+    * **Primary Method:** Document these within an `r""" """` docstring block following the signature (for functions) or in the `__init__` method's docstring (for class parameters).
+    * **Format:**
+        ```
+        argument_name (`type`, *optional*, defaults to `X`):
+            Description of the argument.
+            Explain its purpose, expected shape/type if complex, and default behavior.
+            This can span multiple lines.
+        ```
+    * Include `type` in backticks.
+    * Add "*optional*" if the argument is not required (has a default value).
+    * Add "defaults to `X`" if it has a default value (no need to specify "defaults to `None`" if the default value is `None`).
+
+3.  **Overriding Standard Arguments:**
+    * If a standard argument behaves differently (e.g., different expected shape, model-specific behavior), provide its complete description in the local `r""" """` docstring. This local definition takes precedence.
+    * The `labels` argument is often customized per model and typically requires a specific docstring.
+
+4.  **Using Decorator Arguments for Overrides or New Arguments (`custom_args`):**
+    * New or custom arguments docstrings can also be passed to `@auto_docstring` as a `custom_args` argument. This can be used to define the docstring block for new arguments once if they are repeated in multiple places in the modeling file.
+
+---
+
+### Usage with [modular files](./modular_transformers)
+
+When working with modular files, follow these guidelines for applying the `@auto_docstring` decorator:
+
+- **For standalone models in modular files:**
+  Apply the `@auto_docstring` decorator just as you would in regular modeling files.
+
+- **For models inheriting from other library models:**
+  - When inheriting from a parent model, decorators (including `@auto_docstring`) are automatically carried over to the generated modeling file without needing to add them in your modular file.
+  - If you need to modify the `@auto_docstring` behavior, apply the customized decorator in your modular file, making sure to *include all other decorators* that were present on the original function/class.
+
+  > **Warning**: When overriding any decorator in a modular file, you must include ALL decorators that were applied to that function/class in the parent model. If you only override some decorators, the others won't be included in the generated modeling file.
+
+
+**Note**: The `check_auto_docstrings` tool doesn't check modular files directly, but it will check (and modify when using `--fix_and_overwrite`) the generated modeling files. If issues are found in the generated files, you'll need to update your modular files accordingly.
+
+---
+
+## ✅ Checking Your Docstrings with `check_auto_docstrings`
+
+The library includes a utility script to validate docstrings. This check is typically run during Continuous Integration (CI).
+
+#### What it Checks:
+
+* **Decorator Presence:** Ensures `@auto_docstring` is applied to relevant model classes and public methods. (TODO)
+* **Argument Completeness & Consistency:**
+    * Flags arguments in the signature that are not known standard arguments and lack a local description.
+    * Ensures documented arguments exist in the signature. (TODO)
+    * Verifies that types and default values in the docstring match the signature. (TODO)
+* **Placeholder Detection:** Reminds you to complete placeholders like `<fill_type>` or `<fill_docstring>`.
+* **Formatting:** Adherence to the expected docstring style.
+
+#### Running the Check Locally:
+
+Run this check locally before committing. The common command is:
+
+```bash
+make fix-copies
+```
+
+Alternatively, to only perform docstrings and auto-docstring checks, you can use:
+
+```bash
+python utils/check_docstrings.py # to only check files included in the diff without fixing them
+# Or: python utils/check_docstrings.py --fix_and_overwrite # to fix and overwrite the files in the diff
+# Or: python utils/check_docstrings.py --fix_and_overwrite --check_all # to fix and overwrite all files
+```
+
+#### Workflow with the Checker:
+
+1.  Add `@auto_docstring(...)` to the class or method.
+2.  For new, custom, or overridden arguments, add descriptions in an `r""" """` block.
+3.  Run `make fix-copies` (or the `check_docstrings.py` utility).
+    * For unrecognized arguments lacking documentation, the utility will create placeholder entries.
+4.  Manually edit these placeholders with accurate types and descriptions.
+5.  Re-run the check to ensure all issues are resolved.
+
+---
+
+## 🔑 Key Takeaways & Best Practices
+
+* Use `@auto_docstring` for new PyTorch model classes (`PreTrainedModel` subclasses) and their primary for methods (e.g., `forward`, `get_text_features` etc.).
+* For classes, the `__init__` method's docstring is the main source for parameter descriptions when using `@auto_docstring` on the class.
+* Rely on standard docstrings; do not redefine common arguments unless their behavior is different in your specific model.
+* Document new or custom arguments clearly.
+* Run `check_docstrings` locally and iteratively.
+
+By following these guidelines, you help maintain consistent and informative documentation for the Hugging Face Transformers library 🤗.

docs/source/en/conversations.md

@@ -25,22 +25,28 @@ Check model leaderboards like [OpenLLM](https://hf.co/spaces/HuggingFaceH4/open_
 
 This guide shows you how to quickly start chatting with Transformers from the command line, how build and format a conversation, and how to chat using the [`TextGenerationPipeline`].
 
-## transformers-cli
+## transformers CLI
 
-Chat with a model directly from the command line as shown below. It launches an interactive session with a model. Enter `clear` to reset the conversation, `exit` to terminate the session, and `help` to display all the command options.
+After you've [installed Transformers](./installation.md), chat with a model directly from the command line as shown below. It launches an interactive session with a model, with a few base commands listed at the start of the session.
 
 ```bash
-transformers-cli chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
+transformers chat Qwen/Qwen2.5-0.5B-Instruct
 ```
 
 <div class="flex justify-center">
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers-chat-cli.png"/>
 </div>
 
+You can launch the CLI with arbitrary `generate` flags, with the format `arg_1=value_1 arg_2=value_2 ...`
+
+```bash
+transformers chat Qwen/Qwen2.5-0.5B-Instruct do_sample=False max_new_tokens=10
+```
+
 For a full list of options, run the command below.
 
 ```bash
-transformers-cli chat -h
+transformers chat -h
 ```
 
 The chat is implemented on top of the [AutoClass](./model_doc/auto), using tooling from [text generation](./llm_tutorial) and [chat](./chat_templating).
@@ -76,16 +82,16 @@ print(response[0]["generated_text"][-1]["content"])
 (sigh) Oh boy, you're asking me for advice? You're gonna need a map, pal! Alright,
 alright, I'll give you the lowdown. But don't say I didn't warn you, I'm a robot, not a tour guide!
 
-So, you wanna know what's fun to do in the Big Apple? Well, let me tell you, there's a million 
-things to do, but I'll give you the highlights. First off, you gotta see the sights: the Statue of 
-Liberty, Central Park, Times Square... you know, the usual tourist traps. But if you're lookin' for 
-something a little more... unusual, I'd recommend checkin' out the Museum of Modern Art. It's got 
+So, you wanna know what's fun to do in the Big Apple? Well, let me tell you, there's a million
+things to do, but I'll give you the highlights. First off, you gotta see the sights: the Statue of
+Liberty, Central Park, Times Square... you know, the usual tourist traps. But if you're lookin' for
+something a little more... unusual, I'd recommend checkin' out the Museum of Modern Art. It's got
 some wild stuff, like that Warhol guy's soup cans and all that jazz.
 
-And if you're feelin' adventurous, take a walk across the Brooklyn Bridge. Just watch out for 
+And if you're feelin' adventurous, take a walk across the Brooklyn Bridge. Just watch out for
 those pesky pigeons, they're like little feathered thieves! (laughs) Get it? Thieves? Ah, never mind.
 
-Now, if you're lookin' for some serious fun, hit up the comedy clubs in Greenwich Village. You might 
+Now, if you're lookin' for some serious fun, hit up the comedy clubs in Greenwich Village. You might
 even catch a glimpse of some up-and-coming comedians... or a bunch of wannabes tryin' to make it big. (winks)
 
 And finally, if you're feelin' like a real New Yorker, grab a slice of pizza from one of the many amazing
@@ -107,9 +113,9 @@ print(response[0]["generated_text"][-1]["content"])
 ```
 
 ```txt
-(laughs) Oh, you're killin' me, pal! You don't get it, do you? Warhol's soup cans are like, art, man! 
-It's like, he took something totally mundane, like a can of soup, and turned it into a masterpiece. It's 
-like, "Hey, look at me, I'm a can of soup, but I'm also a work of art!" 
+(laughs) Oh, you're killin' me, pal! You don't get it, do you? Warhol's soup cans are like, art, man!
+It's like, he took something totally mundane, like a can of soup, and turned it into a masterpiece. It's
+like, "Hey, look at me, I'm a can of soup, but I'm also a work of art!"
 (sarcastically) Oh, yeah, real original, Andy.
 
 But, you know, back in the '60s, it was like, a big deal. People were all about challenging the

docs/source/en/generation_strategies.md

@@ -20,18 +20,22 @@ A decoding strategy informs how a model should select the next generated token.
 
 This guide will help you understand the different decoding strategies available in Transformers and how and when to use them.
 
-## Greedy search
+## Basic decoding methods
 
-Greedy search is the default decoding strategy. It selects the next most likely token at each step. Unless specified in [`GenerationConfig`], this strategy generates a maximum of 20 tokens.
+These are well established decoding methods, and should be your starting point for text generation tasks.
 
-Greedy search works well for tasks with relatively short outputs. However, it breaks down when generating longer sequences because it begins to repeat itself.
+### Greedy search
+
+Greedy search is the default decoding strategy. It selects the next most likely token at each step. Unless specified in [`GenerationConfig`], this strategy generates a maximum of 20 new tokens.
+
+Greedy search works well for tasks with relatively short outputs where creativity is not a priority. However, it breaks down when generating longer sequences because it begins to repeat itself.
 
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-inputs = tokenizer("I look forward to", return_tensors="pt").to("cuda")
+inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
 
 model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
 # explicitly set to default length because Llama2 generation length is 4096
@@ -40,11 +44,11 @@ tokenizer.batch_decode(outputs, skip_special_tokens=True)
 'Hugging Face is an open-source company that provides a suite of tools and services for building, deploying, and maintaining natural language processing'
 ```
 
-## Contrastive search
+### Sampling
 
-[Contrastive search](https://huggingface.co/papers/2202.06417) is a decoding strategy that aims to reduce repetition even while generating longer sequences. This strategy compares how similar a generated token is against previous tokens, and if they're more similar, a penalty is applied.
+Sampling, or multinomial sampling, randomly selects a token based on the probability distribution over the entire model's vocabulary (as opposed to the most likely token, as in greedy search). This means every token with a non-zero probability has a chance to be selected. Sampling strategies reduce repetition and can generate more creative and diverse outputs.
 
-Enable contrastive search with the `penalty_alpha` and `top_k` parameters. The `penalty_alpha` manages the penalty applied and `top_k` is the number of most likely tokens to return.
+Enable multinomial sampling with `do_sample=True` and `num_beams=1`.
 
 ```py
 import torch
@@ -55,14 +59,14 @@ inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt"
 
 model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
 # explicitly set to 100 because Llama2 generation length is 4096
-outputs = model.generate(**inputs, max_new_tokens=100, penalty_alpha=0.6, top_k=4)
+outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, num_beams=1)
 tokenizer.batch_decode(outputs, skip_special_tokens=True)
-'Hugging Face is an open-source company that provides a platform for building and deploying AI models.\nHugging Face is an open-source company that provides a platform for building and deploying AI models. The platform allows developers to build and deploy AI models, as well as collaborate with other developers.\nHugging Face was founded in 2019 by Thibault Wittemberg and Clément Delangue. The company is based in Paris, France.\nHugging Face has'
+'Hugging Face is an open-source company 🤗\nWe are open-source and believe that open-source is the best way to build technology. Our mission is to make AI accessible to everyone, and we believe that open-source is the best way to achieve that.'
 ```
 
-## Beam search
+### Beam search
 
-Beam search keeps track of several generated sequences (beams) at each time step. After a certain number of steps, it selects the sequence with the highest *overall* probability. Unlike greedy search, this strategy can "look ahead" and pick a sequence with a higher probability overall even if the initial tokens have a lower probability.
+Beam search keeps track of several generated sequences (beams) at each time step. After a certain number of steps, it selects the sequence with the highest *overall* probability. Unlike greedy search, this strategy can "look ahead" and pick a sequence with a higher probability overall even if the initial tokens have a lower probability. It is best suited for input-grounded tasks, like describing an image or speech recognition. You can also use `do_sample=True` with beam search to sample at each step, but beam search will still greedily prune out low probability sequences between steps.
 
 > [!TIP]
 > Check out the [beam search visualizer](https://huggingface.co/spaces/m-ric/beam_search_visualizer) to see how beam search works.
@@ -83,66 +87,11 @@ tokenizer.batch_decode(outputs, skip_special_tokens=True)
 "['Hugging Face is an open-source company that develops and maintains the Hugging Face platform, which is a collection of tools and libraries for building and deploying natural language processing (NLP) models. Hugging Face was founded in 2018 by Thomas Wolf']"
 ```
 
-## Diverse beam search
+## Advanced decoding methods
 
-[Diverse beam search](https://hf.co/papers/1610.02424) is a variant of beam search that produces more diverse output candidates to choose from. This strategy measures the dissimilarity of sequences and a penalty is applied if sequences are too similar. To avoid high computation costs, the number of beams is divided into groups.
+Advanced decoding methods aim at either tackling specific generation quality issues (e.g. repetition) or at improving the generation throughput in certain situations. These techniques are more complex, and may not work correctly with all models.
 
-Enable diverse beam search with the `num_beams`, `num_beam_groups` and `diversity_penalty` parameters (the `num_beams` parameter should be divisible by `num_beam_groups`).
-
-```py
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
-
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
-# explicitly set to 100 because Llama2 generation length is 4096
-outputs = model.generate(**inputs, max_new_tokens=50, num_beams=6, num_beam_groups=3, diversity_penalty=1.0, do_sample=False)
-tokenizer.batch_decode(outputs, skip_special_tokens=True)
-'Hugging Face is an open-source company 🤗\nWe are an open-source company. Our mission is to democratize AI and make it accessible to everyone. We believe that AI should be used for the benefit of humanity, not for the benefit of a'
-```
-
-## Multinomial sampling
-
-Search methods selects the most likely tokens. Sampling, or multinomial sampling, randomly selects a token based on the probability distribution over the entire models vocabulary. This means every token with a non-zero probability has a chance to be selected. Sampling strategies reduce repetition and can generate more creative and diverse outputs.
-
-Enable multinomial sampling with `do_sample=True` and `num_beams=1`.
-
-```py
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
-
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
-# explicitly set to 100 because Llama2 generation length is 4096
-outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, num_beams=1)
-tokenizer.batch_decode(outputs, skip_special_tokens=True)
-'Hugging Face is an open-source company 🤗\nWe are open-source and believe that open-source is the best way to build technology. Our mission is to make AI accessible to everyone, and we believe that open-source is the best way to achieve that.'
-```
-
-## Beam search multinomial sampling
-
-This decoding strategy is a combination of beam search and multinomial sampling. It generates multiple beams and uses a sampling strategy for each beam.
-
-Enable beam search multinomial sampling by setting `num_beams` to a value greater than 1 and `do_sample=True`.
-
-```py
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
-
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
-# explicitly set to 100 because Llama2 generation length is 4096
-outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, num_beams=4)
-'Hugging Face is an open-source company 100% dedicated to making AI more accessible. We believe that AI should be available to everyone, and we’re working hard to make that a reality.\nWe’re a team of passionate engineers, designers,'
-```
-
-## Speculative decoding
+### Speculative decoding
 
 [Speculative](https://hf.co/papers/2211.17192) or assistive decoding isn't a search or sampling strategy. Instead, speculative decoding adds a second smaller model to generate candidate tokens. The main model verifies the candidate tokens in a single `forward` pass, which speeds up the decoding process overall. This method is especially useful for LLMs where it can be more costly and slower to generate tokens. Refer to the [speculative decoding](./llm_optims#speculative-decoding) guide to learn more.
 
@@ -203,7 +152,7 @@ tokenizer.batch_decode(outputs, skip_special_tokens=True)
 </hfoption>
 </hfoptions>
 
-### Prompt lookup decoding
+#### Prompt lookup decoding
 
 [Prompt lookup decoding](./llm_optims#prompt-lookup-decoding) is a variant of speculative decoding that uses overlapping n-grams as the candidate tokens. It works well for input-grounded tasks such as summarization. Refer to the [prompt lookup decoding](./llm_optims#prompt-lookup-decoding) guide to learn more.
 
@@ -245,7 +194,7 @@ outputs = model.generate(**inputs, assistant_early_exit=4, do_sample=False, max_
 tokenizer.batch_decode(outputs, skip_special_tokens=True)
 ```
 
-### Universal assisted decoding
+#### Universal assisted decoding
 
 Universal assisted decoding (UAD) enables the main and assistant models to use different tokenizers. The main models input tokens are re-encoded into assistant model tokens. Candidate tokens are generated in the assistant encoding which are re-encoded into the main model candidate tokens. The candidate tokens are verified as explained in [speculative decoding](#speculative-decoding).
 
@@ -269,7 +218,27 @@ 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']
 ```
 
-## DoLa
+### Contrastive search
+
+[Contrastive search](https://huggingface.co/papers/2202.06417) is a decoding strategy that aims to reduce repetition even while generating longer sequences. This strategy compares how similar a generated token is against previous tokens, and if they're more similar, a penalty is applied.
+
+Enable contrastive search with the `penalty_alpha` and `top_k` parameters. The `penalty_alpha` manages the penalty applied and `top_k` is the number of most likely tokens to return.
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
+
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
+# explicitly set to 100 because Llama2 generation length is 4096
+outputs = model.generate(**inputs, max_new_tokens=100, penalty_alpha=0.6, top_k=4)
+tokenizer.batch_decode(outputs, skip_special_tokens=True)
+'Hugging Face is an open-source company that provides a platform for building and deploying AI models.\nHugging Face is an open-source company that provides a platform for building and deploying AI models. The platform allows developers to build and deploy AI models, as well as collaborate with other developers.\nHugging Face was founded in 2019 by Thibault Wittemberg and Clément Delangue. The company is based in Paris, France.\nHugging Face has'
+```
+
+### DoLa
 
 [Decoding by Contrasting Layers (DoLa)](https://hf.co/papers/2309.03883) is a contrastive decoding strategy for improving factuality and reducing hallucination. This strategy works by contrasting the logit differences between the final and early layers. As a result, factual knowledge localized to particular layers are amplified. DoLa is not recommended for smaller models like GPT-2.
 
@@ -325,6 +294,210 @@ tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tok
 </hfoption>
 </hfoptions>
 
+### Diverse beam search
+
+[Diverse beam search](https://hf.co/papers/1610.02424) is a variant of beam search that produces more diverse output candidates to choose from. This strategy measures the dissimilarity of sequences and a penalty is applied if sequences are too similar. To avoid high computation costs, the number of beams is divided into groups.
+
+Enable diverse beam search with the `num_beams`, `num_beam_groups` and `diversity_penalty` parameters (the `num_beams` parameter should be divisible by `num_beam_groups`).
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+inputs = tokenizer("Hugging Face is an open-source company", return_tensors="pt").to("cuda")
+
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.float16).to("cuda")
+# explicitly set to 100 because Llama2 generation length is 4096
+outputs = model.generate(**inputs, max_new_tokens=50, num_beams=6, num_beam_groups=3, diversity_penalty=1.0, do_sample=False)
+tokenizer.batch_decode(outputs, skip_special_tokens=True)
+'Hugging Face is an open-source company 🤗\nWe are an open-source company. Our mission is to democratize AI and make it accessible to everyone. We believe that AI should be used for the benefit of humanity, not for the benefit of a'
+```
+
+
+## Custom decoding methods
+
+Custom decoding methods enable specialized generation behavior such as the following:
+- have the model continue thinking if it is uncertain;
+- roll back generation if the model gets stuck;
+- handle special tokens with custom logic;
+- enhanced input preparation for advanced models;
+
+We enable custom decoding methods through model repositories, assuming a specific model tag and file structure (see subsection below). This feature is an extension of [custom modeling code](./models.md#custom-models) and, like such, requires setting `trust_remote_code=True`.
+
+If a model repository holds a custom decoding method, the easiest way to try it out is to load the model and generate with it:
+
+<!-- TODO before merging: 1) better repo name (use a `generate-community` org?) 2) prettify the repo -->
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# `transformers-community/custom_generate_example` holds a copy of `Qwen/Qwen2.5-0.5B-Instruct`, but
+# with custom generation code -> calling `generate` uses the custom decoding method!
+tokenizer = AutoTokenizer.from_pretrained("transformers-community/custom_generate_example")
+model = AutoModelForCausalLM.from_pretrained(
+    "transformers-community/custom_generate_example", device_map="auto", trust_remote_code=True
+)
+
+inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device)
+# The custom decoding method is a minimal greedy decoding implementation. It also prints a custom message at run time.
+gen_out = model.generate(**inputs)
+# you should now see its custom message, "✨ using a custom generation method ✨"
+print(tokenizer.batch_decode(gen_out, skip_special_tokens=True))
+'The quick brown fox jumps over a lazy dog, and the dog is a type of animal. Is'
+```
+
+Model repositories with custom decoding methods have a special property: their decoding method can be loaded from **any** model through [`~GenerationMixin.generate`]'s `custom_generate` argument. This means anyone can create and share their custom generation method to potentially work with any Transformers model, without requiring users to install additional Python packages.
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", device_map="auto")
+
+inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device)
+# `custom_generate` replaces the original `generate` by the custom decoding method defined in
+# `transformers-community/custom_generate_example`
+gen_out = model.generate(**inputs, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True)
+print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
+'The quick brown fox jumps over a lazy dog, and the dog is a type of animal. Is'
+```
+
+You should read the `README.md` file of the repository containing the custom generation strategy to see what the new arguments and output type differences are, if they exist. Otherwise, you can assume it works like the base [`~GenerationMixin.generate`] method.
+
+> [!TIP]
+> You can find all custom decoding methods by [searching for their custom tag.](https://huggingface.co/models?other=custom_generate), `custom_generate`
+
+Consider the Hub repository [transformers-community/custom_generate_example](https://huggingface.co/transformers-community/custom_generate_example) as an example. The `README.md` states that it has an additional input argument, `left_padding`, which adds a number of padding tokens before the prompt.
+
+```py
+gen_out = model.generate(
+    **inputs, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True, left_padding=5
+)
+print(tokenizer.batch_decode(gen_out)[0])
+'<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>The quick brown fox jumps over the lazy dog.\n\nThe sentence "The quick'
+```
+
+If the custom method has pinned Python requirements that your environment doesn't meet, you'll get an exception about missing requirements. For instance, [transformers-community/custom_generate_bad_requirements](https://huggingface.co/transformers-community/custom_generate_bad_requirements) has an impossible set of requirements defined in its `custom_generate/requirements.txt` file, and you'll see the error message below if you try to run it.
+
+```
+ImportError: Missing requirements in your local environment for `transformers-community/custom_generate_bad_requirements`:
+foo (installed: None)
+bar==0.0.0 (installed: None)
+torch>=99.0 (installed: 2.6.0)
+```
+
+Updating your Python requirements accordingly will remove this error message.
+
+### Creating a custom decoding method
+
+To create a new decoding method, you need to create a new [**Model**](https://huggingface.co/new) repository and push a few files into it.
+1. The model you've designed your decoding method with.
+2. `custom_generate/generate.py`, which contains all the logic for your custom decoding method.
+3. `custom_generate/requirements.txt`, used to optionally add new Python requirements and/or lock specific versions to correctly use your method.
+4. `README.md`, where you should add the `custom_generate` tag and document any new arguments or output type differences of your custom method here.
+
+After you've added all required files, your repository should look like this
+
+```
+your_repo/
+├── README.md          # include the 'custom_generate' tag
+├── config.json
+├── ...
+└── custom_generate/
+    ├── generate.py
+    └── requirements.txt
+```
+
+#### Adding the base model
+
+The starting point for your custom decoding method is a model repository just like any other. The model to add to this repository should be the model you've designed your method with, and it is meant to be part of a working self-contained model-generate pair. When the model in this repository is loaded, your custom decoding method will override `generate`. Don't worry -- your decoding method can still be loaded with any other Transformers model, as explained in the section above.
+
+If you simply want to copy an existing model, you can do
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("source/model_repo")
+model = AutoModelForCausalLM.from_pretrained("source/model_repo")
+tokenizer.save_pretrained("your/decoding_method", push_to_hub=True)
+model.save_pretrained("your/decoding_method", push_to_hub=True)
+```
+
+#### generate.py
+
+This is the core of your decoding method. It *must* contain a method named `generate`, and this method *must* contain a `model` argument as its first argument. `model` is the model instance, which means you have access to all attributes and methods in the model, including the ones defined in [`GenerationMixin`] (like the base `generate` method).
+
+> [!WARNING]
+> `generate.py` must be placed in a folder named `custom_generate`, and not at the root level of the repository. The file paths for this feature are hardcoded.
+
+Under the hood, when the base [`~GenerationMixin.generate`] method is called with a `custom_generate` argument, it first checks its Python requirements (if any), then locates the custom `generate` method in `generate.py`, and finally calls the custom `generate`. All received arguments and `model` are forwarded to your custom `generate` method.
+
+This means your `generate` can have a mix of original and custom arguments (as well as a different output type) as shown below.
+
+```py
+import torch
+
+def generate(model, input_ids, generation_config=None, left_padding=None, **kwargs):
+    generation_config = generation_config or model.generation_config  # default to the model generation config
+    cur_length = input_ids.shape[1]
+    max_length = generation_config.max_length or cur_length + generation_config.max_new_tokens
+
+    # Example of custom argument: add `left_padding` (integer) pad tokens before the prompt
+    if left_padding is not None:
+        if not isinstance(left_padding, int) or left_padding < 0:
+            raise ValueError(f"left_padding must be an integer larger than 0, but is {left_padding}")
+
+        pad_token = kwargs.pop("pad_token", None) or generation_config.pad_token_id or model.config.pad_token_id
+        if pad_token is None:
+            raise ValueError("pad_token is not defined")
+        batch_size = input_ids.shape[0]
+        pad_tensor = torch.full(size=(batch_size, left_padding), fill_value=pad_token).to(input_ids.device)
+        input_ids = torch.cat((pad_tensor, input_ids), dim=1)
+        cur_length = input_ids.shape[1]
+
+    # Simple greedy decoding loop
+    while cur_length < max_length:
+        logits = model(input_ids).logits
+        next_token_logits = logits[:, -1, :]
+        next_tokens = torch.argmax(next_token_logits, dim=-1)
+        input_ids = torch.cat((input_ids, next_tokens[:, None]), dim=-1)
+        cur_length += 1
+
+    return input_ids
+```
+
+Follow the recommended practices below to ensure your custom decoding method works as expected.
+- Feel free to reuse the logic for validation and input preparation in the original [`~GenerationMixin.generate`].
+- Pin the `transformers` version in the requirements if you use any private method/attribute in `model`.
+- You can add other files in the `custom_generate` folder, and use relative imports.
+- Consider adding model validation, input validation, or even a separate test file to help users sanity-check your code in their environment.
+
+#### requirements.txt
+
+You can optionally specify additional Python requirements in a `requirements.txt` file inside the `custom_generate` folder. These are checked at runtime and an exception will be thrown if they're missing, nudging users to update their environment accordingly.
+
+#### README.md
+
+The root level `README.md` in the model repository usually describes the model therein. However, since the focus of the repository is the custom decoding method, we highly recommend to shift its focus towards describing the custom decoding method. In addition to a description of the method, we recommend documenting any input and/or output differences to the original [`~GenerationMixin.generate`]. This way, users can focus on what's new, and rely on Transformers docs for generic implementation details.
+
+For discoverability, we highly recommend you to add the `custom_generate` tag to your repository. To do so, the top of your `README.md` file should look like the example below. After you push the file, you should see the tag in your repository!
+
+```
+---
+library_name: transformers
+tags:
+  - custom_generate
+---
+
+(your markdown content here)
+```
+
+Recommended practices:
+- Document input and output differences in [`~GenerationMixin.generate`].
+- Add self-contained examples to enable quick experimentation.
+- Describe soft-requirements such as if the method only works well with a certain family of models.
+
+
 ## Resources
 
 Read the [How to generate text: using different decoding methods for language generation with Transformers](https://huggingface.co/blog/how-to-generate) blog post for an explanation of how common decoding strategies work.

docs/source/en/how_to_hack_models.md

@@ -90,11 +90,6 @@ class SamVisionAttentionSplit(SamVisionAttention, nn.Module):
 
         attn_weights = (query * self.scale) @ key.transpose(-2, -1)
 
-        if self.use_rel_pos:
-            attn_weights = self.add_decomposed_rel_pos(
-                attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
-            )
-
         attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
         attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
         attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
@@ -114,13 +109,14 @@ Load the model with [`~PreTrainedModel.from_pretrained`].
 
 ```py
 from transformers import SamModel
-from transformers.models.sam import modeling_sam
-
-# replace the attention class in the modeling_sam module
-modeling_sam.SamVisionAttention = SamVisionAttentionSplit
 
 # load the pretrained SAM model
 model = SamModel.from_pretrained("facebook/sam-vit-base")
+
+# replace the attention class in the vision_encoder module
+for layer in model.vision_encoder.layers:
+    if hasattr(layer, "attn"):
+        layer.attn = SamVisionAttentionSplit(model.config.vision_config, model.config.vision_config.window_size)
 ```
 
 ## LoRA
@@ -138,7 +134,7 @@ config = LoraConfig(
     # apply LoRA to q and v
     target_modules=["q", "v"],
     lora_dropout=0.1,
-    task_type="mask-generation"
+    task_type="FEATURE_EXTRACTION"
 )
 ```
 
@@ -152,5 +148,5 @@ Call [print_trainable_parameters](https://huggingface.co/docs/peft/package_refer
 
 ```py
 model.print_trainable_parameters()
-"trainable params: 608,256 || all params: 94,343,728 || trainable%: 0.6447"
+"trainable params: 589,824 || all params: 94,274,096 || trainable%: 0.6256"
 ```

docs/source/en/image_processors.md

@@ -16,7 +16,7 @@ rendered properly in your Markdown viewer.
 
 # Image processors
 
-Image processors converts images into pixel values, tensors that represent image colors and size. The pixel values are inputs to a vision or video model. To ensure a pretrained model receives the correct input, an image processor can perform the following operations to make sure an image is exactly like the images a model was pretrained on.
+Image processors converts images into pixel values, tensors that represent image colors and size. The pixel values are inputs to a vision model. To ensure a pretrained model receives the correct input, an image processor can perform the following operations to make sure an image is exactly like the images a model was pretrained on.
 
 - [`~BaseImageProcessor.center_crop`] to resize an image
 - [`~BaseImageProcessor.normalize`] or [`~BaseImageProcessor.rescale`] pixel values

docs/source/en/internal/import_utils.md

@@ -84,6 +84,19 @@ class Trainer:
 
 Backends that can be added here are all the backends that are available in the `import_utils.py` module.
 
+Additionally, specific versions can be specified in each backend. For example, this is how you would specify
+a requirement on torch>=2.6 on the `Trainer` class:
+
+```python
+from .utils.import_utils import requires
+
+@requires(backends=("torch>=2.6", "accelerate"))
+class Trainer:
+    ...
+```
+
+You can specify the following operators: `==`, `>`, `>=`, `<`, `<=`, `!=`.
+
 ## Methods
 
 [[autodoc]] utils.import_utils.define_import_structure

docs/source/en/internal/model_debugging_utils.md

@@ -28,7 +28,7 @@ Most of those are only useful if you are adding new models in the library.
 
 This context manager is a power user tool intended for model adders.
 It tracks all forward calls within a model forward and logs a slice of each input and output on a nested Json.
-To note, this context manager enforces `torch.inference_mode()`.
+To note, this context manager enforces `torch.no_grad()`.
 
 ### Rationale
 
@@ -43,6 +43,7 @@ import torch
 from PIL import Image
 import requests
 from transformers import LlavaProcessor, LlavaForConditionalGeneration
+from transformers.model_debugging_utils import model_addition_debugger_context
 torch.random.manual_seed(673)
 
 # load pretrained model and processor
@@ -60,12 +61,153 @@ prompt = "<image>Describe this image."
 inputs = processor(text=prompt, images=random_image, return_tensors="pt")
 
 # call forward method (not .generate!)
-with model_addition_debugger_context(model, "optional_path_to_your_output_file.json"):
+with model_addition_debugger_context(
+  model,
+  debug_path="optional_path_to_your_directory",
+  do_prune_layers=False # This will output ALL the layers of a model.
+  ):
     output = model.forward(**inputs)
 
 ```
 
 
-[[autodoc]] model_addition_debugger
+### Reading results
+
+The debugger generates two files from the forward call, both with the same base name, 
+but ending either with `_SUMMARY.json` or with `_FULL_TENSORS.json`. 
+
+The first one will contain a summary of each module's _input_ and _output_ tensor values and shapes.
+
+```json
+{
+  "module_path": "MolmoForConditionalGeneration",
+  "inputs": {
+    "args": [],
+    "kwargs": {
+      "input_ids": {
+        "shape": "torch.Size([1, 589])",
+        "dtype": "torch.int64"
+      },
+      "attention_mask": {
+        "shape": "torch.Size([1, 589])",
+        "dtype": "torch.int64"
+      },
+      "pixel_values": {
+        "shape": "torch.Size([1, 5, 576, 588])",
+        "dtype": "torch.float32",
+        "mean": "tensor(-8.9514e-01, device='cuda:0')",
+        "std": "tensor(9.2586e-01, device='cuda:0')",
+        "min": "tensor(-1.7923e+00, device='cuda:0')",
+        "max": "tensor(1.8899e+00, device='cuda:0')"
+    }
+  },
+  "children": [
+    {
+      "module_path": "MolmoForConditionalGeneration.language_model.model.embed_tokens",
+      "inputs": {
+        "args": [
+          {
+            "shape": "torch.Size([1, 589])",
+            "dtype": "torch.int64"
+          }
+        ]
+      },
+      "outputs": {
+        "shape": "torch.Size([1, 589, 3584])",
+        "dtype": "torch.float32",
+        "mean": "tensor(6.5460e-06, device='cuda:0')",
+        "std": "tensor(2.3807e-02, device='cuda:0')",
+        "min": "tensor(-3.3398e-01, device='cuda:0')",
+        "max": "tensor(3.9453e-01, device='cuda:0')"
+      }
+    },
+    {
+      "module_path": "MolmoForConditionalGeneration.vision_tower",
+      "inputs": {
+        "args": [
+          {
+            "shape": "torch.Size([5, 1, 576, 588])",
+            "dtype": "torch.float32",
+            "mean": "tensor(-8.9514e-01, device='cuda:0')",
+            "std": "tensor(9.2586e-01, device='cuda:0')",
+            "min": "tensor(-1.7923e+00, device='cuda:0')",
+            "max": "tensor(1.8899e+00, device='cuda:0')"
+          }
+        ],
+        "kwargs": {
+          "output_hidden_states": "True"
+        }
+      },
+      "children": [
+        { ... and so on
+```
+
+The `_FULL_TENSORS.json` file will display a full view of all tensors, which is useful
+for comparing two files. 
+```json
+      "pixel_values": {
+        "shape": "torch.Size([1, 5, 576, 588])",
+        "dtype": "torch.float32",
+        "value": [
+          "tensor([[[[-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          ...,",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]],",
+          "",
+          "         [[-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          ...,",
+          "          [-1.4857e+00, -1.4820e+00, -1.2100e+00,  ..., -6.0979e-01, -5.9650e-01, -3.8527e-01],",
+          "          [-1.6755e+00, -1.7221e+00, -1.4518e+00,  ..., -7.5577e-01, -7.4658e-01, -5.5592e-01],",
+          "          [-7.9957e-01, -8.2162e-01, -5.7014e-01,  ..., -1.3689e+00, -1.3169e+00, -1.0678e+00]],",
+          "",
+          "         [[-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          ...,",
+          "          [-3.0322e-01, -5.0645e-01, -5.8436e-01,  ..., -6.2439e-01, -7.9160e-01, -8.1188e-01],",
+          "          [-4.4921e-01, -6.5653e-01, -7.2656e-01,  ..., -3.4702e-01, -5.2146e-01, -5.1326e-01],",
+          "          [-3.4702e-01, -5.3647e-01, -5.4170e-01,  ..., -1.0915e+00, -1.1968e+00, -1.0252e+00]],",
+          "",
+          "         [[-1.1207e+00, -1.2718e+00, -1.0678e+00,  ..., 1.2013e-01, -1.3126e-01, -1.7197e-01],",
+          "          [-6.9738e-01, -9.1166e-01, -8.5454e-01,  ..., -5.5050e-02, -2.8134e-01, -4.2793e-01],",
+          "          [-3.4702e-01, -5.5148e-01, -5.8436e-01,  ..., 1.9312e-01, -8.6235e-02, -2.1463e-01],",
+          "          ...,",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]],",
+          "",
+          "         [[-1.0039e+00, -9.5669e-01, -6.5546e-01,  ..., -1.4711e+00, -1.4219e+00, -1.1389e+00],",
+          "          [-1.0039e+00, -9.5669e-01, -6.5546e-01,  ..., -1.7193e+00, -1.6771e+00, -1.4091e+00],",
+          "          [-1.6317e+00, -1.6020e+00, -1.2669e+00,  ..., -1.2667e+00, -1.2268e+00, -8.9720e-01],",
+          "          ...,",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],",
+          "          [-1.7923e+00, -1.7521e+00, -1.4802e+00,  ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]]]], device='cuda:0')"
+        ],
+        "mean": "tensor(-8.9514e-01, device='cuda:0')",
+        "std": "tensor(9.2586e-01, device='cuda:0')",
+        "min": "tensor(-1.7923e+00, device='cuda:0')",
+        "max": "tensor(1.8899e+00, device='cuda:0')"
+      },
+```
+
+### Comparing between implementations
+
+Once the forward passes of two models have been traced by the debugger, one can compare the `json` output files. See below: we can see slight differences between these two implementations' key projection layer. Inputs are mostly identical, but not quite. Looking through the file differences makes it easier to pinpoint which layer is wrong. 
+
+
+![download-icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/files_difference_debugging.png)
+
+
+### Limitations and scope
+
+This feature will only work for torch-based models, and would require more work and case-by-case approach for say `jax`-based models that are usually compiled. Models relying heavily on external kernel calls may work, but trace will probably miss some things. Regardless, any python implementation that aims at mimicking another implementation can be traced once instead of reran N times with breakpoints.
+
+If you pass `do_prune_layers=False` to your model debugger, ALL the layers will be outputted to `json`. Else, only the first and last layer will be shown. This is useful when some layers (typically cross-attention) appear only after N layers. 
 
 [[autodoc]] model_addition_debugger_context

docs/source/en/internal/modeling_utils.md

@@ -20,6 +20,10 @@ This page lists all the custom layers used by the library, as well as the utilit
 
 Most of those are only useful if you are studying the code of the models in the library.
 
+## Layers
+
+[[autodoc]] GradientCheckpointingLayer
+
 ## Attention Functions
 
 [[autodoc]] AttentionInterface
@@ -33,23 +37,6 @@ Most of those are only useful if you are studying the code of the models in the
 
 [[autodoc]] pytorch_utils.Conv1D
 
-[[autodoc]] modeling_utils.PoolerStartLogits
-    - forward
-
-[[autodoc]] modeling_utils.PoolerEndLogits
-    - forward
-
-[[autodoc]] modeling_utils.PoolerAnswerClass
-    - forward
-
-[[autodoc]] modeling_utils.SquadHeadOutput
-
-[[autodoc]] modeling_utils.SQuADHead
-    - forward
-
-[[autodoc]] modeling_utils.SequenceSummary
-    - forward
-
 ## PyTorch Helper Functions
 
 [[autodoc]] pytorch_utils.apply_chunking_to_forward

docs/source/en/kv_cache.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 The key-value (KV) vectors are used to calculate attention scores. For autoregressive models, KV scores are calculated *every* time because the model predicts one token at a time. Each prediction depends on the previous tokens, which means the model performs the same computations each time.
 
-A KV *cache* stores these calculations so they can be reused without recomputing them. Efficient caching is crucial for optimizing model performance because it reduces computation time and improves response rates. Refer to the [Caching](./cache_explanation.md) doc for a more detailed explanation about how a cache works.
+A KV *cache* stores these calculations so they can be reused without recomputing them. Efficient caching is crucial for optimizing model performance because it reduces computation time and improves response rates. Refer to the [Caching](./cache_explanation) doc for a more detailed explanation about how a cache works.
 
 Transformers offers several [`Cache`] classes that implement different caching mechanisms. Some of these [`Cache`] classes are optimized to save memory while others are designed to maximize generation speed. Refer to the table below to compare cache types and use it to help you select the best cache for your use case.
 

docs/source/en/llm_tutorial.md

@@ -20,9 +20,13 @@ rendered properly in your Markdown viewer.
 
 Text generation is the most popular application for large language models (LLMs). A LLM is trained to generate the next word (token) given some initial text (prompt) along with its own generated outputs up to a predefined length or when it reaches an end-of-sequence (`EOS`) token.
 
-In Transformers, the [`~GenerationMixin.generate`] API handles text generation, and it is available for all models with generative capabilities.
+In Transformers, the [`~GenerationMixin.generate`] API handles text generation, and it is available for all models with generative capabilities. This guide will show you the basics of text generation with [`~GenerationMixin.generate`] and some common pitfalls to avoid.
 
-This guide will show you the basics of text generation with [`~GenerationMixin.generate`] and some common pitfalls to avoid.
+> [!TIP]
+> You can also chat with a model directly from the command line. ([reference](./conversations.md#transformers-cli))
+> ```shell
+> transformers chat Qwen/Qwen2.5-0.5B-Instruct
+> ```
 
 ## Default generate
 
@@ -134,6 +138,20 @@ outputs = model.generate(**inputs, generation_config=generation_config)
 print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
 ```
 
+## Common Options
+
+[`~GenerationMixin.generate`] is a powerful tool that can be heavily customized. This can be daunting for a new users. This section contains a list of popular generation options that you can define in most text generation tools in Transformers: [`~GenerationMixin.generate`], [`GenerationConfig`], `pipelines`, the `chat` CLI, ...
+
+| Option name | Type | Simplified description |
+|---|---|---|
+| `max_new_tokens` | `int` | Controls the maximum generation length. Be sure to define it, as it usually defaults to a small value. |
+| `do_sample` | `bool` | Defines whether generation will sample the next token (`True`), or is greedy instead (`False`). Most use cases should set this flag to `True`. Check [this guide](./generation_strategies.md) for more information. |
+| `temperature` | `float` | How unpredictable the next selected token will be. High values (`>0.8`) are good for creative tasks, low values (e.g. `<0.4`) for tasks that require "thinking". Requires `do_sample=True`. |
+| `num_beams` | `int` | When set to `>1`, activates the beam search algorithm. Beam search is good on input-grounded tasks. Check [this guide](./generation_strategies.md) for more information. |
+| `repetition_penalty` | `float` | Set it to `>1.0` if you're seeing the model repeat itself often. Larger values apply a larger penalty. |
+| `eos_token_id` | `List[int]` | The token(s) that will cause generation to stop. The default value is usually good, but you can specify a different token. |
+
+
 ## Pitfalls
 
 The section below covers some common issues you may encounter during text generation and how to solve them.
@@ -286,4 +304,4 @@ Take a look below for some more specific and specialized text generation librari
 - [SynCode](https://github.com/uiuc-focal-lab/syncode): a library for context-free grammar guided generation (JSON, SQL, Python).
 - [Text Generation Inference](https://github.com/huggingface/text-generation-inference): a production-ready server for LLMs.
 - [Text generation web UI](https://github.com/oobabooga/text-generation-webui): a Gradio web UI for text generation.
-- [logits-processor-zoo](https://github.com/NVIDIA/logits-processor-zoo): additional logits processors for controlling text generation.
+- [logits-processor-zoo](https://github.com/NVIDIA/logits-processor-zoo): additional logits processors for controlling text generation.

docs/source/en/main_classes/quantization.md

@@ -77,9 +77,9 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
 
 [[autodoc]] TorchAoConfig
 
-## BitNetConfig
+## BitNetQuantConfig
 
-[[autodoc]] BitNetConfig
+[[autodoc]] BitNetQuantConfig
 
 ## SpQRConfig
 
@@ -92,3 +92,7 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
 ## QuarkConfig
 
 [[autodoc]] QuarkConfig
+
+## AutoRoundConfig
+
+[[autodoc]] AutoRoundConfig

docs/source/en/main_classes/video_processor.md

@@ -0,0 +1,55 @@
+<!--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.
+
+-->
+
+
+# Video Processor
+
+A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. 
+
+The video processor extends the functionality of image processors by allowing Vision Large Language Models (VLMs) to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.
+
+When adding a new VLM or updating an existing one to enable distinct video preprocessing, saving and reloading the processor configuration will store the video related arguments in a dedicated file named `video_preprocessing_config.json`. Don't worry if you haven't upadted your VLM, the processor will try to load video related configurations from a file named `preprocessing_config.json`.
+
+
+### Usage Example
+Here's an example of how to load a video processor with [`llava-hf/llava-onevision-qwen2-0.5b-ov-hf`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf) model:
+
+```python
+from transformers import AutoVideoProcessor
+
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
+```
+
+Currently, if using base image processor for videos, it processes video data by treating each frame as an individual image and applying transformations frame-by-frame. While functional, this approach is not highly efficient. Using `AutoVideoProcessor` allows us to take advantage of **fast video processors**, leveraging the [torchvision](https://pytorch.org/vision/stable/index.html) library. Fast processors handle the whole batch of videos at once, without iterating over each video or frame. These updates introduce GPU acceleration and significantly enhance processing speed, especially for tasks requiring high throughput.
+
+Fast video processors are available for all models and are loaded by default when an `AutoVideoProcessor` is initialized. When using a fast video processor, you can also set the `device` argument to specify the device on which the processing should be done. By default, the processing is done on the same device as the inputs if the inputs are tensors, or on the CPU otherwise. For even more speed improvement, we can compile the processor when using 'cuda' as device.
+
+```python
+import torch
+from transformers.video_utils import load_video
+from transformers import AutoVideoProcessor
+
+video = load_video("video.mp4")
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", device="cuda")
+processor = torch.compile(processor)
+processed_video = processor(video, return_tensors="pt")
+```
+
+
+## BaseVideoProcessor
+
+[[autodoc]] video_processing_utils.BaseVideoProcessor
+

docs/source/en/model_doc/albert.md

@@ -57,6 +57,7 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). This
 - Embedding size E is different from hidden size H justified because the embeddings are context independent (one embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V being the vocab size). If E < H, it has less parameters.
 - Layers are split in groups that share parameters (to save memory).
 Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have been swapped or not.
+- The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`  
 
 ### Using Scaled Dot Product Attention (SDPA)
 

docs/source/en/model_doc/aria.md

@@ -102,6 +102,10 @@ response = processor.decode(output_ids, skip_special_tokens=True)
 
 [[autodoc]] AriaTextModel
 
+## AriaModel
+
+[[autodoc]] AriaModel
+
 ## AriaTextForCausalLM
 
 [[autodoc]] AriaTextForCausalLM

docs/source/en/model_doc/auto.md

@@ -74,6 +74,10 @@ Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
 
 [[autodoc]] AutoImageProcessor
 
+## AutoVideoProcessor
+
+[[autodoc]] AutoVideoProcessor
+
 ## AutoProcessor
 
 [[autodoc]] AutoProcessor

docs/source/en/model_doc/aya_vision.md

@@ -237,6 +237,10 @@ for i, output in enumerate(batch_outputs):
 
 [[autodoc]] AyaVisionConfig
 
+## AyaVisionModel
+
+[[autodoc]] AyaVisionModel
+
 ## AyaVisionForConditionalGeneration
 
 [[autodoc]] AyaVisionForConditionalGeneration

docs/source/en/model_doc/bamba.md

@@ -39,7 +39,7 @@ Checkout all Bamba-9B model checkpoints [here](https://github.com/foundation-mod
 <!---
 ## Usage Tips
 
-Tips: 
+Tips:
 
 - The architecture is based on Mamba-2 models.
 
@@ -63,7 +63,35 @@ response = model.generate(**inputs, max_new_tokens=64)
 print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
 ```
 
+
+## Padding-Free Training
+
+Bamba supports padding-free training in which distinct training examples can be concatenated
+together while nevertheless processing the inputs as though they belonged to separate batches. When
+the examples are of varying lengths, padding-free training can provide significant speed ups and
+memory savings compared to batching the examples together and using padding, as the unnecessary
+compute and memory due to padding is avoided entirely. The performance gains depend on factors such
+as the model and the data distribution, but throughput gains up to [~2x are commonly
+seen](https://github.com/huggingface/transformers/pull/35861#issue-2807873129).
+
+Using padding-free training with Bamba requires the `flash-attn`, `mamba-ssm`, and `causal-conv1d`
+packages, and the following arguments must be passed to the model in addition to `input_ids` and
+`labels`:
+* `position_ids: torch.LongTensor`: the position index of each token in each sequence.
+* `seq_idx: torch.IntTensor`: the index of each sequence in the batch.
+* Each of the [`FlashAttentionKwargs`]
+    * `cu_seq_lens_q: torch.LongTensor`: The cumulative sequence lengths of all queries.
+    * `cu_seq_lens_k: torch.LongTensor`: The cumulative sequence lengths of all keys.
+    * `max_length_q: int`: the longest query length in the batch.
+    * `max_length_k: int`: the longest key length in the batch.
+
+The `attention_mask` inputs should not be provided. The [`DataCollatorWithFlattening`] can be used
+to programmatically generate the above set of additional arguments using `return_seq_idx=True` and
+`return_flash_attn_kwargs=True`. See [this blog post](https://huggingface.co/blog/packing-with-FA2)
+for additional information.
+
+
 [[autodoc]] BambaForCausalLM
     - forward
 
-This HF implementation is contributed by [ani300](https://github.com/ani300) and [fabianlim](https://github.com/fabianlim). 
+This HF implementation is contributed by [ani300](https://github.com/ani300) and [fabianlim](https://github.com/fabianlim).

docs/source/en/model_doc/bart.md

@@ -55,6 +55,7 @@ This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The
   * mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
   * permute sentences
   * rotate the document to make it start at a specific token
+- The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`  
 
 ## Implementation Notes
 

docs/source/en/model_doc/beit.md

@@ -151,6 +151,12 @@ If you're interested in submitting a resource to be included here, please feel f
     - preprocess
     - post_process_semantic_segmentation
 
+## BeitImageProcessorFast
+
+[[autodoc]] BeitImageProcessorFast
+    - preprocess
+    - post_process_semantic_segmentation
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/bert.md

@@ -81,10 +81,10 @@ print(f"The predicted token is: {predicted_token}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers-cli run --task fill-mask --model google-bert/bert-base-uncased --device 0
+echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0
 ```
 
 </hfoption>
@@ -256,4 +256,4 @@ echo -e "Plants create [MASK] through a process known as photosynthesis." | tran
 
 [[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
 
-[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
+[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput

docs/source/en/model_doc/biogpt.md

@@ -36,6 +36,7 @@ This model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The
 - BioGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left.
 - BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
 - The model can take the `past_key_values` (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.
+- The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`  
 
 ### Using Scaled Dot Product Attention (SDPA)
 

docs/source/en/model_doc/bitnet.md

@@ -0,0 +1,121 @@
+<!--Copyright 2025 The BitNet Team 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.
+
+-->
+
+# BitNet
+
+## Overview
+
+Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
+
+➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://arxiv.org/abs/2504.12285)
+
+➡️ **Official Inference Code:** [microsoft/BitNet (bitnet.cpp)](https://github.com/microsoft/BitNet)
+
+## Model Variants
+
+Several versions of the model weights are available on Hugging Face:
+
+* [**`microsoft/bitnet-b1.58-2B-4T`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T): Contains the packed 1.58-bit weights optimized for efficient inference. **Use this for deployment.**
+
+* [**`microsoft/bitnet-b1.58-2B-4T-bf16`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-bf16): Contains the master weights in BF16 format. **Use this only for training or fine-tuning purposes.**
+
+* [**`microsoft/bitnet-b1.58-2B-4T-gguf`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf): Contains the model weights in GGUF format, compatible with the `bitnet.cpp` library for CPU inference.
+
+
+### Model Details
+
+
+* **Architecture:** Transformer-based, modified with `BitLinear` layers (BitNet framework).
+    * Uses Rotary Position Embeddings (RoPE).
+    * Uses squared ReLU (ReLU²) activation in FFN layers.
+    * Employs [`subln`](https://proceedings.mlr.press/v202/wang23u.html) normalization.
+    * No bias terms in linear or normalization layers.
+* **Quantization:** Native 1.58-bit weights and 8-bit activations (W1.58A8).
+    * Weights are quantized to ternary values {-1, 0, +1} using absmean quantization during the forward pass.
+    * Activations are quantized to 8-bit integers using absmax quantization (per-token).
+    * **Crucially, the model was *trained from scratch* with this quantization scheme, not post-training quantized.**
+* **Parameters:** ~2 Billion
+* **Training Tokens:** 4 Trillion
+*   **Context Length:** Maximum sequence length of **4096 tokens**.
+    *   *Recommendation:* For optimal performance on tasks requiring very long contexts (beyond the pre-training length or for specialized long-reasoning tasks), we recommend performing intermediate long-sequence adaptation/training before the final fine-tuning stage.
+* **Training Stages:**
+    1.  **Pre-training:** Large-scale training on public text/code and synthetic math data using a two-stage learning rate and weight decay schedule.
+    2.  **Supervised Fine-tuning (SFT):** Fine-tuned on instruction-following and conversational datasets using sum loss aggregation and specific hyperparameter tuning.
+    3.  **Direct Preference Optimization (DPO):** Aligned with human preferences using preference pairs.
+* **Tokenizer:** LLaMA 3 Tokenizer (vocab size: 128,256).
+
+
+## Usage tips
+
+
+**VERY IMPORTANT NOTE ON EFFICIENCY**
+
+> Please do NOT expect performance efficiency gains (in terms of speed, latency, or energy consumption) when using this model with the standard transformers library.
+>
+> The current execution paths within transformers do not contain the specialized, highly optimized computational kernels required to leverage the advantages of the BitNet architecture. Running the model via transformers will likely result in inference speeds and energy usage comparable to, or potentially worse than, standard full-precision models within this framework on both CPU and GPU.
+>
+> While you might observe reduced memory usage due to the quantized weights, the primary computational efficiency benefits are not accessible through this standard transformers usage path.
+>
+> For achieving the efficiency benefits demonstrated in the technical paper, you MUST use the dedicated C++ implementation: [bitnet.cpp](https://github.com/microsoft/BitNet).
+
+### Requirements
+
+```bash
+pip install transformers
+```
+
+### Example
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "microsoft/bitnet-b1.58-2B-4T"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16
+)
+
+# Apply the chat template
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "How are you?"},
+]
+chat_input = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+# Generate response
+chat_outputs = model.generate(chat_input, max_new_tokens=50)
+response = tokenizer.decode(chat_outputs[0][chat_input.shape[-1]:], skip_special_tokens=True) # Decode only the response part
+print("\nAssistant Response:", response)
+```
+
+
+## BitNetConfig
+
+[[autodoc]] BitNetConfig
+
+## BitNetModel
+
+[[autodoc]] BitNetModel
+    - forward
+
+## BitNetForCausalLM
+
+[[autodoc]] BitNetForCausalLM
+    - forward

docs/source/en/model_doc/bridgetower.md

@@ -147,6 +147,11 @@ Tips:
 [[autodoc]] BridgeTowerImageProcessor
     - preprocess
 
+## BridgeTowerImageProcessorFast
+
+[[autodoc]] BridgeTowerImageProcessorFast
+    - preprocess
+
 ## BridgeTowerProcessor
 
 [[autodoc]] BridgeTowerProcessor

docs/source/en/model_doc/chinese_clip.md

@@ -90,6 +90,11 @@ Currently, following scales of pretrained Chinese-CLIP models are available on
 [[autodoc]] ChineseCLIPImageProcessor
     - preprocess
 
+## ChineseCLIPImageProcessorFast
+
+[[autodoc]] ChineseCLIPImageProcessorFast
+    - preprocess
+
 ## ChineseCLIPFeatureExtractor
 
 [[autodoc]] ChineseCLIPFeatureExtractor

docs/source/en/model_doc/code_llama.md

@@ -35,7 +35,7 @@ The example below demonstrates how to generate code with [`Pipeline`], or the [`
 
 <hfoptions id="usage">
 <hfoption id="Pipeline">
-    
+
 ```py
 import torch
 from transformers import pipeline
@@ -76,7 +76,7 @@ prompt = "# Function to calculate the factorial of a number\ndef factorial(n):"
 input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
 
 output = model.generate(
-    **input_ids, 
+    **input_ids,
     max_new_tokens=256,
     cache_implementation="static"
 )
@@ -92,10 +92,10 @@ print(filled_text)
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
-    
+<hfoption id="transformers CLI">
+
 ```bash
-echo -e "# Function to calculate the factorial of a number\ndef factorial(n):" | transformers-cli run --task text-generation --model meta-llama/CodeLlama-7b-hf --device 0
+echo -e "# Function to calculate the factorial of a number\ndef factorial(n):" | transformers run --task text-generation --model meta-llama/CodeLlama-7b-hf --device 0
 ```
 
 </hfoption>
@@ -146,7 +146,7 @@ visualizer("""def func(a, b):
 - Use the `<FILL_ME>` token where you want your input to be filled. The tokenizer splits this token to create a formatted input string that follows the [original training pattern](https://github.com/facebookresearch/codellama/blob/cb51c14ec761370ba2e2bc351374a79265d0465e/llama/generation.py#L402). This is more robust than preparing the pattern yourself.
     ```py
     from transformers import LlamaForCausalLM, CodeLlamaTokenizer
-    
+
     tokenizer = CodeLlamaTokenizer.from_pretrained("meta-llama/CodeLlama-7b-hf")
     model = LlamaForCausalLM.from_pretrained("meta-llama/CodeLlama-7b-hf")
     PROMPT = '''def remove_non_ascii(s: str) -> str:
@@ -155,7 +155,7 @@ visualizer("""def func(a, b):
     '''
     input_ids = tokenizer(PROMPT, return_tensors="pt")["input_ids"]
     generated_ids = model.generate(input_ids, max_new_tokens=128)
-    
+
     filling = tokenizer.batch_decode(generated_ids[:, input_ids.shape[1]:], skip_special_tokens = True)[0]
     print(PROMPT.replace("<FILL_ME>", filling))
     ```

docs/source/en/model_doc/cohere.md

@@ -49,9 +49,9 @@ model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", t
 messages = [{"role": "user", "content": "How do plants make energy?"}]
 input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 output = model.generate(
-    input_ids, 
-    max_new_tokens=100, 
-    do_sample=True, 
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
     temperature=0.3,
     cache_implementation="static",
 )
@@ -59,11 +59,11 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
 # pip install -U flash-attn --no-build-isolation
-transformers-cli chat --model_name_or_path CohereForAI/c4ai-command-r-v01 --torch_dtype auto --attn_implementation flash_attention_2
+transformers chat CohereForAI/c4ai-command-r-v01 --torch_dtype auto --attn_implementation flash_attention_2
 ```
 
 </hfoption>
@@ -85,9 +85,9 @@ model = AutoModelForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", t
 messages = [{"role": "user", "content": "How do plants make energy?"}]
 input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 output = model.generate(
-    input_ids, 
-    max_new_tokens=100, 
-    do_sample=True, 
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
     temperature=0.3,
     cache_implementation="static",
 )

docs/source/en/model_doc/colpali.md

@@ -1,5 +1,4 @@
 <!--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
 
@@ -9,76 +8,134 @@ Unless required by applicable law or agreed to in writing, software distributed
 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
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
 rendered properly in your Markdown viewer.
-
 -->
 
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
 # ColPali
 
-<div class="flex flex-wrap space-x-1">
-<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
-</div>
+[ColPali](https://huggingface.co/papers/2407.01449) is a model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColPali treats each page as an image. It uses [Paligemma-3B](./paligemma) to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed embeddings. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.
 
-## Overview
+You can find all the original ColPali checkpoints under the [ColPali](https://huggingface.co/collections/vidore/hf-native-colvision-models-6755d68fc60a8553acaa96f7) collection.
 
-The *ColPali* model was proposed in [ColPali: Efficient Document Retrieval with Vision Language Models](https://doi.org/10.48550/arXiv.2407.01449) by **Manuel Faysse***, **Hugues Sibille***, **Tony Wu***, Bilel Omrani, Gautier Viaud, Céline Hudelot, Pierre Colombo (* denotes equal contribution). Work lead by ILLUIN Technology.
+> [!TIP]
+> Click on the ColPali models in the right sidebar for more examples of how to use ColPali for image retrieval.
 
-In our proposed *ColPali* approach, we leverage VLMs to construct efficient multi-vector embeddings directly from document images (“screenshots”) for document retrieval. We train the model to maximize the similarity between these document embeddings and the corresponding query embeddings, using the late interaction method introduced in ColBERT.
+<hfoptions id="usage">
+<hfoption id="image retrieval">
 
-Using *ColPali* removes the need for potentially complex and brittle layout recognition and OCR pipelines with a single model that can take into account both the textual and visual content (layout, charts, etc.) of a document.
-
-## Resources
-
-- The *ColPali* arXiv paper can be found [here](https://doi.org/10.48550/arXiv.2407.01449). 📄
-- The official blog post detailing ColPali can be found [here](https://huggingface.co/blog/manu/colpali). 📝
-- The original model implementation code for the ColPali model and for the `colpali-engine` package can be found [here](https://github.com/illuin-tech/colpali). 🌎
-- Cookbooks for learning to use the transformers-native version of *ColPali*, fine-tuning, and similarity maps generation can be found [here](https://github.com/tonywu71/colpali-cookbooks). 📚
-
-This model was contributed by [@tonywu71](https://huggingface.co/tonywu71) and [@yonigozlan](https://huggingface.co/yonigozlan).
-
-## Usage
-
-This example demonstrates how to use *ColPali* to embed both queries and images, calculate their similarity scores, and identify the most relevant matches. For a specific query, you can retrieve the top-k most similar images by selecting the ones with the highest similarity scores.
-
-```python
+```py
+import requests
 import torch
 from PIL import Image
-
 from transformers import ColPaliForRetrieval, ColPaliProcessor
 
-model_name = "vidore/colpali-v1.2-hf"
-
+# Load model (bfloat16 support is limited; fallback to float32 if needed)
 model = ColPaliForRetrieval.from_pretrained(
-    model_name,
-    torch_dtype=torch.bfloat16,
-    device_map="cuda:0",  # or "mps" if on Apple Silicon
+    "vidore/colpali-v1.2-hf",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",  # "cpu", "cuda", or "mps" for Apple Silicon
 ).eval()
 
 processor = ColPaliProcessor.from_pretrained(model_name)
 
-# Your inputs (replace dummy images with screenshots of your documents)
+url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
+url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
+
 images = [
-    Image.new("RGB", (32, 32), color="white"),
-    Image.new("RGB", (16, 16), color="black"),
+    Image.open(requests.get(url1, stream=True).raw),
+    Image.open(requests.get(url2, stream=True).raw),
 ]
+
 queries = [
-    "What is the organizational structure for our R&D department?",
-    "Can you provide a breakdown of last year’s financial performance?",
+    "Who printed the edition of Romeo and Juliet?",
+    "When was the United States Declaration of Independence proclaimed?",
 ]
 
 # Process the inputs
-batch_images = processor(images=images).to(model.device)
-batch_queries = processor(text=queries).to(model.device)
+inputs_images = processor(images=images, return_tensors="pt").to(model.device)
+inputs_text = processor(text=queries, return_tensors="pt").to(model.device)
 
 # Forward pass
 with torch.no_grad():
-    image_embeddings = model(**batch_images).embeddings
-    query_embeddings = model(**batch_queries).embeddings
+    image_embeddings = model(**inputs_images).embeddings
+    query_embeddings = model(**inputs_text).embeddings
 
-# Score the queries against the images
 scores = processor.score_retrieval(query_embeddings, image_embeddings)
+
+print("Retrieval scores (query x image):")
+print(scores)
 ```
+</hfoption>
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes.md) to quantize the weights to int4.
+
+```py
+import requests
+import torch
+from PIL import Image
+from transformers import ColPaliForRetrieval, ColPaliProcessor
+from transformers import BitsAndBytesConfig
+
+# 4-bit quantization configuration
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+
+model_name = "vidore/colpali-v1.2-hf"
+
+# Load model 
+model = ColPaliForRetrieval.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="cuda"
+).eval()
+
+processor = ColPaliProcessor.from_pretrained(model_name)
+
+url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
+url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
+
+images = [
+    Image.open(requests.get(url1, stream=True).raw),
+    Image.open(requests.get(url2, stream=True).raw),
+]
+
+queries = [
+    "Who printed the edition of Romeo and Juliet?",
+    "When was the United States Declaration of Independence proclaimed?",
+]
+
+# Process the inputs
+inputs_images = processor(images=images, return_tensors="pt").to(model.device)
+inputs_text = processor(text=queries, return_tensors="pt").to(model.device)
+
+# Forward pass
+with torch.no_grad():
+    image_embeddings = model(**inputs_images).embeddings
+    query_embeddings = model(**inputs_text).embeddings
+
+scores = processor.score_retrieval(query_embeddings, image_embeddings)
+
+print("Retrieval scores (query x image):")
+print(scores)
+```
+
+## Notes
+
+- [`~ColPaliProcessor.score_retrieval`] returns a 2D tensor where the first dimension is the number of queries and the second dimension is the number of images. A higher score indicates more similarity between the query and image.
 
 ## ColPaliConfig
 

docs/source/en/model_doc/conditional_detr.md

@@ -48,6 +48,11 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
 
 [[autodoc]] ConditionalDetrImageProcessor
     - preprocess
+
+## ConditionalDetrImageProcessorFast
+
+[[autodoc]] ConditionalDetrImageProcessorFast
+    - preprocess
     - post_process_object_detection
     - post_process_instance_segmentation
     - post_process_semantic_segmentation

docs/source/en/model_doc/csm.md

@@ -0,0 +1,377 @@
+<!--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.
+
+-->
+
+# Csm
+
+## Overview
+
+The Conversational Speech Model (CSM) is the first open-source contextual text-to-speech model [released by Sesame](https://www.sesame.com/research/crossing_the_uncanny_valley_of_voice). It is designed to generate natural-sounding speech with or without conversational context. This context typically consists of multi-turn dialogue between speakers, represented as sequences of text and corresponding spoken audio.
+
+**Model Architecture:**
+CSM is composed of two LLaMA-style auto-regressive transformer decoders: a backbone decoder that predicts the first codebook token and a depth decoder that generates the remaining tokens. It uses the pretrained codec model [Mimi](./mimi.md), introduced by Kyutai, to encode speech into discrete codebook tokens and decode them back into audio.
+
+The original csm-1b checkpoint is available under the [Sesame](https://huggingface.co/sesame/csm-1b) organization on Hugging Face.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/eustlb/documentation-images/resolve/main/csm_architecture.png"/>
+</div>
+
+## Usage Tips
+
+### Without Conversational Context
+
+CSM can be used to simply generate speech from a text prompt:
+
+```python
+import torch
+from transformers import CsmForConditionalGeneration, AutoProcessor
+
+model_id = "eustlb/csm-1b"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load the model and the processor
+processor = AutoProcessor.from_pretrained(model_id)
+model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
+
+# prepare the inputs
+text = "[0]The past is just a story we tell ourselves." # `[0]` for speaker id 0
+inputs = processor(text, add_special_tokens=True).to(device)
+
+# another equivalent way to prepare the inputs
+conversation = [
+    {"role": "0", "content": [{"type": "text", "text": "The past is just a story we tell ourselves."}]},
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+).to(device)
+
+# infer the model
+audio = model.generate(**inputs, output_audio=True)
+processor.save_audio(audio, "example_without_context.wav")
+```
+
+### With Conversational Context
+
+CSM can be used to generate speech given a conversation, allowing consistency in the voices and content-aware generation:
+
+```python
+import torch
+from transformers import CsmForConditionalGeneration, AutoProcessor
+from datasets import load_dataset, Audio
+
+model_id = "eustlb/csm-1b"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load the model and the processor
+processor = AutoProcessor.from_pretrained(model_id)
+model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
+
+# prepare the inputs
+ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
+# ensure the audio is 24kHz
+ds = ds.cast_column("audio", Audio(sampling_rate=24000))
+conversation = []
+
+# 1. context
+for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
+    conversation.append(
+        {
+            "role": f"{speaker_id}",
+            "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
+        }
+    )
+
+# 2. text prompt
+conversation.append({"role": f"{ds[4]['speaker_id']}", "content": [{"type": "text", "text": ds[4]["text"]}]})
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+).to(device)
+
+# infer the model
+audio = model.generate(**inputs, output_audio=True)
+processor.save_audio(audio, "example_with_context.wav")
+```
+
+### Batched Inference
+
+CSM supports batched inference!
+
+```python
+import torch
+from transformers import CsmForConditionalGeneration, AutoProcessor
+from datasets import load_dataset, Audio
+
+model_id = "eustlb/csm-1b"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load the model and the processor
+processor = AutoProcessor.from_pretrained(model_id)
+model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
+
+# prepare the inputs 
+ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
+# ensure the audio is 24kHz
+ds = ds.cast_column("audio", Audio(sampling_rate=24000))
+# here a batch with two prompts
+conversation = [
+    [
+        {
+            "role": f"{ds[0]['speaker_id']}",
+            "content": [
+                {"type": "text", "text": ds[0]["text"]},
+                {"type": "audio", "path": ds[0]["audio"]["array"]},
+            ],
+        },
+        {
+            "role": f"{ds[1]['speaker_id']}",
+            "content": [
+                {"type": "text", "text": ds[1]["text"]},
+            ],
+        },
+    ],
+    [
+        {
+            "role": f"{ds[0]['speaker_id']}",
+            "content": [
+                {"type": "text", "text": ds[0]["text"]},
+            ],
+        }
+    ],
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+).to(device)
+
+audio = model.generate(**inputs, output_audio=True)
+processor.save_audio(audio, [f"speech_batch_idx_{i}.wav" for i in range(len(audio))])
+```
+
+### Making The Model Go Brrr
+
+CSM supports full-graph compilation with CUDA graphs!
+
+```python
+import torch
+import copy
+from transformers import CsmForConditionalGeneration, AutoProcessor
+from datasets import load_dataset
+
+model_id = "eustlb/csm-1b"
+device = "cuda"
+
+# set logs to ensure no recompilation and graph breaks
+torch._logging.set_logs(graph_breaks=True, recompiles=True, cudagraphs=True)
+
+# load the model and the processor
+processor = AutoProcessor.from_pretrained(model_id)
+model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
+
+# use static cache, enabling automatically torch compile with fullgraph and reduce-overhead
+model.generation_config.max_length = 250 # big enough to avoid recompilation
+model.generation_config.max_new_tokens = None # would take precedence over max_length
+model.generation_config.cache_implementation = "static"
+model.depth_decoder.generation_config.cache_implementation = "static"
+
+# generation kwargs
+gen_kwargs = {
+    "do_sample": False,
+    "depth_decoder_do_sample": False,
+    "temperature": 1.0,
+    "depth_decoder_temperature": 1.0,
+}
+
+# Define a timing decorator
+class TimerContext:
+    def __init__(self, name="Execution"):
+        self.name = name
+        self.start_event = None
+        self.end_event = None
+        
+    def __enter__(self):
+        # Use CUDA events for more accurate GPU timing
+        self.start_event = torch.cuda.Event(enable_timing=True)
+        self.end_event = torch.cuda.Event(enable_timing=True)
+        self.start_event.record()
+        return self
+
+    def __exit__(self, *args):
+        self.end_event.record()
+        torch.cuda.synchronize()
+        elapsed_time = self.start_event.elapsed_time(self.end_event) / 1000.0
+        print(f"{self.name} time: {elapsed_time:.4f} seconds")
+
+# prepare the inputs 
+ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
+
+conversation = [
+    {
+        "role": f"{ds[0]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[0]["text"]},
+            {"type": "audio", "path": ds[0]["audio"]["array"]},
+        ],
+    },
+    {
+        "role": f"{ds[1]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[1]["text"]},
+            {"type": "audio", "path": ds[1]["audio"]["array"]},
+        ],
+    },
+    {
+        "role": f"{ds[2]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[2]["text"]},
+        ],
+    },
+]
+
+padded_inputs_1 = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+).to(device)
+
+print("\n" + "="*50)
+print("First generation - compiling and recording CUDA graphs...")
+with TimerContext("First generation"):
+    _ = model.generate(**padded_inputs_1, **gen_kwargs)
+print("="*50)
+
+print("\n" + "="*50)
+print("Second generation - fast !!!")
+with TimerContext("Second generation"):
+    _ = model.generate(**padded_inputs_1, **gen_kwargs)
+print("="*50)
+
+# now with different inputs
+conversation = [
+    {
+        "role": f"{ds[0]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[2]["text"]},
+            {"type": "audio", "path": ds[2]["audio"]["array"]},
+        ],
+    },
+    {
+        "role": f"{ds[1]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[3]["text"]},
+            {"type": "audio", "path": ds[3]["audio"]["array"]},
+        ],
+    },
+    {
+        "role": f"{ds[2]['speaker_id']}",
+        "content": [
+            {"type": "text", "text": ds[4]["text"]},
+        ],
+    },
+]
+padded_inputs_2 = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+).to(device)
+
+print("\n" + "="*50)
+print("Generation with other inputs!")
+with TimerContext("Generation with different inputs"):
+    _ = model.generate(**padded_inputs_2, **gen_kwargs)
+print("="*50)
+```
+
+### Training
+
+CSM Transformers integration supports training!
+
+```python
+from transformers import CsmForConditionalGeneration, AutoProcessor
+from datasets import load_dataset, Audio
+
+model_id = "eustlb/csm-1b"
+device = "cuda"
+
+# load the model and the processor
+processor = AutoProcessor.from_pretrained(model_id)
+model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
+model.train()
+
+ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
+# ensure the audio is 24kHz
+ds = ds.cast_column("audio", Audio(sampling_rate=24000))
+conversation = []
+
+# context
+for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
+    conversation.append(
+        {
+            "role": f"{speaker_id}",
+            "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
+        }
+    )
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    return_dict=True,
+    output_labels=True,
+).to(device)
+
+out = model(**inputs)
+out.loss.backward()
+```
+
+This model was contributed by [Eustache Le Bihan](https://huggingface.co/eustlb).
+The original code can be found [here](https://github.com/SesameAILabs/csm).
+
+
+## CsmConfig
+
+[[autodoc]] CsmConfig
+
+## CsmDepthDecoderConfig
+
+[[autodoc]] CsmDepthDecoderConfig
+
+## CsmProcessor
+
+[[autodoc]] CsmProcessor
+    - __call__
+
+## CsmForConditionalGeneration
+
+[[autodoc]] CsmForConditionalGeneration
+    - forward
+    - generate
+
+## CsmDepthDecoderForCausalLM
+
+[[autodoc]] CsmDepthDecoderForCausalLM
+
+## CsmDepthDecoderModel
+
+[[autodoc]] CsmDepthDecoderModel
+
+## CsmBackboneModel
+
+[[autodoc]] CsmBackboneModel

docs/source/en/model_doc/d_fine.md

@@ -0,0 +1,76 @@
+<!--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.
+
+-->
+
+# D-FINE
+
+## Overview
+
+The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
+Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
+
+The abstract from the paper is the following:
+
+*We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). 
+FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: this https URL.*
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber). 
+The original code can be found [here](https://github.com/Peterande/D-FINE).
+
+## Usage tips 
+
+```python
+>>> import torch
+>>> from transformers.image_utils import load_image
+>>> from transformers import DFineForObjectDetection, AutoImageProcessor
+
+>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+>>> image = load_image(url)
+
+>>> image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_coco")
+>>> model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_coco")
+
+>>> 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=[(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: 0.96 [344.49, 23.4, 639.84, 374.27]
+cat: 0.96 [11.71, 53.52, 316.64, 472.33]
+remote: 0.95 [40.46, 73.7, 175.62, 117.57]
+sofa: 0.92 [0.59, 1.88, 640.25, 474.74]
+remote: 0.89 [333.48, 77.04, 370.77, 187.3]
+```
+
+## DFineConfig
+
+[[autodoc]] DFineConfig
+
+## DFineModel
+
+[[autodoc]] DFineModel
+    - forward
+
+## DFineForObjectDetection
+
+[[autodoc]] DFineForObjectDetection
+    - forward

docs/source/en/model_doc/data2vec.md

@@ -53,6 +53,7 @@ The original code for vision can be found [here](https://github.com/facebookrese
 - For Data2VecAudio, preprocessing is identical to [`Wav2Vec2Model`], including feature extraction
 - For Data2VecText, preprocessing is identical to [`RobertaModel`], including tokenization.
 - For Data2VecVision, preprocessing is identical to [`BeitModel`], including feature extraction.
+- The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`  
 
 ### Using Scaled Dot Product Attention (SDPA)
 

docs/source/en/model_doc/dinov2.md

@@ -111,33 +111,68 @@ print("Predicted class:", model.config.id2label[predicted_class_idx])
 
 ## Notes
 
-- Use [torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) to speedup inference. However, it will produce some mismatched elements. The difference between the original and traced model is 1e-4.
+- The example below shows how to split the output tensor into:
+  - one embedding for the whole image, commonly referred to as a `CLS` token,
+    useful for classification and retrieval
+  - a set of local embeddings, one for each `14x14` patch of the input image,
+    useful for dense tasks, such as semantic segmentation
 
-    ```py
-    import torch
-    from transformers import AutoImageProcessor, AutoModel
-    from PIL import Image
-    import requests
+  ```py
+  from transformers import AutoImageProcessor, AutoModel
+  from PIL import Image
+  import requests
+  
+  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+  image = Image.open(requests.get(url, stream=True).raw)
+  print(image.height, image.width)  # [480, 640]
+  
+  processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
+  model = AutoModel.from_pretrained('facebook/dinov2-base')
+  patch_size = model.config.patch_size
+  
+  inputs = processor(images=image, return_tensors="pt")
+  print(inputs.pixel_values.shape)  # [1, 3, 224, 224]
+  batch_size, rgb, img_height, img_width = inputs.pixel_values.shape
+  num_patches_height, num_patches_width = img_height // patch_size, img_width // patch_size
+  num_patches_flat = num_patches_height * num_patches_width
+  
+  outputs = model(**inputs)
+  last_hidden_states = outputs[0]
+  print(last_hidden_states.shape)  # [1, 1 + 256, 768]
+  assert last_hidden_states.shape == (batch_size, 1 + num_patches_flat, model.config.hidden_size)
+  
+  cls_token = last_hidden_states[:, 0, :]
+  patch_features = last_hidden_states[:, 1:, :].unflatten(1, (num_patches_height, num_patches_width))
+  ```
 
-    url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
-    image = Image.open(requests.get(url, stream=True).raw)
+- Use [torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) to speedup inference.
+  However, it will produce some mismatched elements. The difference between the original and traced model is 1e-4.
 
-    processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
-    model = AutoModel.from_pretrained('facebook/dinov2-base')
-
-    inputs = processor(images=image, return_tensors="pt")
-    outputs = model(**inputs)
-    last_hidden_states = outputs[0]
-
-    # We have to force return_dict=False for tracing
-    model.config.return_dict = False
-
-    with torch.no_grad():
-        traced_model = torch.jit.trace(model, [inputs.pixel_values])
-        traced_outputs = traced_model(inputs.pixel_values)
-
-    print((last_hidden_states - traced_outputs[0]).abs().max())
-    ```
+  ```py
+  import torch
+  from transformers import AutoImageProcessor, AutoModel
+  from PIL import Image
+  import requests
+  
+  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+  image = Image.open(requests.get(url, stream=True).raw)
+  
+  processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
+  model = AutoModel.from_pretrained('facebook/dinov2-base')
+  
+  inputs = processor(images=image, return_tensors="pt")
+  outputs = model(**inputs)
+  last_hidden_states = outputs[0]
+  
+  # We have to force return_dict=False for tracing
+  model.config.return_dict = False
+  
+  with torch.no_grad():
+      traced_model = torch.jit.trace(model, [inputs.pixel_values])
+      traced_outputs = traced_model(inputs.pixel_values)
+  
+  print((last_hidden_states - traced_outputs[0]).abs().max())
+  ```
 
 ## Dinov2Config
 

docs/source/en/model_doc/distilbert.md

@@ -83,10 +83,10 @@ print(f"Predicted label: {predicted_label}")
 
 </hfoption>
 
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "I love using Hugging Face Transformers!" | transformers-cli run --task text-classification --model distilbert-base-uncased-finetuned-sst-2-english
+echo -e "I love using Hugging Face Transformers!" | transformers run --task text-classification --model distilbert-base-uncased-finetuned-sst-2-english
 ```
 
 </hfoption>
@@ -213,7 +213,3 @@ echo -e "I love using Hugging Face Transformers!" | transformers-cli run --task
 
 </jax>
 </frameworkcontent>
-
-
-
-

docs/source/en/model_doc/efficientnet.md

@@ -43,6 +43,11 @@ The original code can be found [here](https://github.com/tensorflow/tpu/tree/mas
 [[autodoc]] EfficientNetImageProcessor
     - preprocess
 
+## EfficientNetImageProcessorFast
+
+[[autodoc]] EfficientNetImageProcessorFast
+    - preprocess
+
 ## EfficientNetModel
 
 [[autodoc]] EfficientNetModel

docs/source/en/model_doc/electra.md

@@ -45,9 +45,9 @@ import torch
 from transformers import pipeline
 
 classifier = pipeline(
-    task="text-classification", 
-    model="bhadresh-savani/electra-base-emotion", 
-    torch_dtype=torch.float16, 
+    task="text-classification",
+    model="bhadresh-savani/electra-base-emotion",
+    torch_dtype=torch.float16,
     device=0
 )
 classifier("This restaurant has amazing food!")
@@ -64,7 +64,7 @@ tokenizer = AutoTokenizer.from_pretrained(
     "bhadresh-savani/electra-base-emotion",
 )
 model = AutoModelForSequenceClassification.from_pretrained(
-    "bhadresh-savani/electra-base-emotion", 
+    "bhadresh-savani/electra-base-emotion",
     torch_dtype=torch.float16
 )
 inputs = tokenizer("ELECTRA is more efficient than BERT", return_tensors="pt")
@@ -78,10 +78,10 @@ print(f"Predicted label: {predicted_label}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "This restaurant has amazing food." | transformers-cli run --task text-classification --model bhadresh-savani/electra-base-emotion --device 0
+echo -e "This restaurant has amazing food." | transformers run --task text-classification --model bhadresh-savani/electra-base-emotion --device 0
 ```
 
 </hfoption>
@@ -96,12 +96,12 @@ echo -e "This restaurant has amazing food." | transformers-cli run --task text-c
 
     ```py
     # Example of properly handling padding with attention masks
-    inputs = tokenizer(["Short text", "This is a much longer text that needs padding"], 
-                    padding=True, 
+    inputs = tokenizer(["Short text", "This is a much longer text that needs padding"],
+                    padding=True,
                     return_tensors="pt")
     outputs = model(**inputs)  # automatically uses the attention_mask
     ```
-    
+
 - When using the discriminator for a downstream task, you can load it into any of the ELECTRA model classes ([`ElectraForSequenceClassification`], [`ElectraForTokenClassification`], etc.).
 
 ## ElectraConfig

docs/source/en/model_doc/emu3.md

@@ -174,6 +174,10 @@ for i, image in enumerate(images['pixel_values']):
 [[autodoc]] Emu3TextModel
     - forward
 
+## Emu3Model
+
+[[autodoc]] Emu3Model
+
 ## Emu3ForCausalLM
 
 [[autodoc]] Emu3ForCausalLM

docs/source/en/model_doc/falcon.md

@@ -41,7 +41,7 @@ import torch
 from transformers import pipeline
 
 pipeline = pipeline(
-    task="text-generation", 
+    task="text-generation",
     model="tiiuae/falcon-7b-instruct",
     torch_dtype=torch.bfloat16,
     device=0
@@ -76,11 +76,11 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
 # pip install -U flash-attn --no-build-isolation
-transformers-cli chat --model_name_or_path tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
+transformers chat tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
 ```
 
 </hfoption>
@@ -150,4 +150,4 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## FalconForQuestionAnswering
 
 [[autodoc]] FalconForQuestionAnswering
-    - forward
+    - forward

docs/source/en/model_doc/falcon_h1.md

@@ -0,0 +1,65 @@
+<!--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.
+-->
+
+# FalconH1
+
+## Overview
+
+The FalconH1 model was developed by the TII Pretraining team. A comprehensive research paper covering the architecture, pretraining dynamics, experimental results, and conclusions is forthcoming. You can read more about this series in [this website](https://github.com/tiiuae/Falcon-H1).
+
+## Contributors
+
+This model was contributed by [DhiyaEddine](https://huggingface.co/DhiyaEddine), [ybelkada](https://huggingface.co/ybelkada), [JingweiZuo](https://huggingface.co/JingweiZuo), [IlyasChahed](https://huggingface.co/IChahed), and [MaksimVelikanov](https://huggingface.co/yellowvm).
+The original code can be found [here](https://github.com/tiiuae/Falcon-H1).
+
+
+## FalconH1Config
+
+| Model     | Depth | Dim  | Attn Heads | KV | Mamba Heads | d_head       | d_state | Ctx Len        |
+|-----------|--------|------|------------|----|--------------|--------------|------|-----------------|
+| H1 0.5B   | 36     | 1024 | 8          | 2  | 24           | 64 / 64      | 128  | 4K, 16K-SFT     |
+| H1 1.5B   | 24     | 2048 | 8          | 2  | 48           | 128 / 64     | 256  | 128K            |
+| H1 1.5B-d | 66     | 1280 | 6          | 2  | 24           | 128 / 64     | 256  | 128K            |
+| H1 3B     | 32     | 2560 | 10         | 2  | 32           | 128 / 128    | 256  | 128K            |
+| H1 7B     | 44     | 3072 | 12         | 2  | 24           | 128 / 128    | 256  | 256K            |
+| H1 34B    | 72     | 5120 | 20         | 4  | 32           | 128 / 128    | 256  | 256K            |
+
+
+
+[[autodoc]] FalconH1Config
+
+<!---
+## Usage Tips
+Tips: 
+- The architecture is based on Mamba-2 models.
+## FalconH1Model
+[[autodoc]] FalconH1Model
+    - forward
+-->
+
+## FalconH1ForCausalLM
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-H1-7B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-H1-7B-Instruct")
+
+message = ["Mamba is a snake with following properties  "]
+inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
+response = model.generate(**inputs, max_new_tokens=64)
+print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
+```
+
+[[autodoc]] FalconH1ForCausalLM
+    - forward
+
+This HF implementation is contributed by [younesbelkada](https://github.com/younesbelkada) and [DhiaEddineRhaiem](https://github.com/dhiaEddineRhaiem). 

docs/source/en/model_doc/falcon_mamba.md

@@ -39,7 +39,7 @@ import torch
 from transformers import pipeline
 
 pipeline = pipeline(
-    "text-generation", 
+    "text-generation",
     model="tiiuae/falcon-mamba-7b-instruct",
     torch_dtype=torch.bfloat16,
     device=0
@@ -73,10 +73,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-transformers-cli chat --model_name_or_path tiiuae/falcon-mamba-7b-instruct --torch_dtype auto --device 0
+transformers chat tiiuae/falcon-mamba-7b-instruct --torch_dtype auto --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/fuyu.md

@@ -103,6 +103,10 @@ The `LlamaTokenizer` is used as it is a standard wrapper around sentencepiece.
 
 [[autodoc]] FuyuConfig
 
+## FuyuModel
+
+[[autodoc]] FuyuModel
+
 ## FuyuForCausalLM
 
 [[autodoc]] FuyuForCausalLM

docs/source/en/model_doc/gemma.md

@@ -1,4 +1,5 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+<!--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
@@ -14,31 +15,146 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Gemma
-
-<div class="flex flex-wrap space-x-1">
-<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
-<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,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
-">
-<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
-<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
+        <img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,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
+        ">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
 </div>
 
-## Overview
+# Gemma
 
-The Gemma model was proposed in [Gemma: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/gemma-open-models/) by Gemma Team, Google.
-Gemma models are trained on 6T tokens, and released with 2 versions, 2b and 7b.
+[Gemma](https://huggingface.co/papers/2403.08295) is a family of lightweight language models with pretrained and instruction-tuned variants, available in 2B and 7B parameters. The architecture is based on a transformer decoder-only design. It features Multi-Query Attention, rotary positional embeddings (RoPE), GeGLU activation functions, and RMSNorm layer normalization.
 
-The abstract from the paper is the following:
+The instruction-tuned variant was fine-tuned with supervised learning on instruction-following data, followed by reinforcement learning from human feedback (RLHF) to align the model outputs with human preferences.
 
-*This work introduces Gemma, a new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of our model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations*
+You can find all the original Gemma checkpoints under the [Gemma](https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b) release.
 
-Tips:
 
-- The original checkpoints can be converted using the conversion script `src/transformers/models/gemma/convert_gemma_weights_to_hf.py` 
+> [!TIP]
+> Click on the Gemma models in the right sidebar for more examples of how to apply Gemma to different language tasks.
 
-This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Younes Belkada](https://huggingface.co/ybelkada), [Sanchit Gandhi](https://huggingface.co/sanchit-gandhi), [Pedro Cuenca](https://huggingface.co/pcuenq).
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class, and from the command line.
 
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+    task="text-generation",
+    model="google/gemma-2b",
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+)
+
+pipeline("LLMs generate text through a process known as", max_new_tokens=50)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+input_text = "LLMs generate text through a process known as"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=50, cache_implementation="static")
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "LLMs generate text through a process known as" | transformers run --task text-generation --model google/gemma-2b --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
+
+```py
+#!pip install bitsandbytes
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4"
+)
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-7b",
+    quantization_config=quantization_config,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+input_text = "LLMs generate text through a process known as."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **input_ids,
+    max_new_tokens=50,
+    cache_implementation="static"
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
+
+```py
+from transformers.utils.attention_visualizer import AttentionMaskVisualizer
+
+visualizer = AttentionMaskVisualizer("google/gemma-2b")
+visualizer("LLMs generate text through a process known as")
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/gemma-attn-mask.png"/>
+</div>
+
+## Notes
+
+- The original Gemma models support standard kv-caching used in many transformer-based language models. You can use use the default [`DynamicCache`] instance or a tuple of tensors for past key values during generation. This makes it compatible with typical autoregressive generation workflows.
+
+   ```py
+   import torch
+   from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
+
+   tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+   model = AutoModelForCausalLM.from_pretrained(
+       "google/gemma-2b",
+       torch_dtype=torch.bfloat16,
+       device_map="auto",
+       attn_implementation="sdpa"
+   )
+   input_text = "LLMs generate text through a process known as"
+   input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+   past_key_values = DynamicCache()
+   outputs = model.generate(**input_ids, max_new_tokens=50, past_key_values=past_key_values)
+   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+   ```
 
 ## GemmaConfig
 

docs/source/en/model_doc/gemma2.md

@@ -58,7 +58,7 @@ pipe("Explain quantum computing simply. ", max_new_tokens=50)
 
 </hfoption>
 <hfoption id="AutoModel">
-    
+
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -80,16 +80,16 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```
-echo -e "Explain quantum computing simply." | transformers-cli run --task text-generation --model google/gemma-2-2b --device 0
+echo -e "Explain quantum computing simply." | transformers run --task text-generation --model google/gemma-2-2b --device 0
 ```
 </hfoption>
 </hfoptions>
 
 Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
-	
+
 The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
 
 ```python
@@ -118,7 +118,7 @@ Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/bl
 ```python
 from transformers.utils.attention_visualizer import AttentionMaskVisualizer
 visualizer = AttentionMaskVisualizer("google/gemma-2b")
-visualizer("You are an assistant. Make sure you print me") 
+visualizer("You are an assistant. Make sure you print me")
 ```
 
 <div class="flex justify-center">
@@ -137,7 +137,7 @@ visualizer("You are an assistant. Make sure you print me")
 
     inputs = tokenizer(text="My name is Gemma", return_tensors="pt")
     max_generated_length = inputs.input_ids.shape[1] + 10
-    past_key_values = HybridCache(config=model.config, max_batch_size=1, 
+    past_key_values = HybridCache(config=model.config, max_batch_size=1,
     max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
     outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
     ```

docs/source/en/model_doc/gemma3.md

@@ -28,7 +28,7 @@ rendered properly in your Markdown viewer.
 
 The instruction-tuned variant was post-trained with knowledge distillation and reinforcement learning.
 
-You can find all the original Gemma 3 checkpoints under the [Gemma 3](https://huggingface.co/collections/meta-llama/llama-2-family-661da1f90a9d678b6f55773b) release.
+You can find all the original Gemma 3 checkpoints under the [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) release.
 
 > [!TIP]
 > Click on the Gemma 3 models in the right sidebar for more examples of how to apply Gemma to different vision and language tasks.
@@ -99,10 +99,10 @@ print(processor.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model google/gemma-3-1b-pt --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model google/gemma-3-1b-pt --device 0
 ```
 
 </hfoption>
@@ -254,6 +254,10 @@ visualizer("<img>What is shown in this image?")
 [[autodoc]] Gemma3TextModel
     - forward
 
+## Gemma3Model
+
+[[autodoc]] Gemma3Model
+
 ## Gemma3ForCausalLM
 
 [[autodoc]] Gemma3ForCausalLM

docs/source/en/model_doc/got_ocr2.md

@@ -277,6 +277,10 @@ alt="drawing" width="600"/>
 
 [[autodoc]] GotOcr2Processor
 
+## GotOcr2Model
+
+[[autodoc]] GotOcr2Model
+
 ## GotOcr2ForConditionalGeneration
 
 [[autodoc]] GotOcr2ForConditionalGeneration

docs/source/en/model_doc/gpt2.md

@@ -64,15 +64,21 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Hello, I'm a language model" | transformers-cli run --task text-generation --model openai-community/gpt2 --device 0
+echo -e "Hello, I'm a language model" | transformers run --task text-generation --model openai-community/gpt2 --device 0
 ```
 
 </hfoption>
 </hfoptions>
 
+One can also serve the model using vLLM with the `transformers backend`.
+
+```
+vllm serve openai-community/gpt2 --model-imp transformers
+```
+
 Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
 
 The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.
@@ -82,16 +88,16 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
 
 quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,  
-    bnb_4bit_quant_type="nf4",  
-    bnb_4bit_compute_dtype="float16",  
-    bnb_4bit_use_double_quant=True 
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype="float16",
+    bnb_4bit_use_double_quant=True
 )
 
 model = AutoModelForCausalLM.from_pretrained(
     "openai-community/gpt2-xl",
     quantization_config=quantization_config,
-    device_map="auto"  
+    device_map="auto"
 )
 
 tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-xl")

docs/source/en/model_doc/gpt_bigcode.md

@@ -46,8 +46,12 @@ The main differences compared to GPT2.
 - Merge the key and value caches into one (this changes the format of layer_past/ present, does it risk creating problems?)
 - Use the memory layout (self.num_heads, 3, self.head_dim) instead of `(3, self.num_heads, self.head_dim)` for the QKV tensor with MHA. (prevents an overhead with the merged key and values, but makes the checkpoints incompatible with the original openai-community/gpt2 model).
 
+
 You can read more about the optimizations in the [original pull request](https://github.com/huggingface/transformers/pull/22575)
 
+> [!NOTE]
+> The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`
+
 ## Combining Starcoder and Flash Attention 2
 
 First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.

docs/source/en/model_doc/granitemoehybrid.md

@@ -0,0 +1,64 @@
+<!--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.
+
+-->
+
+# GraniteMoeHybrid
+
+## Overview
+
+
+The `GraniteMoeHybrid` model builds on top of `GraniteMoeSharedModel` and `Bamba`. Its decoding layers consist of state space layers or MoE attention layers with shared experts. By default, the attention layers do not use positional encoding.
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "ibm-granite/granite-4.0-tiny-preview"
+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 [Sukriti Sharma](https://huggingface.co/SukritiSharma) and [Alexander Brooks](https://huggingface.co/abrooks9944).
+
+
+## GraniteMoeHybridConfig
+
+[[autodoc]] GraniteMoeHybridConfig
+
+## GraniteMoeHybridModel
+
+[[autodoc]] GraniteMoeHybridModel
+    - forward
+
+## GraniteMoeHybridForCausalLM
+
+[[autodoc]] GraniteMoeHybridForCausalLM
+    - forward

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

@@ -102,6 +102,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] GroundingDinoImageProcessor
     - preprocess
+
+## GroundingDinoImageProcessorFast
+
+[[autodoc]] GroundingDinoImageProcessorFast
+    - preprocess
     - post_process_object_detection
 
 ## GroundingDinoProcessor

docs/source/en/model_doc/hgnet_v2.md

@@ -0,0 +1,46 @@
+<!--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.
+
+-->
+
+# HGNet-V2
+
+## Overview
+
+A HGNet-V2 (High Performance GPU Net) image classification model.
+HGNet arhtictecture was proposed in [HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction](https://arxiv.org/abs/2407.01097) by
+Zhan Chen, Chen Tang, Lu Xiong
+
+The abstract from the HGNET paper is the following:
+
+*Predicting the motion of multiple traffic participants has always been one of the most challenging tasks in autonomous driving. The recently proposed occupancy flow field prediction method has shown to be a more effective and scalable representation compared to general trajectory prediction methods. However, in complex multi-agent traffic scenarios, it remains difficult to model the interactions among various factors and the dependencies among prediction outputs at different time steps. In view of this, we propose a transformer-based hierarchical feature guided network (HGNET), which can efficiently extract features of agents and map information from visual and vectorized inputs, modeling multimodal interaction relationships. Second, we design the Feature-Guided Attention (FGAT) module to leverage the potential guiding effects between different prediction targets, thereby improving prediction accuracy. Additionally, to enhance the temporal consistency and causal relationships of the predictions, we propose a Time Series Memory framework to learn the conditional distribution models of the prediction outputs at future time steps from multivariate time series. The results demonstrate that our model exhibits competitive performance, which ranks 3rd in the 2024 Waymo Occupancy and Flow Prediction Challenge.*
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber). 
+The original code can be found [here](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py).
+
+## HGNetV2Config
+
+[[autodoc]] HGNetV2Config
+
+
+## HGNetV2Backbone
+
+[[autodoc]] HGNetV2Backbone
+    - forward
+
+
+## HGNetV2ForImageClassification
+
+[[autodoc]] HGNetV2ForImageClassification
+    - forward

docs/source/en/model_doc/hubert.md

@@ -50,7 +50,7 @@ This model was contributed by [patrickvonplaten](https://huggingface.co/patrickv
 - Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
 - Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
   using [`Wav2Vec2CTCTokenizer`].
-
+- The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`  
 
 ## Using Flash Attention 2
 

docs/source/en/model_doc/instructblip.md

@@ -69,6 +69,10 @@ The attributes can be obtained from model config, as `model.config.num_query_tok
 [[autodoc]] InstructBlipQFormerModel
     - forward
 
+## InstructBlipModel
+
+[[autodoc]] InstructBlipModel
+
 ## InstructBlipForConditionalGeneration
 
 [[autodoc]] InstructBlipForConditionalGeneration

docs/source/en/model_doc/instructblipvideo.md

@@ -58,6 +58,12 @@ The attributes can be obtained from model config, as `model.config.num_query_tok
 
 [[autodoc]] InstructBlipVideoProcessor
 
+
+## InstructBlipVideoVideoProcessor
+
+[[autodoc]] InstructBlipVideoVideoProcessor
+    - preprocess
+
 ## InstructBlipVideoImageProcessor
 
 [[autodoc]] InstructBlipVideoImageProcessor
@@ -73,6 +79,10 @@ The attributes can be obtained from model config, as `model.config.num_query_tok
 [[autodoc]] InstructBlipVideoQFormerModel
     - forward
 
+## InstructBlipVideoModel
+[[autodoc]] InstructBlipVideoModel
+    - forward
+
 ## InstructBlipVideoForConditionalGeneration
 
 [[autodoc]] InstructBlipVideoForConditionalGeneration

docs/source/en/model_doc/internvl.md

@@ -0,0 +1,359 @@
+<!--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.
+
+-->
+
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    </div>
+</div>
+
+# InternVL
+
+The InternVL3 family of Visual Language Models was introduced in [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://huggingface.co/papers/2504.10479).
+
+The abstract from the paper is the following:
+
+*We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and linguistic capabilities from both diverse multimodal data and pure-text corpora during a single pre-training stage. This unified training paradigm effectively addresses the complexities and alignment challenges commonly encountered in conventional post-hoc training pipelines for MLLMs. To further improve performance and scalability, InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure. Extensive empirical evaluations demonstrate that InternVL3 delivers superior performance across a wide range of multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its capabilities remain highly competitive with leading proprietary models, including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also maintaining strong pure-language proficiency. In pursuit of open-science principles, we will publicly release both the training data and model weights to foster further research and development in next-generation MLLMs.*
+
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/internvl_architecture.png" alt="drawing" width="600"/>
+
+<small> Overview of InternVL3 models architecture, which is the same as InternVL2.5. Taken from the <a href="https://huggingface.co/OpenGVLab/InternVL3-1B">original checkpoint.</a> </small>
+
+
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/internvl_overview_performance.png" alt="drawing" width="600"/>
+
+<small> Comparison of InternVL3 performance on OpenCompass against other SOTA VLLMs. Taken from the <a href="https://huggingface.co/OpenGVLab/InternVL3-1B">original checkpoint.</a> </small>
+
+
+
+This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan).
+The original code can be found [here](https://github.com/OpenGVLab/InternVL).
+
+## Usage example
+
+### Inference with Pipeline
+
+Here is how you can use the `image-text-to-text` pipeline to perform inference with the `InternVL3` models in just a few lines of code:
+
+```python
+>>> from transformers import pipeline
+
+>>> messages = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {
+...                 "type": "image",
+...                 "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
+...             },
+...             {"type": "text", "text": "Describe this image."},
+...         ],
+...     },
+... ]
+
+>>> pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3-1B-hf")
+>>> outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
+>>> outputs[0]["generated_text"]
+'The image showcases a vibrant scene of nature, featuring several flowers and a bee. \n\n1. **Foreground Flowers**: \n   - The primary focus is on a large, pink cosmos flower with a prominent yellow center. The petals are soft and slightly r'
+```
+### Inference on a single image
+
+This example demonstrates how to perform inference on a single image with the InternVL models using chat templates.
+
+> [!NOTE]
+> Note that the model has been trained with a specific prompt format for chatting. Use `processor.apply_chat_template(my_conversation_dict)` to correctly format your prompts.
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText
+>>> import torch
+
+>>> torch_device = "cuda"
+>>> model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+
+>>> messages = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+...             {"type": "text", "text": "Please describe the image explicitly."},
+...         ],
+...     }
+... ]
+
+>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+
+>>> generate_ids = model.generate(**inputs, max_new_tokens=50)
+>>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+
+>>> decoded_output
+'The image shows two cats lying on a pink blanket. The cat on the left is a tabby with a mix of brown, black, and white fur, and it appears to be sleeping with its head resting on the blanket. The cat on the'
+```
+
+### Text-only generation
+This example shows how to generate text using the InternVL model without providing any image input.
+
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText
+>>> import torch
+
+>>> torch_device = "cuda"
+>>> model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+
+>>> messages = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {"type": "text", "text": "Write a haiku"},
+...         ],
+...     }
+... ]
+
+>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device, dtype=torch.bfloat16)
+
+>>> generate_ids = model.generate(**inputs, max_new_tokens=50)
+>>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+
+>>> print(decoded_output)
+"Whispers of dawn,\nSilent whispers of the night,\nNew day's light begins."
+```
+
+### Batched image and text inputs
+InternVL models also support batched image and text inputs.
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText
+>>> import torch
+
+>>> torch_device = "cuda"
+>>> model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+
+>>> messages = [
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+...                 {"type": "text", "text": "Write a haiku for this image"},
+...             ],
+...         },
+...     ],
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
+...                 {"type": "text", "text": "Describe this image"},
+...             ],
+...         },
+...     ],
+... ]
+
+
+>>> inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+
+>>> output = model.generate(**inputs, max_new_tokens=25)
+
+>>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
+>>> decoded_outputs
+["user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace.",
+ 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of']
+```
+
+### Batched multi-image input
+This implementation of the InternVL models supports batched text-images inputs with different number of images for each text.
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText
+>>> import torch
+
+>>> torch_device = "cuda"
+>>> model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+
+>>> messages = [
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+...                 {"type": "text", "text": "Write a haiku for this image"},
+...             ],
+...         },
+...     ],
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
+...                 {"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"},
+...                 {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
+...             ],
+...         },
+...     ],
+>>> ]
+
+>>> inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+
+>>> output = model.generate(**inputs, max_new_tokens=25)
+
+>>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
+>>> decoded_outputs
+["user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace.",
+ 'user\n\n\nThese images depict two different landmarks. Can you identify them?\nassistant\nYes, these images depict the Statue of Liberty and the Golden Gate Bridge.']
+```
+
+### Video input
+InternVL models can also handle video inputs. Here is an example of how to perform inference on a video input using chat templates.
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
+
+>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
+>>> quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, quantization_config=quantization_config)
+
+>>> messages = [
+...     {
+...         "role": "user",
+...         "content": [
+...             {
+...                 "type": "video",
+...                 "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+...             },
+...             {"type": "text", "text": "What type of shot is the man performing?"},
+...         ],
+...     }
+>>> ]
+>>> inputs = processor.apply_chat_template(
+...     messages,
+...     return_tensors="pt",
+...     add_generation_prompt=True,
+...     tokenize=True,
+...     return_dict=True,
+...     num_frames=8,
+>>> ).to(model.device, dtype=torch.float16)
+
+>>> output = model.generate(**inputs, max_new_tokens=25)
+
+>>> decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+>>> decoded_output
+'The man is performing a forehand shot.'
+```
+
+### Interleaved image and video inputs
+This example showcases how to handle a batch of chat conversations with interleaved image and video inputs using chat template.
+
+```python
+>>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
+>>> import torch
+
+>>> torch_device = "cuda"
+>>> model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
+>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+
+>>> messages = [
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
+...                 {"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"},
+...                 {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
+...             ],
+...         },
+...     ],
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
+...                 {"type": "text", "text": "What type of shot is the man performing?"},
+...             ],
+...         },
+...     ],
+...     [
+...         {
+...             "role": "user",
+...             "content": [
+...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+...                 {"type": "text", "text": "Write a haiku for this image"},
+...             ],
+...         },
+...     ],
+>>> ]
+>>> inputs = processor.apply_chat_template(
+...     messages,
+...     padding=True,
+...     add_generation_prompt=True,
+...     tokenize=True,
+...     return_dict=True,
+...     return_tensors="pt",
+>>> ).to(model.device, dtype=torch.bfloat16)
+
+>>> outputs = model.generate(**inputs, max_new_tokens=25)
+
+>>> decoded_outputs = processor.batch_decode(outputs, skip_special_tokens=True)
+>>> decoded_outputs
+['user\n\n\nThese images depict two different landmarks. Can you identify them?\nassistant\nThe images depict the Statue of Liberty and the Golden Gate Bridge.',
+ 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot',
+ "user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace."]
+```
+
+## InternVLVisionConfig
+
+[[autodoc]] InternVLVisionConfig
+
+## InternVLConfig
+
+[[autodoc]] InternVLConfig
+
+## InternVLVisionModel
+
+[[autodoc]] InternVLVisionModel
+    - forward
+
+## InternVLModel
+
+[[autodoc]] InternVLModel
+    - forward
+
+## InternVLForConditionalGeneration
+
+[[autodoc]] InternVLForConditionalGeneration
+    - forward
+
+## InternVLProcessor
+
+[[autodoc]] InternVLProcessor
+
+## InternVLVideoProcessor
+
+[[autodoc]] InternVLVideoProcessor

docs/source/en/model_doc/jamba.md

@@ -75,10 +75,10 @@ output = model.generate(**input_ids, cache_implementation="static")
 print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model ai21labs/AI21-Jamba-Mini-1.6 --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model ai21labs/AI21-Jamba-Mini-1.6 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/janus.md

@@ -0,0 +1,230 @@
+<!--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.
+
+-->
+
+# Janus
+
+## Overview
+
+The Janus Model was originally proposed in [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) by DeepSeek AI team and later refined in [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://arxiv.org/abs/2501.17811). Janus is a vision-language model that can generate both image and text output, it can also take both images and text as input.
+
+> [!NOTE]
+> The model doesn't generate both images and text in an interleaved format. The user has to pass a parameter indicating whether to generate text or image.
+
+The abstract from the original paper is the following:
+
+*In this paper, we introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.*
+
+The abstract from the aforementioned `Janus-Pro` paper, released afterwards, is the following:
+
+*In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strate (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.*
+
+This model was contributed by [Yaswanth Gali](https://huggingface.co/yaswanthgali) and [Hugo Silva](https://huggingface.co/hugosilva664).
+The original code can be found [here](https://github.com/deepseek-ai/Janus).
+
+## Usage Example
+
+### Single image inference
+
+Here is the example of visual understanding with a single image.
+
+> [!NOTE]
+> Note that the model has been trained with a specific prompt format for chatting. Use `processor.apply_chat_template(my_conversation_dict)` to correctly format your prompts.
+
+```python
+import torch  
+from PIL import Image  
+import requests  
+
+from transformers import JanusForConditionalGeneration, JanusProcessor  
+
+model_id = "deepseek-community/Janus-Pro-1B"
+# Prepare Input for generation.
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {'type':'image', 'url': 'http://images.cocodataset.org/val2017/000000039769.jpg'},
+            {'type':"text", "text":"What do you see in this image?."}
+        ]
+    },
+]
+
+# Set generation mode to `text` to perform text generation.
+processor = JanusProcessor.from_pretrained(model_id)
+model = JanusForConditionalGeneration.from_pretrained(model_id,     
+        torch_dtype=torch.bfloat16,
+        device_map="auto")
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    generation_mode="text",
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device, dtype=torch.bfloat16)
+
+output = model.generate(**inputs, max_new_tokens=40,generation_mode='text',do_sample=True)
+text = processor.decode(output[0], skip_special_tokens=True)
+print(text)
+```
+
+### Multi image inference
+
+Janus can perform inference with multiple images as input, where images can belong to the same prompt or different prompts in batched inference, where the model processes many conversations in parallel. Here is how you can do it:
+
+```python
+import torch
+from PIL import Image
+import requests
+
+from transformers import JanusForConditionalGeneration, JanusProcessor
+
+model_id = "deepseek-community/Janus-Pro-1B"
+
+image_urls = [
+    "http://images.cocodataset.org/val2017/000000039769.jpg",
+    "https://www.ilankelman.org/stopsigns/australia.jpg",
+    "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
+]
+
+messages = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What’s the difference between"},
+                {"type": "image", "url": image_urls[0]},
+                {"type": "text", "text": " and "},
+                {"type": "image", "url": image_urls[1]}
+            ]
+        }
+    ],
+    [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": image_urls[2]},
+                {"type": "text", "text": "What do you see in this image?"}
+            ]
+        }
+    ]
+]
+
+# Load model and processor
+processor = JanusProcessor.from_pretrained(model_id)
+model = JanusForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    generation_mode="text",
+    tokenize=True,
+    padding=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+# Generate response
+output = model.generate(**inputs, max_new_tokens=40, generation_mode='text', do_sample=False)
+text = processor.batch_decode(output, skip_special_tokens=True)
+print(text)
+```
+
+## Text to Image generation
+
+Janus can also generate images given a prompt.
+
+```python
+import torch
+from transformers import JanusForConditionalGeneration, JanusProcessor
+
+# Set generation mode to `image` to prepare inputs for image generation..
+
+model_id = "deepseek-community/Janus-Pro-1B"
+processor = JanusProcessor.from_pretrained(model_id)
+model = JanusForConditionalGeneration.from_pretrained(model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "A dog running under the rain."},
+        ],
+     }
+]
+
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=prompt,generation_mode="image",return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+
+# Set num_return_sequence parameter to generate multiple images per prompt.
+model.generation_config.num_return_sequences = 2
+outputs = model.generate(**inputs,
+                         generation_mode="image",
+                         do_sample=True,
+                         use_cache=True,
+                         )
+# Perform post-processing on the generated token ids.
+decoded_image = model.decode_image_tokens(outputs)
+images = processor.postprocess(list(decoded_image.float()),return_tensors="PIL.Image.Image")
+# Save the image
+for i, image in enumerate(images['pixel_values']):
+    image.save(f"result{i}.png")
+```
+
+## JanusConfig
+
+[[autodoc]] JanusConfig
+
+## JanusVisionConfig
+
+[[autodoc]] JanusVisionConfig
+
+## JanusVQVAEConfig
+
+[[autodoc]] JanusVQVAEConfig
+
+## JanusProcessor
+
+[[autodoc]] JanusProcessor
+
+## JanusImageProcessor
+
+[[autodoc]] JanusImageProcessor
+
+## JanusVisionModel
+
+[[autodoc]] JanusVisionModel
+    - forward
+
+## JanusVQVAE
+
+[[autodoc]] JanusVQVAE
+    - forward
+
+## JanusModel
+
+[[autodoc]] JanusModel
+    - forward
+
+## JanusForConditionalGeneration
+
+[[autodoc]] JanusForConditionalGeneration
+    - forward

docs/source/en/model_doc/llama.md

@@ -74,10 +74,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model huggyllama/llama-7b --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model huggyllama/llama-7b --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/llama2.md

@@ -74,10 +74,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-transformers-cli chat --model_name_or_path meta-llama/Llama-2-7b-chat-hf --torch_dtype auto --attn_implementation flash_attention_2
+transformers chat meta-llama/Llama-2-7b-chat-hf --torch_dtype auto --attn_implementation flash_attention_2
 ```
 
 </hfoption>
@@ -175,4 +175,3 @@ visualizer("Plants create energy through a process known as")
 
 [[autodoc]] LlamaForSequenceClassification
     - forward
-

docs/source/en/model_doc/llava.md

@@ -256,6 +256,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] LlavaProcessor
 
+## LlavaModel
+
+[[autodoc]] LlavaModel
+
 ## LlavaForConditionalGeneration
 
 [[autodoc]] LlavaForConditionalGeneration

docs/source/en/model_doc/llava_next.md

@@ -315,6 +315,10 @@ model = AutoModelForImageTextToText.from_pretrained(
 
 [[autodoc]] LlavaNextProcessor
 
+## LlavaNextModel
+
+[[autodoc]] LlavaNextModel
+
 ## LlavaNextForConditionalGeneration
 
 [[autodoc]] LlavaNextForConditionalGeneration