v4.54.1

* fix gemma

* fix min

* fix quant init issue

* fix gemma 3n

* skip quant cache test

* fix modular

* new test for Gemma

* include cyril change

---------

Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>

.circleci/create_circleci_config.py

@@ -303,7 +303,7 @@ non_model_job = CircleCIJob(
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     # networkx==3.3 (after #36957) cause some issues
     # TODO: remove this once it works directly
-    install_steps=["uv venv && uv pip install ."],
+    install_steps=["uv venv && uv pip install .[serving]"],
     marker="not generate",
     parallelism=6,
 )

.github/workflows/build_documentation.yml

@@ -18,6 +18,10 @@ jobs:
       notebook_folder: transformers_doc
       languages: ar de en es fr hi it ko pt tr zh ja te
       custom_container: huggingface/transformers-doc-builder
+      # Temporary pin to work around datasets exception in the docbuilder.Remove after docker images and main have
+      # the right dependencies (which **should** be the case by 2025-07-20). See
+      # https://github.com/huggingface/transformers/actions/runs/16365952006/job/46243081358?pr=38545
+      pre_command: uv pip install datasets>=2.15.0
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -15,3 +15,7 @@ jobs:
       pr_number: ${{ github.event.number }}
       package: transformers
       languages: en
+      # Temporary pin to work around datasets exception in the docbuilder. Remove after docker images and main have
+      # the right dependencies (which **should** be the case by 2025-07-20). See
+      # https://github.com/huggingface/transformers/actions/runs/16365952006/job/46243081358?pr=38545
+      pre_command: uv pip install datasets>=2.15.0

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

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

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

@@ -0,0 +1,36 @@
+name: Get PR number
+on:
+  workflow_call:
+    outputs:
+      PR_NUMBER:
+        description: "The extracted PR number"
+        value: ${{ jobs.get-pr-number.outputs.PR_NUMBER }}
+
+jobs:
+  get-pr-number:
+    runs-on: ubuntu-22.04
+    name: Get PR number
+    outputs:
+      PR_NUMBER: ${{ steps.set_pr_number.outputs.PR_NUMBER }}
+    steps:
+      - name: Get PR number
+        shell: bash
+        run: |
+          if [[ "${{ github.event.issue.number }}" != "" && "${{ github.event.issue.pull_request }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.issue.number }}" >> $GITHUB_ENV
+          elif [[ "${{ github.event.pull_request.number }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.pull_request.number }}" >> $GITHUB_ENV
+          elif [[ "${{ github.event.pull_request }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.number }}" >> $GITHUB_ENV
+          else
+            echo "PR_NUMBER=" >> $GITHUB_ENV
+          fi
+
+      - name: Check PR number
+        shell: bash
+        run: |
+          echo "${{ env.PR_NUMBER }}"
+
+      - name: Set PR number
+        id: set_pr_number
+        run: echo "PR_NUMBER=${{ env.PR_NUMBER }}" >> "$GITHUB_OUTPUT"

.github/workflows/pr_run_slow_ci.yml

@@ -0,0 +1,199 @@
+name: PR slow CI
+on:
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  get-pr-number:
+    name: Get PR number
+    uses: ./.github/workflows/get-pr-number.yml
+
+  get-pr-info:
+    name: Get PR commit SHA
+    needs: get-pr-number
+    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
+    uses: ./.github/workflows/get-pr-info.yml
+    with:
+      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
+
+  # We only need to verify the timestamp if the workflow is triggered by `issue_comment`.
+  verity_pr_commit:
+    name: Verity PR commit corresponds to a specific event by comparing timestamps
+    if: ${{ github.event.comment.created_at != '' }}
+    runs-on: ubuntu-22.04
+    needs: get-pr-info
+    env:
+      COMMENT_DATE: ${{ github.event.comment.created_at }}
+      PR_MERGE_COMMIT_DATE: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
+      PR_MERGE_COMMIT_TIMESTAMP: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
+    steps:
+      - run: |
+          COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
+          echo "COMMENT_DATE: $COMMENT_DATE"
+          echo "PR_MERGE_COMMIT_DATE: $PR_MERGE_COMMIT_DATE"
+          echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
+          echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP"
+          if [ $COMMENT_TIMESTAMP -le $PR_MERGE_COMMIT_TIMESTAMP ]; then
+            echo "Last commit on the pull request is newer than the issue comment triggering this run! Abort!";
+            exit -1;
+          fi
+
+  get-jobs:
+    name: Get test files to run
+    runs-on: ubuntu-22.04
+    needs: [get-pr-number, get-pr-info]
+    outputs:
+      jobs: ${{ steps.get_jobs.outputs.jobs_to_run }}
+    steps:
+      - name: Get repository content
+        id: repo_content
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const { data: tests_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            const { data: tests_models_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests/models',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            const { data: tests_quantization_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests/quantization',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            core.setOutput('tests_dir', tests_dir);
+            core.setOutput('tests_models_dir', tests_models_dir);
+            core.setOutput('tests_quantization_dir', tests_quantization_dir);
+
+      # This checkout to the main branch
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: "0"
+
+      - name: Write pr_files file
+        run: |
+          cat > pr_files.txt << 'EOF'
+          ${{ needs.get-pr-info.outputs.PR_FILES }}
+          EOF
+
+      - name: Write tests_dir file
+        run: |
+          cat > tests_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_dir }}
+          EOF
+
+      - name: Write tests_models_dir file
+        run: |
+          cat > tests_models_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_models_dir }}
+          EOF
+
+      - name: Write tests_quantization_dir file
+        run: |
+          cat > tests_quantization_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_quantization_dir }}
+          EOF
+
+      - name: Run script to get jobs to run
+        id: get_jobs
+        run: |
+          python utils/get_pr_run_slow_jobs.py | tee output.txt
+          echo "jobs_to_run: $(tail -n 1 output.txt)"
+          echo "jobs_to_run=$(tail -n 1 output.txt)" >> $GITHUB_OUTPUT
+
+  send_comment:
+    # Will delete the previous comment and send a new one if:
+    #   - either the content is changed
+    #   - or the previous comment is 30 minutes or more old
+    name: Send a comment to suggest jobs to run
+    if: ${{ needs.get-jobs.outputs.jobs != '' }}
+    needs: [get-pr-number, get-jobs]
+    permissions:
+      pull-requests: write
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check and update comment if needed
+        uses: actions/github-script@v7
+        env:
+          BODY: "\n\nrun-slow: ${{ needs.get-jobs.outputs.jobs }}"
+        with:
+          script: |
+            const prNumber = ${{ needs.get-pr-number.outputs.PR_NUMBER }};
+            const commentPrefix = "**[For maintainers]** Suggested jobs to run (before merge)";
+            const thirtyMinutesAgo = new Date(Date.now() - 30 * 60 * 1000); // 30 minutes ago
+            const newBody = `${commentPrefix}${process.env.BODY}`;
+            
+            // Get all comments on the PR
+            const { data: comments } = await github.rest.issues.listComments({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: prNumber
+            });
+            
+            // Find existing comments that start with our prefix
+            const existingComments = comments.filter(comment => 
+              comment.user.login === 'github-actions[bot]' && 
+              comment.body.startsWith(commentPrefix)
+            );
+            
+            let shouldCreateNewComment = true;
+            let commentsToDelete = [];
+            
+            if (existingComments.length > 0) {
+              // Get the most recent comment
+              const mostRecentComment = existingComments
+                .sort((a, b) => new Date(b.created_at) - new Date(a.created_at))[0];
+              
+              const commentDate = new Date(mostRecentComment.created_at);
+              const isOld = commentDate < thirtyMinutesAgo;
+              const isDifferentContent = mostRecentComment.body !== newBody;
+              
+              console.log(`Most recent comment created: ${mostRecentComment.created_at}`);
+              console.log(`Is older than 30 minutes: ${isOld}`);
+              console.log(`Has different content: ${isDifferentContent}`);
+              
+              if (isOld || isDifferentContent) {
+                // Delete all existing comments and create new one
+                commentsToDelete = existingComments;
+                console.log(`Will delete ${commentsToDelete.length} existing comment(s) and create new one`);
+              } else {
+                // Content is same and comment is recent, skip
+                shouldCreateNewComment = false;
+                console.log('Comment is recent and content unchanged, skipping update');
+              }
+            } else {
+              console.log('No existing comments found, will create new one');
+            }
+            
+            // Delete old comments if needed
+            for (const comment of commentsToDelete) {
+              console.log(`Deleting comment #${comment.id} (created: ${comment.created_at})`);
+              await github.rest.issues.deleteComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                comment_id: comment.id
+              });
+            }
+            
+            // Create new comment if needed
+            if (shouldCreateNewComment) {
+              await github.rest.issues.createComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: prNumber,
+                body: newBody
+              });
+              console.log('✅ New comment created');
+            } else {
+              console.log('ℹ️ No comment update needed');
+            }

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

@@ -29,7 +29,7 @@ jobs:
     runs-on: ubuntu-22.04
     name: Get PR number
     # For security: only allow team members to run
-    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "vasqu", "ivarflakstad"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
+    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "vasqu", "ivarflakstad", "stevhliu"]'), 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:

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

@@ -84,8 +84,6 @@ jobs:
       machine_type: ${{ matrix.machine_type }}
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
-      report_name_prefix: run_models_gpu
-
     secrets: inherit
 
   run_trainer_and_fsdp_gpu:
@@ -104,11 +102,10 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
       report_name_prefix: run_trainer_and_fsdp_gpu
-
     secrets: inherit
 
-  run_pipelines_gpu:
-    if: ${{ inputs.job == 'run_pipelines_gpu' }}
+  run_pipelines_torch_gpu:
+    if: ${{ inputs.job == 'run_pipelines_torch_gpu' }}
     name: Pipelines
     strategy:
       fail-fast: false
@@ -161,20 +158,20 @@ jobs:
 
       - name: Run all pipeline tests on Intel Gaudi
         run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_pipelines_gpu_test_reports tests/pipelines -m "not not_device_test"
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines -m "not not_device_test"
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
         run: |
-          cat reports/${{ env.machine_type }}_run_pipelines_gpu_test_reports/failures_short.txt
+          cat reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_pipelines_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_pipelines_gpu_test_reports
+          name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
+          path: reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
 
   run_examples_gpu:
     if: ${{ inputs.job == 'run_examples_gpu' }}
@@ -248,8 +245,8 @@ jobs:
           name: ${{ env.machine_type }}_run_examples_gpu_test_reports
           path: reports/${{ env.machine_type }}_run_examples_gpu_test_reports
 
-  run_deepspeed_gpu:
-    if: ${{ inputs.job == 'run_deepspeed_gpu' }}
+  run_torch_cuda_extensions_gpu:
+    if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }}
     name: Intel Gaudi deepspeed tests
     strategy:
       fail-fast: false
@@ -305,20 +302,20 @@ jobs:
 
       - name: Run all deepspeed tests on intel Gaudi
         run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_deepspeed_gpu_test_reports tests/deepspeed -m "not not_device_test"
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed -m "not not_device_test"
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
         run: |
-          cat reports/${{ env.machine_type }}_run_deepspeed_gpu_test_reports/failures_short.txt
+          cat reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_deepspeed_gpu_test_reports"
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
-          name: ${{ env.machine_type }}_run_deepspeed_gpu_test_reports
-          path: reports/${{ env.machine_type }}_run_deepspeed_gpu_test_reports
+          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
 
   send_results:
     name: Slack Report
@@ -327,8 +324,8 @@ jobs:
         setup,
         run_models_gpu,
         run_examples_gpu,
-        run_pipelines_gpu,
-        run_deepspeed_gpu,
+        run_torch_cuda_extensions_gpu,
+        run_pipelines_torch_gpu,
         run_trainer_and_fsdp_gpu,
       ]
     if: ${{ always() }}

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

@@ -23,7 +23,7 @@ jobs:
     name: Pipeline CI
     uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
     with:
-      job: run_pipelines_gpu
+      job: run_pipelines_torch_gpu
       ci_event: Scheduled CI (Intel) - Gaudi3
       runner_scale_set: itac-bm-emr-gaudi3-dell
       slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
@@ -47,7 +47,7 @@ jobs:
     name: DeepSpeed CI
     uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
     with:
-      job: run_deepspeed_gpu
+      job: run_torch_cuda_extensions_gpu
       ci_event: Scheduled CI (Intel) - Gaudi3
       runner_scale_set: itac-bm-emr-gaudi3-dell
       slack_report_channel: "#transformers-ci-daily-intel-gaudi3"

.github/workflows/self-scheduled.yml

@@ -135,6 +135,7 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       machine_type: ${{ matrix.machine_type }}
       slice_id: ${{ matrix.slice_id }}
+      runner_map: ${{ needs.setup.outputs.runner_map }}
       docker: ${{ inputs.docker }}
       report_name_prefix: run_trainer_and_fsdp_gpu
     secrets: inherit

.gitignore

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

README.md

@@ -44,7 +44,7 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ja.md">日本語</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_hd.md">हिन्दी</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ru.md">Русский</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Рortuguês</a> |
+        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Português</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_te.md">తెలుగు</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_fr.md">Français</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_de.md">Deutsch</a> |

conftest.py

@@ -28,6 +28,7 @@ from transformers.testing_utils import HfDoctestModule, HfDocTestParser
 
 NOT_DEVICE_TESTS = {
     "test_tokenization",
+    "test_tokenization_mistral_common",
     "test_processor",
     "test_processing",
     "test_beam_constraints",

docker/examples-torch.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --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/pipeline-torch.dockerfile

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --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

@@ -2,10 +2,10 @@ FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
 RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --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/transformers-all-latest-gpu/Dockerfile

@@ -26,10 +26,12 @@ RUN git clone https://github.com/huggingface/transformers && cd transformers &&
 # 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future.
 # 2. Regarding `torch` part, We might need to specify proper versions for `torchvision` and `torchaudio`.
 #    Currently, let's not bother to specify their versions explicitly (so installed with their latest release versions).
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA && python3 -m pip uninstall -y tensorflow tensorflow_text tensorflow_probability
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA && python3 -m pip uninstall -y tensorflow tensorflow_text tensorflow_probability
 
 RUN python3 -m pip uninstall -y flax jax
 
+RUN python3 -m pip install --no-cache-dir -U timm
+
 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"
 

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,10 +1,10 @@
-FROM rocm/pytorch:rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.6.0
+FROM rocm/pytorch:rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.7.1
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TORCH_VISION='0.21.0'
-ARG TORCH_AUDIO='2.6.0'
+ARG TORCH_VISION='0.22.0'
+ARG TORCH_AUDIO='2.7.0'
 
 RUN apt update && \
     apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs && \

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

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

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

@@ -19,7 +19,7 @@ RUN python3 -m pip uninstall -y torch torchvision torchaudio
 # Install **nightly** release PyTorch (flag `--pre`)
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
+RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
 # `datasets` requires pandas, pandas has some modules compiled with numpy=1.x causing errors
 RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'pandas<2' 'numpy<2'

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -26,7 +26,7 @@ RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch';
 RUN echo torch=$VERSION
 # `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build.
 # Currently, let's just use their latest releases (when `torch` is installed with a release version)
-RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
@@ -78,6 +78,9 @@ RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submod
 # RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
 # RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git
 
+# Add fp-quant for quantization testing
+RUN python3 -m pip install --no-cache-dir "fp-quant>=0.1.6"
+
 # Add compressed-tensors for quantization testing
 RUN python3 -m pip install --no-cache-dir compressed-tensors
 

docs/source/ar/custom_models.md

@@ -280,7 +280,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 الآن لإرسال النموذج إلى Hub، تأكد من تسجيل الدخول. إما تشغيل في المحطة الأوامر الطرفية الخاصة بك:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 أو من دفتر ملاحظات:

docs/source/ar/model_sharing.md

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

docs/source/ar/run_scripts.md

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

docs/source/de/model_sharing.md

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

docs/source/de/run_scripts.md

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

docs/source/en/_toctree.yml

@@ -72,8 +72,6 @@
       title: Caching
     - local: kv_cache
       title: KV cache strategies
-    - local: serving
-      title: Serving
     - local: llm_tutorial_optimization
       title: Getting the most out of LLMs
     - local: perplexity
@@ -100,13 +98,15 @@
       title: Distributed inference
     - local: perf_infer_cpu
       title: CPU
-    - local: tf_xla
-      title: XLA
     title: Optimization
   - local: agents
     title: Agents
   - local: tools
     title: Tools
+  - local: serving
+    title: Serving
+  - local: transformers_as_backend
+    title: Inference server backends
   title: Inference
 - isExpanded: false
   sections:
@@ -141,8 +141,6 @@
       title: GPU
     - local: perf_train_cpu
       title: CPU
-    - local: perf_train_tpu_tf
-      title: TPU
     - local: perf_train_special
       title: Apple Silicon
     - local: perf_train_gaudi
@@ -181,6 +179,8 @@
     title: FBGEMM
   - local: quantization/finegrained_fp8
     title: Fine-grained FP8
+  - local: quantization/fp_quant
+    title: FP-Quant
   - local: gguf
     title: GGUF
   - local: quantization/gptq
@@ -433,6 +433,8 @@
         title: DiffLlama
       - local: model_doc/distilbert
         title: DistilBERT
+      - local: model_doc/doge
+        title: Doge
       - local: model_doc/dots1
         title: dots1
       - local: model_doc/dpr
@@ -443,10 +445,16 @@
         title: Encoder Decoder Models
       - local: model_doc/ernie
         title: ERNIE
+      - local: model_doc/ernie4_5
+        title: Ernie4_5
+      - local: model_doc/ernie4_5_moe
+        title: Ernie4_5_MoE
       - local: model_doc/ernie_m
         title: ErnieM
       - local: model_doc/esm
         title: ESM
+      - local: model_doc/exaone4
+        title: EXAONE-4.0
       - local: model_doc/falcon
         title: Falcon
       - local: model_doc/falcon3
@@ -477,6 +485,8 @@
         title: GLM
       - local: model_doc/glm4
         title: glm4
+      - local: model_doc/glm4_moe
+        title: glm4_moe
       - local: model_doc/openai-gpt
         title: GPT
       - local: model_doc/gpt_neo
@@ -519,6 +529,8 @@
         title: Jukebox
       - local: model_doc/led
         title: LED
+      - local: model_doc/lfm2
+        title: LFM2
       - local: model_doc/llama
         title: LLaMA
       - local: model_doc/llama2
@@ -563,6 +575,8 @@
         title: MobileBERT
       - local: model_doc/modernbert
         title: ModernBert
+      - local: model_doc/modernbert-decoder
+        title: ModernBERTDecoder
       - local: model_doc/mpnet
         title: MPNet
       - local: model_doc/mpt
@@ -685,6 +699,8 @@
         title: XLM-V
       - local: model_doc/xlnet
         title: XLNet
+      - local: model_doc/xlstm
+        title: xLSTM
       - local: model_doc/yoso
         title: YOSO
       - local: model_doc/zamba
@@ -693,6 +709,8 @@
         title: Zamba2
       title: Text models
     - sections:
+      - local: model_doc/aimv2
+        title: Aimv2
       - local: model_doc/beit
         title: BEiT
       - local: model_doc/bit
@@ -709,6 +727,12 @@
         title: D-FINE
       - local: model_doc/dab-detr
         title: DAB-DETR
+      - local: model_doc/deepseek_v2
+        title: DeepSeek-V2
+      - local: model_doc/deepseek_vl
+        title: DeepseekVL
+      - local: model_doc/deepseek_vl_hybrid
+        title: DeepseekVLHybrid
       - local: model_doc/deformable_detr
         title: Deformable DETR
       - local: model_doc/deit
@@ -735,6 +759,8 @@
         title: DPT
       - local: model_doc/efficientformer
         title: EfficientFormer
+      - local: model_doc/efficientloftr
+        title: EfficientLoFTR
       - local: model_doc/efficientnet
         title: EfficientNet
       - local: model_doc/eomt
@@ -957,6 +983,8 @@
         title: Donut
       - local: model_doc/emu3
         title: Emu3
+      - local: model_doc/evolla
+        title: Evolla
       - local: model_doc/flava
         title: FLAVA
       - local: model_doc/gemma3
@@ -1035,6 +1063,8 @@
         title: PaliGemma
       - local: model_doc/perceiver
         title: Perceiver
+      - local: model_doc/perception_lm
+        title: PerceptionLM
       - local: model_doc/phi4_multimodal
         title: Phi4 Multimodal
       - local: model_doc/pix2struct
@@ -1087,6 +1117,8 @@
         title: Vision Text Dual Encoder
       - local: model_doc/visual_bert
         title: VisualBERT
+      - local: model_doc/voxtral
+        title: Voxtral
       - local: model_doc/xclip
         title: X-CLIP
       title: Multimodal models
@@ -1144,4 +1176,3 @@
       title: Environment Variables
     title: Reference
   title: API
-

docs/source/en/agents.md

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

docs/source/en/attention_interface.md

@@ -60,11 +60,11 @@ You will see it prints "I just entered the attention computation" as many times
 
 ## Dynamically switching attention function
 
-You could dynamically change the model's attention function as well, by overriding the `config._attn_implementation` field:
+You could dynamically change the model's attention function as well:
 
 ```python
 # Back to use original sdpa implementation
-model.config._attn_implementation = "sdpa"
+model.set_attn_implementation("sdpa")
 
 model(torch.ones(1, 5, dtype=int))
 ```
@@ -72,6 +72,34 @@ model(torch.ones(1, 5, dtype=int))
 and it will stop printing the statements, as it now uses the `sdpa` attention.  
 This allows to quickly change an attention function, without needing to reload the model!
 
+## Different attention per backbone in multimodal models
+
+For multimodal models different attention functions may work better for each backbone module. For example, some vision backbones perform better in fp32, but are incompatible with FlashAttention. To continue using FlashAttention while keeping the vision encoder in fp32, create a dict and map each config to an attention implementation as shown below.
+
+```python
+from transformers import AutoModelForImageTextToText
+
+model_id = "facebook/chameleon-7b"
+
+attention_implementation_per_backbone = {"vision_config": "sdpa", "text_config": "flash_attention_2"}
+model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation=attention_implementation_per_backbone)
+
+# NOTE: keys in the attention implementation have to be the same as the sub-config names
+for key in attention_implementation_per_backbone:
+    assert key in model.config.sub_configs, f"Invalid key in `attention_implementation`"
+
+# You can omit certain backbones - the default attention function (SDPA) will be used
+# This is equivalent to the previous example
+model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation={"text_config": "flash_attention_2"})
+
+
+# Set the same attention implementation for all backbones with single string, same as in non-multimodal models
+model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation="eager")
+
+# Alternatively use a dict with an empty key for global configuration
+model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation={"": "eager"})
+```
+
 ## What about new args needed in my custom attention function?
 
 But indeed, what if the new function requires a new arg to be properly used? It's no issue! Models supporting the

docs/source/en/auto_docstring.md

@@ -64,9 +64,9 @@ Arguments can also be passed directly to `@auto_docstring` for more control. Use
     It builds upon the standard Transformer architecture with unique modifications.""",
     custom_args="""
     custom_parameter (`type`, *optional*, defaults to `default_value`):
-        A concise description for custom_parameter if not defined or overriding the description in `args_doc.py`.
+        A concise description for custom_parameter if not defined or overriding the description in `auto_docstring.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`.
+        A concise description for internal_helper_arg if not defined or overriding the description in `auto_docstring.py`.
     """
 )
 class MySpecialModel(PreTrainedModel):
@@ -85,13 +85,40 @@ 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`.
+            A concise description for custom_parameter if not defined or overriding the description in `auto_docstring.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`.
+            A concise description for internal_helper_arg if not defined or overriding the description in `auto_docstring.py`.
         """
         # ...
 ```
 
+You should also use the `@auto_docstring` decorator for classes that inherit from [`~utils.ModelOutput`].
+
+```python
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Custom model outputs with additional fields.
+    """
+)
+class MyModelOutput(ImageClassifierOutput):
+    r"""
+    loss (`torch.FloatTensor`, *optional*):
+        The loss of the model.
+    custom_field (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*):
+        A custom output field specific to this model.
+    """
+
+    # Standard fields like hidden_states, logits, attentions etc. can be automatically documented if the description is the same as the standard arguments.
+    # However, given that the loss docstring is often different per model, you should document it in the docstring above.
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+    # Custom fields need to be documented in the docstring above
+    custom_field: Optional[torch.FloatTensor] = None
+```
+
 </hfoption>
 <hfoption id="functions">
 
@@ -171,7 +198,7 @@ class MyModel(PreTrainedModel):
 
 There are some rules for documenting different types of arguments and they're listed below.
 
-- Standard arguments (`input_ids`, `attention_mask`, `pixel_values`, etc.) are defined and retrieved from `args_doc.py`. It is the single source of truth for standard arguments and should not be redefined locally if an argument's description and shape is the same as an argument in `args_doc.py`.
+- Standard arguments (`input_ids`, `attention_mask`, `pixel_values`, etc.) are defined and retrieved from `auto_docstring.py`. It is the single source of truth for standard arguments and should not be redefined locally if an argument's description and shape is the same as an argument in `auto_docstring.py`.
 
     If a standard argument behaves differently in your model, then you can override it locally in a `r""" """` block. This local definition has a higher priority. For example, the `labels` argument is often customized per model and typically requires overriding.
 
@@ -245,7 +272,7 @@ When working with modular files (`modular_model.py`), follow the guidelines belo
 The `@auto_docstring` decorator automatically generates docstrings by:
 
 1. Inspecting the signature (arguments, types, defaults) of the decorated class' `__init__` method or the decorated function.
-2. Retrieving the predefined docstrings for common arguments (`input_ids`, `attention_mask`, etc.) from internal library sources like [`ModelArgs`], [`ImageProcessorArgs`], and the `args_doc.py` file.
+2. Retrieving the predefined docstrings for common arguments (`input_ids`, `attention_mask`, etc.) from internal library sources like [`ModelArgs`], [`ImageProcessorArgs`], and the `auto_docstring.py` file.
 3. Adding argument descriptions in one of two ways as shown below.
 
     | method | description | usage |
@@ -253,7 +280,7 @@ The `@auto_docstring` decorator automatically generates docstrings by:
     | `r""" """` | add custom docstring content directly to a method signature or within the `__init__` docstring | document new arguments or override standard descriptions |
     | `custom_args` | add custom docstrings for specific arguments directly in `@auto_docstring` | define docstring for new arguments once if they're repeated in multiple places in the modeling file |
 
-4. Adding class and function descriptions. For model classes with standard naming patterns, like `ModelForCausalLM`, or if it belongs to a pipeline, `@auto_docstring` automatically generates the appropriate descriptions with `ClassDocstring` from `args_doc.py`.
+4. Adding class and function descriptions. For model classes with standard naming patterns, like `ModelForCausalLM`, or if it belongs to a pipeline, `@auto_docstring` automatically generates the appropriate descriptions with `ClassDocstring` from `auto_docstring.py`.
 
     `@auto_docstring` also accepts the `custom_intro` argument to describe a class or function.
 

docs/source/en/cache_explanation.md

@@ -82,24 +82,18 @@ When you use Transformers' [`Cache`] class, the self-attention module performs s
 
 ## Cache storage implementation
 
-The actual storage of key-value pairs varies between cache implementations. As an example, consider the [`DynamicCache`].
+Caches are structured as a list of layers, where each layer contains a key and value cache. The key and value caches are tensors with the shape `[batch_size, num_heads, seq_len, head_dim]`.
 
+Layers can be of different types (e.g. `DynamicLayer`, `StaticLayer`, `SlidingWindowLayer`), which mostly changes how sequence length is handled and how the cache is updated.
 
-In [`DynamicCache`], the key-value pairs are stored as two lists of tensors. Each tensor in the lists have the shape `[batch_size, num_heads, seq_len, head_dim]`.
-- `key_cache`: A list of tensors, one for each layer.
-- `value_cache`: A list of tensors, one for each layer.
+The simplest is a `DynamicLayer` that grows as more tokens are processed. The sequence length dimension (`seq_len`) increases with each new token:
 
-When new tokens are processed:
-
-1. For each layer, the new key and value states are concatenated with the existing cache.
 ```py
-self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+cache.layers[idx].keys = torch.cat([cache.layers[idx].keys, key_states], dim=-2)
+cache.layers[idx].values = torch.cat([cache.layers[idx].values, value_states], dim=-2)
 ```
 
-2. The cache grows dynamically as more tokens are processed. The sequence length dimension (`seq_len`) increases with each new token.
-
-3. The cache maintains a count of seen tokens through `self._seen_tokens`. This is updated when the first layer processes a new token.
+Other layer types like `StaticLayer` and `SlidingWindowLayer` have a fixed sequence length that is set when the cache is created. This makes them compatible with `torch.compile`. In the case of `SlidingWindowLayer`, existing tokens are shifted out of the cache when a new token is added.
 
 The example below demonstrates how to create a generation loop with [`DynamicCache`]. As discussed, the attention mask is a concatenation of past and current token values and `1` is added to the cache position for the next token.
 
@@ -134,6 +128,34 @@ for _ in range(max_new_tokens):
 print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
 "[INST] Hello, what's your name. [/INST]  Hello! My name is LLaMA,"
 ```
+
+## Cache position
+
+The cache position tracks where to insert new tokens in the attention cache. It represents the *absolute* position of each token in the context, independent of padding or batch structure. Suppose you already cached `N` tokens and are now processing `K` new tokens. The cache position for the new tokens will range from `N` to `N + K - 1`. In other words, you're processing tokens at positions - `[N, N + 1, N + 2, ..., N + K - 1]`.
+
+Cache position is used internally for two purposes:
+
+1. Selecting new tokens to process in the input sequence and ensuring only tokens that haven’t been cached yet are passed to the model's `forward`.
+2. Storing key/value pairs at the correct positions in the cache. This is especially important for fixed-size caches, like [`StaticCache`], that pre-allocates a specific cache length.
+
+The generation loop usually takes care of the cache position, but if you're writing a custom generation method, it is important that cache positions are accurate since they are used to write and read key/value states into fixed slots.
+
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
+
+model_id = "meta-llama/Llama-2-7b-chat-hf"
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda:0")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [{"role": "user", "content": "You are a helpful assistant."}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda:0")
+generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=10)
+
+```
+
+
 ## Legacy cache format
 
 Before the [`Cache`] class, the cache used to be stored as a tuple of tuples of tensors. This format is dynamic because it grows as text is generated, similar to [`DynamicCache`].
@@ -143,7 +165,7 @@ The legacy format is essentially the same data structure but organized different
 - The tensors have the same shape `[batch_size, num_heads, seq_len, head_dim]`.
 - The format is less flexible and doesn't support features like quantization or offloading.
 
-If your project depends on this legacy format, you can convert between [`DynamicCache`] and a tuple of tuples as shown below with the [`~DynamicCache.from_legacy_cache`] and [`DynamicCache.to_legacy_cache`] functions. This is helpful if you have custom logic for manipulating a cache in a specific format.
+If your project depends on this legacy format, we recommend to convert to [`DynamicCache`] with [`~DynamicCache.from_legacy_cache`]. Note that legacy cache format is deprecated and not used anymore in `Transformers`. You can convert back to tuple format with [`DynamicCache.to_legacy_cache`] functions, which is helpful if you have custom logic for manipulating a cache in a specific format.
 
 ```py
 import torch
@@ -159,4 +181,4 @@ generation_outputs = model.generate(**inputs, return_dict_in_generate=True, retu
 
 cache = DynamicCache.from_legacy_cache(generation_outputs.past_key_values)
 legacy_format_cache = cache.to_legacy_cache()
-```
+```

docs/source/en/conversations.md

@@ -25,10 +25,7 @@ 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
-
-
-### Interactive chat session
+## chat CLI
 
 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.
 
@@ -52,68 +49,7 @@ For a full list of options, run the command below.
 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).
-
-
-### Serving a model and using MCP tools
-
-> [!WARNING]
-> This section is experimental and subject to changes in future versions
-
-Powering the `chat` interface, we have a server that takes user messages and returns completions. The server has a chat completion API compatible with the OpenAI SDK, so you can also quickly experiment with `transformers` models on existing aplications. To launch a server separately, use the `transformers serve` CLI:
-
-```bash
-transformers serve Menlo/Jan-nano
-```
-
-Under the hood, the `chat` CLI launches and uses `transformers serve`. This server is also an MCP client, which can receive information available MCP servers (i.e. tools), massage their information into the model prompt, and prepare calls to these tools when the model commands to do so. Naturally, this requires a model that is trained to use tools.
-
-At the moment, MCP tool usage in `transformers` has the following constraints:
-- `chat` can't handle tools, but the [`tiny-agents`](https://huggingface.co/blog/python-tiny-agents) CLI can;
-- Only the `qwen` family of models is supported.
-
-The first step to use MCP tools is to let the model know which tools are available. As an example, let's consider a `tiny-agents` configuration file with a reference to an [image generation MCP server](https://evalstate-flux1-schnell.hf.space/).
-
-> [!TIP]
-> Many Hugging Face Spaces can be used as MCP servers. You can find all compatible Spaces [here](https://huggingface.co/spaces?filter=mcp-server).
-
-```json
-{
-    "model": "http://localhost:8000",
-    "provider": "local",
-    "servers": [
-        {
-            "type": "sse",
-            "config": {
-                "url": "https://evalstate-flux1-schnell.hf.space/gradio_api/mcp/sse"
-            }
-        }
-    ]
-}
-```
-
-You can then launch your `tiny-agents` chat interface with the following command.
-
-```bash
-tiny-agents run path/to/your/config.json
-```
-
-If you have a server (from `transformers serve`) running in the background, you're ready to use MCP tools from a local model! For instance, here's the example of a chat session:
-
-```bash
-Agent loaded with 1 tools:
- • flux1_schnell_infer
-»  Generate an image of a cat on the moon
-<Tool req_0_tool_call>flux1_schnell_infer {"prompt": "a cat on the moon", "seed": 42, "randomize_seed": true, "width": 1024, "height": 1024, "num_inference_steps": 4}
-
-Tool req_0_tool_call
-[Binary Content: Image image/webp, 57732 bytes]
-The task is complete and the content accessible to the User
-Image URL: https://evalstate-flux1-schnell.hf.space/gradio_api/file=/tmp/gradio/3dbddc0e53b5a865ed56a4e3dbdd30f3f61cf3b8aabf1b456f43e5241bd968b8/image.webp
-380576952
-
-I have generated an image of a cat on the moon using the Flux 1 Schnell Image Generator. The image is 1024x1024 pixels and was created with 4 inference steps. Let me know if you would like to make any changes or need further assistance!
-```
+The chat is implemented on top of the [AutoClass](./model_doc/auto), using tooling from [text generation](./llm_tutorial) and [chat](./chat_templating). It uses the `transformers serve` CLI under the hood ([docs](./serving.md#serve-cli)).
 
 
 ## TextGenerationPipeline

docs/source/en/custom_models.md

@@ -271,7 +271,7 @@ The model is ready to be pushed to the Hub now. Log in to your Hugging Face acco
 <hfoption id="huggingface-CLI">
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 </hfoption>

docs/source/en/internal/generation_utils.md

@@ -356,66 +356,93 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 ## Caches
 
-[[autodoc]] Cache
-    - update
-
-[[autodoc]] CacheConfig
-	- update
-
-[[autodoc]] QuantizedCacheConfig
-	- validate
-
-[[autodoc]] DynamicCache
+[[autodoc]] CacheLayerMixin
     - update
     - get_seq_length
+    - get_mask_sizes
+    - get_max_cache_shape
+    - reset
     - reorder_cache
+
+[[autodoc]] DynamicLayer
+    - update
+    - crop
+    - batch_repeat_interleave
+    - batch_select_indices
+
+[[autodoc]] StaticLayer
+    - update
+
+[[autodoc]] SlidingWindowLayer
+    - update
+
+[[autodoc]] CacheProcessor
+    - pre_update
+    - post_update
+
+[[autodoc]] OffloadedCacheProcessor
+    - pre_update
+
+[[autodoc]] QuantizedCacheProcessor
+    - post_update
+
+[[autodoc]] QuantoQuantizedCacheProcessor
+    - post_update
+
+[[autodoc]] HQQQuantizedCacheProcessor
+    - post_update
+
+[[autodoc]] Cache
+    - update
+    - get_seq_length
+    - get_mask_sizes
+    - get_max_cache_shape
+    - reset
+    - reorder_cache
+    - crop
+    - batch_repeat_interleave
+    - batch_select_indices
+
+[[autodoc]] DynamicCache
     - to_legacy_cache
     - from_legacy_cache
 
 [[autodoc]] QuantizedCache
-    - update
-    - get_seq_length
 
 [[autodoc]] QuantoQuantizedCache
 
+[[autodoc]] QuantoQuantizedCacheProcessor
+
 [[autodoc]] HQQQuantizedCache
 
+[[autodoc]] HQQQuantizedCacheProcessor
+
 [[autodoc]] OffloadedCache
-    - update
-    - prefetch_layer
-    - evict_previous_layer
 
 [[autodoc]] StaticCache
-    - update
-    - get_seq_length
-    - reset
 
 [[autodoc]] OffloadedStaticCache
-    - update
-    - get_seq_length
-    - reset
 
 [[autodoc]] HybridCache
-    - update
-    - get_seq_length
-    - reset
+
+[[autodoc]] HybridChunkedCache
 
 [[autodoc]] SlidingWindowCache
-    - update
-    - reset
 
 [[autodoc]] EncoderDecoderCache
-    - get_seq_length
     - to_legacy_cache
     - from_legacy_cache
-    - reset
-    - reorder_cache
 
 [[autodoc]] MambaCache
     - update_conv_state
     - update_ssm_state
     - reset
 
+[[autodoc]] CacheConfig
+
+[[autodoc]] QuantizedCacheConfig
+
+
 ## Watermark Utils
 
 [[autodoc]] WatermarkingConfig

docs/source/en/internal/model_debugging_utils.md

@@ -247,3 +247,114 @@ first and last layer will be shown. This is useful when some layers (typically c
 layers.
 
 [[autodoc]] model_addition_debugger_context
+
+## Analyzer of skipped tests
+
+### Scan skipped tests - for model adders and maintainers
+
+This small util is a power user tool intended for model adders and maintainers. It lists all test methods
+existing in `test_modeling_common.py`, inherited by all model tester classes, and scans the repository to measure
+how many tests are being skipped and for which models. 
+
+### Rationale
+
+When porting models to transformers, tests fail as they should, and sometimes `test_modeling_common` feels irreconcilable with the peculiarities of our brand new model. But how can we be sure we're not breaking everything by adding a seemingly innocent skip?
+
+This utility:
+- scans all test_modeling_common methods
+- looks for times where a method is skipped
+- returns a summary json you can load as a DataFrame/inspect
+
+**For instance test_inputs_embeds is skipped in a whooping 39% proportion at the time of writing this util.**
+
+![download-icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/f7f671f69b88ce4967e19179172c248958d35742/transformers/tests_skipped_visualisation.png)
+
+
+### Usage 
+
+You can run the skipped test analyzer in two ways:
+
+#### Full scan (default)
+
+From the root of `transformers` repo, scans all common test methods and outputs the results to a JSON file (default: `all_tests_scan_result.json`).
+
+```bash
+python utils/scan_skipped_tests.py --output_dir path/to/output
+```
+
+- `--output_dir` (optional): Directory where the JSON results will be saved. Defaults to the current directory.
+
+**Example output:**
+
+```
+🔬 Parsing 331 model test files once each...
+📝 Aggregating 224 tests...
+  (224/224) test_update_candidate_strategy_with_matches_1es_3d_is_nonecodet_schedule_fa_kwargs
+✅ Scan complete.
+
+📄 JSON saved to /home/pablo/git/transformers/all_tests_scan_result.json
+
+```
+
+And it will generate `all_tests_scan_result.json` file that you can inspect. The JSON is indexed by method name, and each entry follows this schema, indicating the origin as well (from `common`or `GenerationMixin`.)
+
+```json
+{
+  "<method_name>": {
+    "origin": "<test suite>"
+    "models_ran": ["<model_name>", ...],
+    "models_skipped": ["<model_name>", ...],
+    "skipped_proportion": <float>,
+    "reasons_skipped": ["<model_name>: <reason>",
+      ...
+    ]
+  },
+  ...
+}
+```
+
+Which you can visualise as above with e.g. `pandas`
+
+```python
+df = pd.read_json('all_tests_scan_result.json').T
+df.sort_values(by=['skipped_proportion'], ascending=False)
+
+```
+
+### Scan a single test method
+
+You can focus on a specific test method using `--test_method_name`:
+
+```bash
+$ python utils/scan_skipped_tests.py --test_method_name test_inputs_embeds --output_dir path/to/output
+```
+
+- `--test_method_name`: Name of the test method to scan (e.g., `test_inputs_embeds`).
+- `--output_dir` (optional): Directory where the JSON result will be saved.
+
+**Example output:**
+
+```bash
+$ python utils/scan_skipped_tests.py --test_method_name test_inputs_embeds
+
+🔬 Parsing 331 model test files once each...
+
+== test_inputs_embeds ==
+
+Ran    : 199/323
+Skipped : 124/323 (38.4%)
+ - aimv2: Aimv2 does not use inputs_embeds
+ - align: Inputs_embeds is tested in individual model tests
+ - altclip: Inputs_embeds is tested in individual model tests
+ - audio_spectrogram_transformer: AST does not use inputs_embeds
+ - beit: BEiT does not use inputs_embeds
+ - bit: Bit does not use inputs_embeds
+ - blip: Blip does not use inputs_embeds
+ - blip_2: Inputs_embeds is tested in individual model tests
+ - bridgetower: 
+ - canine: CANINE does not have a get_input_embeddings() method.
+ - ...
+
+📄 JSON saved to /home/pablo/git/transformers/scan_test_inputs_embeds.json
+
+```

docs/source/en/kv_cache.md

@@ -44,7 +44,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
 
 model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False)
@@ -59,7 +59,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
 
 past_key_values = DynamicCache()
@@ -134,7 +134,7 @@ The [`QuantizedCache`] reduces memory requirements by quantizing the KV values t
 > [!WARNING]
 > Quantizing the cache can harm latency if the context length is short and there is enough GPU memory available for generation without enabling cache quantization. Try to find a balance between memory efficiency and latency.
 
-Enable [`QuantizedCache`] by configuring `cache_implementation="quantized"` in [`GenerationConfig`], and indicate the quantization backend in [`QuantizedCacheConfig`]. Any additional quantization related parameters should also be passed either as a dict or an instance of [`QuantizedCacheConfig`]. You should use the default values for these additional parameters unless you're running out-of-memory. In that case, consider decreasing the residual length.
+Enable [`QuantizedCache`] by configuring `cache_implementation="quantized"` in [`GenerationConfig`], and the quantization backend, as well as any additional quantization related parameters should also be passed either as a dict. You should use the default values for these additional parameters unless you're running out-of-memory. In that case, consider decreasing the residual length.
 
 <hfoptions id="quantized-cache">
 <hfoption id="HQQQuantizedCache">
@@ -142,13 +142,14 @@ Enable [`QuantizedCache`] by configuring `cache_implementation="quantized"` in [
 For [`HQQQuantizedCache`], we recommend setting the `axis-key` and `axis-value` parameters to `1`.
 
 ```py
-from transformers import AutoTokenizer, AutoModelForCausalLM, HQQQuantizedCache, QuantizedCacheConfig
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, HQQQuantizedCache
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
 
-out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"axis-key": 1, "axis-value": 1, "backend": "hqq"})
+out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"backend": "HQQ"})
 print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
 I like rock music because it's loud and energetic. It's a great way to express myself and rel
 ```
@@ -159,13 +160,14 @@ I like rock music because it's loud and energetic. It's a great way to express m
 For [`QuantoQuantizedCache`], we recommend setting the `axis-key` and `axis-value` parameters to `0`.
 
 ```py
-from transformers import AutoTokenizer, AutoModelForCausalLM, QuantoQuantizedCache, QuantizedCacheConfig
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, QuantoQuantizedCache
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
 
-out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "axis-key": 0, "axis-value": 0, "backend": "quanto"})
+out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "backend": "quanto"})
 print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
 I like rock music because it's loud and energetic. It's a great way to express myself and rel
 ```
@@ -207,14 +209,14 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map={"": 0})
 inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
 
 out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="offloaded_static")
 tokenizer.batch_decode(out, skip_special_tokens=True)[0]
 "Hello, my name is [Your Name], and I am a [Your Profession] with [Number of Years] of"
 ```
-Cache offloading requires a CUDA GPU.
+Cache offloading requires a CUDA GPU or Intel XPU.
 
 ### Sliding window cache
 
@@ -227,7 +229,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
-model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16).to("cuda:0")
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, device_map="auto")
 inputs = tokenizer("Yesterday I was on a rock concert and.", return_tensors="pt").to(model.device)
 
 out = model.generate(**inputs, do_sample=False, max_new_tokens=30, cache_implementation="sliding_window")
@@ -273,7 +275,6 @@ from transformers.cache_utils import (
     StaticCache,
     SlidingWindowCache,
     QuantoQuantizedCache,
-    QuantizedCacheConfig,
 )
 
 model_id = "meta-llama/Llama-2-7b-chat-hf"
@@ -306,15 +307,15 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache, StaticCache
 
 model_id = "meta-llama/Llama-2-7b-chat-hf"
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda")
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map={"": 0})
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 # Init StaticCache with big enough max-length (1024 tokens for the below example)
 # You can also init a DynamicCache, if that suits you better
-prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16)
+prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device=model.device.type, dtype=torch.bfloat16)
 
 INITIAL_PROMPT = "You are a helpful assistant. "
-inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda")
+inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to(model.device.type)
 # This is the common prompt cached, we need to run forward without grad to be able to copy
 with torch.no_grad():
      prompt_cache = model(**inputs_initial_prompt, past_key_values = prompt_cache).past_key_values
@@ -322,7 +323,7 @@ with torch.no_grad():
 prompts = ["Help me to write a blogpost about travelling.", "What is the capital of France?"]
 responses = []
 for prompt in prompts:
-    new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
+    new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to(model.device.type)
     past_key_values = copy.deepcopy(prompt_cache)
     outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20)
     response = tokenizer.batch_decode(outputs)[0]

docs/source/en/llm_optims.md

@@ -341,7 +341,7 @@ A known issue with transformer models is that the self-attention mechanism grows
 
 FlashAttention and [FlashAttention-2](./perf_infer_gpu_one#flashattention-2) break up the attention computation into smaller chunks and reduces the number of intermediate read/write operations to the GPU memory to speed up inference. FlashAttention-2 improves on the original FlashAttention algorithm by also parallelizing over sequence length dimension and better partitioning work on the hardware to reduce synchronization and communication overhead.
 
-To use FlashAttention-2, set [attn_implementation](https://hf.co/docs/transformers/main/en/main_classes/text_generation#transformers.PreTrainedModel.from_pretrained.attn_implementation) to `"flash_attention_2"` in [`~PreTrainedModel.from_pretrained`].
+To use FlashAttention-2, set [attn_implementation](https://hf.co/docs/transformers/main/en/main_classes/text_generation#transformers.PreTrainedModel.from_pretrained.attn_implementation) to `"flash_attention_2"` in [`~PreTrainedModel.from_pretrained`] or set with `model.set_attention_implementation("flash_attention_2")` to dynamically update the [attention interface](./attention_interface) after the model is loaded.
 
 ```py
 from transformers import AutoModelForCausalLM, BitsAndBytesConfig
@@ -353,6 +353,14 @@ model = AutoModelForCausalLM.from_pretrained(
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
 )
+
+# Change the model's attention dynamically after loading
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b",
+    quantization_config=quant_config,
+    torch_dtype=torch.bfloat16
+)
+model.set_attention_implementation("flash_attention_2")
 ```
 
 ### PyTorch scaled dot product attention
@@ -360,7 +368,7 @@ model = AutoModelForCausalLM.from_pretrained(
 Scaled dot product attention (SDPA) is automatically enabled in PyTorch 2.0 and it supports FlashAttention, xFormers, and PyTorch's C++ implementation. SDPA chooses the most performant attention algorithm if you're using a CUDA backend. For other backends, SDPA defaults to the PyTorch C++ implementation.
 
 > [!TIP]
-> SDPA automaticallysupports FlashAttention-2 as long as you have the latest PyTorch version installed.
+> SDPA automatically supports FlashAttention-2 as long as you have the latest PyTorch version installed.
 
 Use the [torch.nn.attention.sdpa_kernel](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html) context manager to explicitly enable or disable any of the four attention algorithms. For example, use `SDPBackend.FLASH_ATTENTION` to enable FlashAttention.
 

docs/source/en/main_classes/callback.md

@@ -33,6 +33,7 @@ By default, `TrainingArguments.report_to` is set to `"all"`, so a [`Trainer`] wi
   it's the second one).
 - [`~integrations.TensorBoardCallback`] if tensorboard is accessible (either through PyTorch >= 1.4
   or tensorboardX).
+- [`~integrations.TrackioCallback`] if [trackio](https://github.com/gradio-app/trackio) is installed.
 - [`~integrations.WandbCallback`] if [wandb](https://www.wandb.com/) is installed.
 - [`~integrations.CometCallback`] if [comet_ml](https://www.comet.com/site/) is installed.
 - [`~integrations.MLflowCallback`] if [mlflow](https://www.mlflow.org/) is installed.
@@ -72,6 +73,9 @@ Here is the list of the available [`TrainerCallback`] in the library:
 
 [[autodoc]] integrations.TensorBoardCallback
 
+[[autodoc]] integrations.TrackioCallback
+    - setup
+
 [[autodoc]] integrations.WandbCallback
     - setup
 

docs/source/en/main_classes/quantization.md

@@ -93,6 +93,10 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
 
 [[autodoc]] QuarkConfig
 
+## FPQuantConfig
+
+[[autodoc]] FPQuantConfig
+
 ## AutoRoundConfig
 
 [[autodoc]] AutoRoundConfig

docs/source/en/model_doc/aimv2.md

@@ -0,0 +1,104 @@
+<!--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.
+
+-->
+
+# AIMv2
+
+## Overview
+
+The AIMv2 model was proposed in [Multimodal Autoregressive Pre-training of Large Vision Encoders](https://arxiv.org/abs/2411.14402) by Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor Guilherme Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander T Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua M. Susskind, Alaaeldin El-Nouby.
+
+The abstract from the paper is the following:
+
+*We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Furthermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal image understanding across diverse settings.*
+
+
+This model was contributed by [Yaswanth Gali](https://huggingface.co/yaswanthgali).
+The original code can be found [here](https://github.com/apple/ml-aim).
+
+## Usage Example
+
+Here is an example of Image Feature Extraction using specific checkpoints on resized images and native resolution images:
+
+```python
+import requests
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModel
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-native")
+model = AutoModel.from_pretrained("apple/aimv2-large-patch14-native")
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+Here is an example of a checkpoint performing zero-shot classification:
+
+```python
+import requests
+from PIL import Image
+from transformers import AutoProcessor, AutoModel
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+text = ["Picture of a dog.", "Picture of a cat.", "Picture of a horse."]
+
+processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")
+model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-lit")
+
+inputs = processor(
+    images=image,
+    text=text,
+    add_special_tokens=True,
+    truncation=True,
+    padding=True,
+    return_tensors="pt",
+)
+outputs = model(**inputs)
+probs = outputs.logits_per_image.softmax(dim=-1)
+```
+
+## Aimv2Config
+
+[[autodoc]] Aimv2Config
+
+## Aimv2TextConfig
+
+[[autodoc]] Aimv2TextConfig
+
+## Aimv2VisionConfig
+
+[[autodoc]] Aimv2VisionConfig
+
+## Aimv2Model
+
+[[autodoc]] Aimv2Model
+    - forward
+
+## Aimv2VisionModel
+
+[[autodoc]] Aimv2VisionModel
+    - forward
+
+## Aimv2TextModel
+
+[[autodoc]] Aimv2TextModel
+    - forward
+
+</pt>
+<tf>

docs/source/en/model_doc/auto.md

@@ -258,6 +258,10 @@ The following auto classes are available for the following computer vision tasks
 
 [[autodoc]] AutoModelForKeypointDetection
 
+### AutoModelForKeypointMatching
+
+[[autodoc]] AutoModelForKeypointMatching
+
 ### AutoModelForMaskedImageModeling
 
 [[autodoc]] AutoModelForMaskedImageModeling

docs/source/en/model_doc/camembert.md

@@ -14,49 +14,105 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# CamemBERT
-
-<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="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="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+	</div>
 </div>
 
-## Overview
+# CamemBERT
 
-The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by
-[Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Villemonte de la
-Clergerie, [Djamé Seddah](https://huggingface.co/Djame), and [Benoît Sagot](https://huggingface.co/sagot). It is based on Facebook's RoBERTa model released in 2019. It is a model
-trained on 138GB of French text.
+[CamemBERT](https://huggingface.co/papers/1911.03894) is a language model based on [RoBERTa](./roberta), but trained specifically on French text from the OSCAR dataset, making it more effective for French language tasks.
 
-The abstract from the paper is the following:
+What sets CamemBERT apart is that it learned from a huge, high quality collection of French data, as opposed to mixing lots of languages. This helps it really understand French better than many multilingual models.
 
-*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
-models have either been trained on English data or on the concatenation of data in multiple languages. This makes
-practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
-we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
-performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
-dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
-for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
-downstream applications for French NLP.*
+Common applications of CamemBERT include masked language modeling (Fill-mask prediction), text classification (sentiment analysis), token classification (entity recognition) and sentence pair classification (entailment tasks).
 
-This model was contributed by [the ALMAnaCH team (Inria)](https://huggingface.co/almanach). The original code can be found [here](https://camembert-model.fr/).
+You can find all the original CamemBERT checkpoints under the [ALMAnaCH](https://huggingface.co/almanach/models?search=camembert) organization.
 
-<Tip>
+> [!TIP]
+> This model was contributed by the [ALMAnaCH (Inria)](https://huggingface.co/almanach) team.
+>
+> Click on the CamemBERT models in the right sidebar for more examples of how to apply CamemBERT to different NLP tasks.
 
-This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well 
-as the information relative to the inputs and outputs.
+The examples below demonstrate how to predict the `<mask>` token with [`Pipeline`], [`AutoModel`], and from the command line.
 
-</Tip>
+<hfoptions id="usage">
 
-## Resources
+<hfoption id="Pipeline">
 
-- [Text classification task guide](../tasks/sequence_classification)
-- [Token classification task guide](../tasks/token_classification)
-- [Question answering task guide](../tasks/question_answering)
-- [Causal language modeling task guide](../tasks/language_modeling)
-- [Masked language modeling task guide](../tasks/masked_language_modeling)
-- [Multiple choice task guide](../tasks/multiple_choice)
+```python
+import torch
+from transformers import pipeline
+
+pipeline = pipeline("fill-mask", model="camembert-base", torch_dtype=torch.float16, device=0)
+pipeline("Le camembert est un délicieux fromage <mask>.")
+```
+</hfoption> 
+
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("camembert-base")
+model = AutoModelForMaskedLM.from_pretrained("camembert-base", torch_dtype="auto", device_map="auto", attn_implementation="sdpa")
+inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = outputs.logits
+
+masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
+predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+
+print(f"The predicted token is: {predicted_token}")
+```
+</hfoption> 
+
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "Le camembert est un délicieux fromage <mask>." | transformers run --task fill-mask --model camembert-base --device 0
+```
+
+</hfoption> 
+
+</hfoptions> 
+
+
+Quantization reduces the memory burden of large models by representing weights in lower precision. Refer to the [Quantization](../quantization/overview) overview for available options.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) quantization to quantize the weights to 8-bits.
+  
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM, BitsAndBytesConfig
+import torch
+
+quant_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForMaskedLM.from_pretrained(
+    "almanach/camembert-large",
+    quantization_config=quant_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("almanach/camembert-large")
+
+inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = outputs.logits
+
+masked_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+
+print(f"The predicted token is: {predicted_token}")
+```
 
 ## CamembertConfig
 
@@ -137,5 +193,4 @@ as the information relative to the inputs and outputs.
 [[autodoc]] TFCamembertForQuestionAnswering
 
 </tf>
-</frameworkcontent>
-
+</frameworkcontent>

docs/source/en/model_doc/deepseek_v2.md

@@ -0,0 +1,49 @@
+<!--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.
+
+-->
+
+# DeepSeek-V2
+
+## Overview
+
+The DeepSeek-V2 model was proposed in [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) by DeepSeek-AI Team.
+
+The abstract from the paper is the following:
+We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).
+The original code can be found [here](https://huggingface.co/deepseek-ai/DeepSeek-V2).
+
+### Usage tips
+The model uses Multi-head Latent Attention (MLA) and DeepSeekMoE architectures for efficient inference and cost-effective training. It employs an auxiliary-loss-free strategy for load balancing and multi-token prediction training objective. The model can be used for various language tasks after being pre-trained on 14.8 trillion tokens and going through Supervised Fine-Tuning and Reinforcement Learning stages.
+
+## DeepseekV2Config
+
+[[autodoc]] DeepseekV2Config
+
+## DeepseekV2Model
+
+[[autodoc]] DeepseekV2Model
+    - forward
+
+## DeepseekV2ForCausalLM
+
+[[autodoc]] DeepseekV2ForCausalLM
+    - forward
+
+## DeepseekV2ForSequenceClassification
+
+[[autodoc]] DeepseekV2ForSequenceClassification
+    - forward

docs/source/en/model_doc/deepseek_vl.md

@@ -0,0 +1,220 @@
+<!--Copyright 2025 Deepseek AI 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.
+
+-->
+
+<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="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>
+
+# DeepseekVL
+
+[Deepseek-VL](https://arxiv.org/abs/2403.05525) was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages [LLaMA](./llama) as its text encoder, while [SigLip](./siglip) is used for encoding images.
+
+You can find all the original Deepseek-VL checkpoints under the [DeepSeek-community](https://huggingface.co/deepseek-community) organization.
+
+> [!TIP]
+> Click on the Deepseek-VL models in the right sidebar for more examples of how to apply Deepseek-VL to different vision and language tasks.
+
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    task="image-text-to-text",
+    model="deepseek-community/deepseek-vl-1.3b-chat",
+    device=0,
+    torch_dtype=torch.float16
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+            },
+            { "type": "text", "text": "Describe this image."},
+        ]
+    }
+]
+
+pipe(text=messages, max_new_tokens=20, return_full_text=False)
+```
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import DeepseekVLForConditionalGeneration, AutoProcessor
+
+model = DeepseekVLForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-1.3b-chat",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-1.3b-chat")
+
+messages = [
+    {
+        "role":"user",
+        "content":[
+            {
+                "type":"image",
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+            },
+            {
+                "type":"text",
+                "text":"Describe this image."
+            }
+        ]
+    }
+
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device, dtype=model.dtype)
+
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+
+print(output_text)
+```
+</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 [torchao](../quantization/torchao) to only quantize the weights to int4.
+
+```python
+import torch
+from transformers import TorchAoConfig, DeepseekVLForConditionalGeneration, AutoProcessor
+
+quantization_config = TorchAoConfig(
+    "int4_weight_only",
+    group_size=128
+)
+
+model = DeepseekVLForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-1.3b-chat",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=quantization_config
+)
+```
+### Notes
+
+- Do inference with multiple images in a single conversation.
+    ```py
+    import torch
+    from transformers import DeepseekVLForConditionalGeneration, AutoProcessor
+
+    model = DeepseekVLForConditionalGeneration.from_pretrained(
+        "deepseek-community/deepseek-vl-1.3b-chat",
+        torch_dtype=torch.float16,
+        device_map="auto",
+        attn_implementation="sdpa"
+    )
+
+    processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-1.3b-chat")
+
+    messages = [
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What’s the difference between"},
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": " and "},
+                    {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
+                ]
+            }
+        ],
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
+                    {"type": "text", "text": "What do you see in this image?"}
+                ]
+            }
+        ]
+    ]
+
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        padding=True,
+        truncation=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device, dtype=model.dtype)
+
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+
+    print(output_text)
+    ```
+
+## DeepseekVLConfig
+
+[[autodoc]] DeepseekVLConfig
+
+## DeepseekVLProcessor
+
+[[autodoc]] DeepseekVLProcessor
+
+## DeepseekVLImageProcessor
+
+[[autodoc]] DeepseekVLImageProcessor
+
+## DeepseekVLModel
+
+[[autodoc]] DeepseekVLModel
+    - forward
+
+## DeepseekVLForConditionalGeneration
+
+[[autodoc]] DeepseekVLForConditionalGeneration
+    - forward

docs/source/en/model_doc/deepseek_vl_hybrid.md

@@ -0,0 +1,219 @@
+<!--Copyright 2025 Deepseek AI 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.
+
+-->
+
+<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">
+    </div>
+</div>
+
+# DeepseekVLHybrid
+
+[Deepseek-VL-Hybrid](https://arxiv.org/abs/2403.05525) was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages [LLaMA](./llama) as its text encoder, while [SigLip](./siglip) is used for encoding low-resolution images and [SAM (Segment Anything Model)](./sam) is incorporated to handle high-resolution image encoding, enhancing the model’s ability to process fine-grained visual details. Deepseek-VL-Hybrid is a variant of Deepseek-VL that uses [SAM (Segment Anything Model)](./sam) to handle high-resolution image encoding.
+
+You can find all the original Deepseek-VL-Hybrid checkpoints under the [DeepSeek-community](https://huggingface.co/deepseek-community) organization.
+
+> [!TIP]
+> Click on the Deepseek-VL-Hybrid models in the right sidebar for more examples of how to apply Deepseek-VL-Hybrid to different vision and language tasks.
+
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    task="image-text-to-text",
+    model="deepseek-community/deepseek-vl-7b-chat",
+    device=0,
+    torch_dtype=torch.float16
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+            },
+            { "type": "text", "text": "Describe this image."},
+        ]
+    }
+]
+
+pipe(text=messages, max_new_tokens=20, return_full_text=False)
+```
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import DeepseekVLHybridForConditionalGeneration, AutoProcessor
+
+model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-7b-chat",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-7b-chat")
+
+messages = [
+    {
+        "role":"user",
+        "content":[
+            {
+                "type":"image",
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+            },
+            {
+                "type":"text",
+                "text":"Describe this image."
+            }
+        ]
+    }
+
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device, dtype=model.dtype)
+
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+
+print(output_text)
+```
+</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 [torchao](../quantization/torchao) to only quantize the weights to int4.
+
+```python
+import torch
+from transformers import TorchAoConfig, DeepseekVLHybridForConditionalGeneration, AutoProcessor
+
+quantization_config = TorchAoConfig(
+    "int4_weight_only",
+    group_size=128
+)
+
+model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+    "deepseek-community/deepseek-vl-7b-chat",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=quantization_config
+)
+```
+### Notes
+
+- Do inference with multiple images in a single conversation.
+    ```py
+    import torch
+    from transformers import DeepseekVLHybridForConditionalGeneration, AutoProcessor
+
+    model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+        "deepseek-community/deepseek-vl-7b-chat",
+        torch_dtype=torch.float16,
+        device_map="auto",
+        attn_implementation="sdpa"
+    )
+
+    processor = AutoProcessor.from_pretrained("deepseek-community/deepseek-vl-7b-chat")
+
+    messages = [
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What’s the difference between"},
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": " and "},
+                    {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}
+                ]
+            }
+        ],
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
+                    {"type": "text", "text": "What do you see in this image?"}
+                ]
+            }
+        ]
+    ]
+
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        padding=True,
+        truncation=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device, dtype=model.dtype)
+
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+
+    print(output_text)
+    ```
+
+## DeepseekVLHybridConfig
+
+[[autodoc]] DeepseekVLHybridConfig
+
+## DeepseekVLHybridProcessor
+
+[[autodoc]] DeepseekVLHybridProcessor
+
+## DeepseekVLHybridImageProcessor
+
+[[autodoc]] DeepseekVLHybridImageProcessor
+
+## DeepseekVLHybridModel
+
+[[autodoc]] DeepseekVLHybridModel
+    - forward
+
+## DeepseekVLHybridForConditionalGeneration
+
+[[autodoc]] DeepseekVLHybridForConditionalGeneration
+    - forward

docs/source/en/model_doc/dia.md

@@ -44,7 +44,7 @@ tokens and decodes them back into audio.
 from transformers import AutoProcessor, DiaForConditionalGeneration
 
 torch_device = "cuda"
-model_checkpoint = "buttercrab/dia-v1-1.6b"
+model_checkpoint = "nari-labs/Dia-1.6B-0626"
 
 text = ["[S1] Dia is an open weights text to dialogue model."]
 processor = AutoProcessor.from_pretrained(model_checkpoint)
@@ -66,7 +66,7 @@ from datasets import load_dataset, Audio
 from transformers import AutoProcessor, DiaForConditionalGeneration
 
 torch_device = "cuda"
-model_checkpoint = "buttercrab/dia-v1-1.6b"
+model_checkpoint = "nari-labs/Dia-1.6B-0626"
 
 ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
 ds = ds.cast_column("audio", Audio(sampling_rate=44100))
@@ -93,7 +93,7 @@ from datasets import load_dataset, Audio
 from transformers import AutoProcessor, DiaForConditionalGeneration
 
 torch_device = "cuda"
-model_checkpoint = "buttercrab/dia-v1-1.6b"
+model_checkpoint = "nari-labs/Dia-1.6B-0626"
 
 ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
 ds = ds.cast_column("audio", Audio(sampling_rate=44100))

docs/source/en/model_doc/doge.md

@@ -0,0 +1,103 @@
+<!--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.
+
+-->
+
+# Doge
+
+
+## Overview
+
+Doge is a series of small language models based on the [Doge](https://github.com/SmallDoges/small-doge) architecture, aiming to combine the advantages of state-space and self-attention algorithms, calculate dynamic masks from cached value states using the zero-order hold method, and solve the problem of existing mainstream language models getting lost in context. It uses the `wsd_scheduler` scheduler to pre-train on the `smollm-corpus`, and can continue training on new datasets or add sparse activation feedforward networks from stable stage checkpoints.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/refs%2Fpr%2F426/transformers/model_doc/doge_architecture.png" alt="drawing" width="600"/>
+
+As shown in the figure below, the sequence transformation part of the Doge architecture uses `Dynamic Mask Attention`, which can be understood as using self-attention related to value states during training, and using state-space without past state decay during inference, to solve the problem of existing Transformers or SSMs getting lost in long text. The state transformation part of Doge uses `Cross Domain Mixture of Experts`, which consists of dense linear layers and sparse embedding layers, and can additionally increase sparse parameters to continue training from dense weight checkpoints without retraining the entire model, thereby reducing the cost of continuous iteration of the model. In addition, Doge also uses `RMSNorm` and `Residual` with learnable parameters to adapt the gradient range of deep models.
+
+Checkout all Doge model checkpoints [here](https://huggingface.co/collections/SmallDoge/doge-slm-679cc991f027c4a3abbded4a).
+
+
+## Usage
+
+<details>
+<summary>Using Doge-Base for text generation</summary>
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
+model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
+inputs = tokenizer("Hey how are you doing?", return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.batch_decode(outputs))
+```
+</details>
+
+<details>
+<summary>Using Doge-Instruct for question answering</summary>
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M-Instruct")
+model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M-Instruct")
+
+generation_config = GenerationConfig(
+      max_new_tokens=100, 
+      use_cache=True, 
+      do_sample=True, 
+      temperature=0.8, 
+      top_p=0.9,
+      repetition_penalty=1.0
+)
+steamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
+
+prompt = "Hi, how are you doing today?"
+conversation = [
+      {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(
+    conversation=conversation,
+    tokenize=True,
+    return_tensors="pt",
+)
+
+outputs = model.generate(
+    inputs, 
+    tokenizer=tokenizer,
+    generation_config=generation_config, 
+    streamer=steamer
+)
+```
+</details>
+
+## DogeConfig
+
+[[autodoc]] DogeConfig
+
+## DogeModel
+
+[[autodoc]] DogeModel
+    - forward
+
+## DogeForCausalLM
+
+[[autodoc]] DogeForCausalLM
+    - forward
+
+## DogeForSequenceClassification
+
+[[autodoc]] DogeForSequenceClassification
+    - forward

docs/source/en/model_doc/efficientloftr.md

@@ -0,0 +1,114 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the MIT License; you may not use this file except in compliance with
+the License.
+
+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.
+
+
+-->
+
+# EfficientLoFTR
+
+<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>
+
+## Overview
+
+The EfficientLoFTR model was proposed in [Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed](https://arxiv.org/abs/2403.04765) by Yifan Wang, Xingyi He, Sida Peng, Dongli Tan and Xiaowei Zhou.
+
+This model consists of matching two images together by finding pixel correspondences. It can be used to estimate the pose between them. 
+This model is useful for tasks such as image matching, homography estimation, etc.
+
+The abstract from the paper is the following:
+
+*We present a novel method for efficiently producing semidense matches across images. Previous detector-free matcher 
+LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers
+from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. 
+One key observation is that performing the transformer over the entire feature map is redundant due to shared local 
+information, therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. 
+Furthermore, we find spatial variance exists in LoFTR’s fine correlation module, which is adverse to matching accuracy. 
+A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. 
+Our efficiency optimized model is ∼ 2.5× faster than LoFTR which can even surpass state-of-the-art efficient sparse 
+matching pipeline SuperPoint + LightGlue. Moreover, extensive experiments show that our method can achieve higher 
+accuracy compared with competitive semi-dense matchers, with considerable efficiency benefits. This opens up exciting 
+prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. 
+Project page: [https://zju3dv.github.io/efficientloftr/](https://zju3dv.github.io/efficientloftr/).*
+
+## How to use
+
+Here is a quick example of using the model. 
+```python
+import torch
+
+from transformers import AutoImageProcessor, AutoModelForKeypointMatching
+from transformers.image_utils import load_image
+
+
+image1 = load_image("https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg")
+image2 = load_image("https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg")
+
+images = [image1, image2]
+
+processor = AutoImageProcessor.from_pretrained("stevenbucaille/efficientloftr")
+model = AutoModelForKeypointMatching.from_pretrained("stevenbucaille/efficientloftr")
+
+inputs = processor(images, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+```
+
+You can use the `post_process_keypoint_matching` method from the `ImageProcessor` to get the keypoints and matches in a more readable format:
+
+```python
+image_sizes = [[(image.height, image.width) for image in images]]
+outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
+for i, output in enumerate(outputs):
+    print("For the image pair", i)
+    for keypoint0, keypoint1, matching_score in zip(
+            output["keypoints0"], output["keypoints1"], output["matching_scores"]
+    ):
+        print(
+            f"Keypoint at coordinate {keypoint0.numpy()} in the first image matches with keypoint at coordinate {keypoint1.numpy()} in the second image with a score of {matching_score}."
+        )
+```
+
+From the post processed outputs, you can visualize the matches between the two images using the following code:
+```python
+images_with_matching = processor.visualize_keypoint_matching(images, outputs)
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/2nJZQlFToCYp_iLurvcZ4.png)
+
+This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
+The original code can be found [here](https://github.com/zju3dv/EfficientLoFTR).
+
+## EfficientLoFTRConfig
+
+[[autodoc]] EfficientLoFTRConfig
+
+## EfficientLoFTRImageProcessor
+
+[[autodoc]] EfficientLoFTRImageProcessor
+
+- preprocess
+- post_process_keypoint_matching
+- visualize_keypoint_matching
+
+## EfficientLoFTRModel
+
+[[autodoc]] EfficientLoFTRModel
+
+- forward
+
+## EfficientLoFTRForKeypointMatching
+
+[[autodoc]] EfficientLoFTRForKeypointMatching
+
+- forward

docs/source/en/model_doc/encodec.md

@@ -47,7 +47,8 @@ Here is a quick example of how to encode and decode an audio using this model:
 >>> inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt")
 
 >>> encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"])
->>> audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0]
+>>> # `encoder_outputs.audio_codes` contains discrete codes
+>>> audio_values = model.decode(**encoder_outputs, padding_mask=inputs["padding_mask"])[0]
 >>> # or the equivalent with a forward pass
 >>> audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values
 ```

docs/source/en/model_doc/encoder-decoder.md

@@ -14,115 +14,88 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Encoder Decoder Models
-
-<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="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="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
 </div>
 
-## Overview
+# Encoder Decoder Models
 
-The [`EncoderDecoderModel`] can be used to initialize a sequence-to-sequence model with any
-pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
+[`EncoderDecoderModel`](https://huggingface.co/papers/1706.03762) initializes a sequence-to-sequence model with any pretrained autoencoder and pretrained autoregressive model. It is effective for sequence generation tasks as demonstrated in [Text Summarization with Pretrained Encoders](https://huggingface.co/papers/1908.08345) which uses [`BertModel`] as the encoder and decoder.
 
-The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
-was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by
-Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+> [!TIP]
+> This model was contributed by [thomwolf](https://huggingface.co/thomwolf) and the TensorFlow/Flax version by [ydshieh](https://huggingface.co/ydshieh).
+>
+> Click on the Encoder Decoder models in the right sidebar for more examples of how to apply Encoder Decoder to different language tasks.
 
-After such an [`EncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like
-any other models (see the examples for more information).
+The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
 
-An application of this architecture could be to leverage two pretrained [`BertModel`] as the encoder
-and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://huggingface.co/papers/1908.08345) by Yang Liu and Mirella Lapata.
-
-## Randomly initializing `EncoderDecoderModel` from model configurations.
-
-[`EncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`BertModel`] configuration for the encoder and the default [`BertForCausalLM`] configuration for the decoder.
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
 ```python
->>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
+from transformers import pipeline
 
->>> config_encoder = BertConfig()
->>> config_decoder = BertConfig()
+summarizer = pipeline(
+    "summarization",
+    model="patrickvonplaten/bert2bert-cnn_dailymail-fp16",
+    device=0
+)
 
->>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
->>> model = EncoderDecoderModel(config=config)
+text = "Plants create energy through a process known as photosynthesis. This involves capturing sunlight and converting carbon dioxide and water into glucose and oxygen."
+print(summarizer(text))
 ```
 
-## Initialising `EncoderDecoderModel` from a pretrained encoder and a pretrained decoder.
-
-[`EncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained auto-encoding model, *e.g.* BERT, can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the pretrained decoder part of sequence-to-sequence models, *e.g.* decoder of BART, can be used as the decoder.
-Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
-Initializing [`EncoderDecoderModel`] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in [the *Warm-starting-encoder-decoder blog post*](https://huggingface.co/blog/warm-starting-encoder-decoder).
-To do so, the `EncoderDecoderModel` class provides a [`EncoderDecoderModel.from_encoder_decoder_pretrained`] method.
+</hfoption>
+<hfoption id="AutoModel">
 
 ```python
->>> from transformers import EncoderDecoderModel, BertTokenizer
+import torch  
+from transformers import AutoModelForCausalLM, AutoTokenizer  
 
->>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
->>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
+model = AutoModelForCausalLM.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16", torch_dtype=torch.bfloat16, device_map="auto",attn_implementation="sdpa")  
+
+text = "Plants create energy through a process known as photosynthesis. This involves capturing sunlight and converting carbon dioxide and water into glucose and oxygen."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
+
+summary = model.generate(**inputs, max_length=60, num_beams=4, early_stopping=True)
+print(tokenizer.decode(summary[0], skip_special_tokens=True))
 ```
 
-## Loading an existing `EncoderDecoderModel` checkpoint and perform inference.
+</hfoption>
+<hfoption id="transformers CLI">
 
-To load fine-tuned checkpoints of the `EncoderDecoderModel` class, [`EncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
+```bash
+echo -e "Plants create energy through a process known as photosynthesis. This involves capturing sunlight and converting carbon dioxide and water into glucose and oxygen." | transformers-cli run --task summarization --model "patrickvonplaten/bert2bert-cnn_dailymail-fp16" --device 0
+```
 
-To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- [`EncoderDecoderModel`] can be initialized using any pretrained encoder and decoder. But depending on the decoder architecture, the cross-attention layers may be randomly initialized.
+
+These models require downstream fine-tuning, as discussed in this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder). Use [`~EncoderDecoderModel.from_encoder_decoder_pretrained`] to combine encoder and decoder checkpoints.
 
 ```python
->>> from transformers import AutoTokenizer, EncoderDecoderModel
+from transformers import EncoderDecoderModel, BertTokenizer
 
->>> # load a fine-tuned seq2seq model and corresponding tokenizer
->>> model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
->>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
-
->>> # let's perform inference on a long piece of text
->>> ARTICLE_TO_SUMMARIZE = (
-...     "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
-...     "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
-...     "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
-... )
->>> input_ids = tokenizer(ARTICLE_TO_SUMMARIZE, return_tensors="pt").input_ids
-
->>> # autoregressively generate summary (uses greedy decoding by default)
->>> generated_ids = model.generate(input_ids)
->>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
->>> print(generated_text)
-nearly 800 thousand customers were affected by the shutoffs. the aim is to reduce the risk of wildfires. nearly 800, 000 customers were expected to be affected by high winds amid dry conditions. pg & e said it scheduled the blackouts to last through at least midday tomorrow.
+tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
+model = EncoderDecoderModel.from_encoder_decoder_pretrained(
+    "google-bert/bert-base-uncased", 
+    "google-bert/bert-base-uncased"
+)
 ```
 
-## Loading a PyTorch checkpoint into `TFEncoderDecoderModel`.
-
-[`TFEncoderDecoderModel.from_pretrained`] currently doesn't support initializing the model from a
-pytorch checkpoint. Passing `from_pt=True` to this method will throw an exception. If there are only pytorch
-checkpoints for a particular encoder-decoder model, a workaround is:
-
-```python
->>> # a workaround to load from pytorch checkpoint
->>> from transformers import EncoderDecoderModel, TFEncoderDecoderModel
-
->>> _model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
-
->>> _model.encoder.save_pretrained("./encoder")
->>> _model.decoder.save_pretrained("./decoder")
-
->>> model = TFEncoderDecoderModel.from_encoder_decoder_pretrained(
-...     "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True
-... )
->>> # This is only for copying some specific attributes of this particular model.
->>> model.config = _model.config
-```
-
-## Training
-
-Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model.
-As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the
-`input_ids` of the encoded input sequence) and `labels` (which are the `input_ids` of the encoded
-target sequence).
+- Encoder Decoder models can be fine-tuned like BART, T5 or any other encoder-decoder model. Only 2 inputs are required to compute a loss, `input_ids` and `labels`. Refer to this [notebook](https://colab.research.google.com/drive/1WIk2bxglElfZewOHboPFNj8H44_VAyKE?usp=sharing#scrollTo=ZwQIEhKOrJpl) for a more detailed training example.
 
 ```python
 >>> from transformers import BertTokenizer, EncoderDecoderModel
@@ -147,11 +120,42 @@ target sequence).
 >>> loss = model(input_ids=input_ids, labels=labels).loss
 ```
 
-Detailed [colab](https://colab.research.google.com/drive/1WIk2bxglElfZewOHboPFNj8H44_VAyKE?usp=sharing#scrollTo=ZwQIEhKOrJpl) for training.
+- [`EncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config as shown below.
 
-This model was contributed by [thomwolf](https://github.com/thomwolf). This model's TensorFlow and Flax versions
-were contributed by [ydshieh](https://github.com/ydshieh).
+```python
+>>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
 
+>>> config_encoder = BertConfig()
+>>> config_decoder = BertConfig()
+
+>>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
+>>> model = EncoderDecoderModel(config=config)
+```
+
+- The Encoder Decoder Model can also be used for translation as shown below.
+
+```python
+from transformers import AutoTokenizer, EncoderDecoderModel  
+
+# Load a pre-trained translation model  
+model_name = "google/bert2bert_L-24_wmt_en_de" 
+tokenizer = AutoTokenizer.from_pretrained(model_name, pad_token="<pad>", eos_token="</s>", bos_token="<s>")  
+model = EncoderDecoderModel.from_pretrained(model_name)  
+
+# Input sentence to translate  
+input_text = "Plants create energy through a process known as"  
+
+# Encode the input text  
+inputs = tokenizer(input_text, return_tensors="pt", add_special_tokens=False).input_ids  
+
+# Generate the translated output  
+outputs = model.generate(inputs)[0]  
+
+# Decode the output tokens to get the translated sentence  
+translated_text = tokenizer.decode(outputs, skip_special_tokens=True)  
+
+print("Translated text:", translated_text)  
+```
 
 ## EncoderDecoderConfig
 

docs/source/en/model_doc/eomt.md

@@ -74,20 +74,16 @@ inputs = processor(
     return_tensors="pt",
 )
 
-# Remove Patch Offsets from inputs — only used later for post-processing.
-patch_offsets = inputs.pop("patch_offsets")
-
 with torch.inference_mode():
     outputs = model(**inputs)
 
 # Prepare the original image size in the format (height, width)
-original_image_sizes = [(image.height, image.width)]
+target_sizes = [(image.height, image.width)]
 
 # Post-process the model outputs to get final segmentation prediction
 preds = processor.post_process_semantic_segmentation(
     outputs,
-    patch_offsets=patch_offsets,
-    original_image_sizes=original_image_sizes,
+    target_sizes=target_sizes,
 )
 
 # Visualize the segmentation mask
@@ -130,12 +126,12 @@ with torch.inference_mode():
     outputs = model(**inputs)
 
 # Prepare the original image size in the format (height, width)
-original_image_sizes = [(image.height, image.width)]
+target_sizes = [(image.height, image.width)]
 
 # Post-process the model outputs to get final segmentation prediction
 preds = processor.post_process_instance_segmentation(
     outputs,
-    original_image_sizes=original_image_sizes,
+    target_sizes=target_sizes,
 )
 
 # Visualize the segmentation mask
@@ -173,12 +169,12 @@ with torch.inference_mode():
     outputs = model(**inputs)
 
 # Prepare the original image size in the format (height, width)
-original_image_sizes = [(image.height, image.width)]
+target_sizes = [(image.height, image.width)]
 
 # Post-process the model outputs to get final segmentation prediction
 preds = processor.post_process_panoptic_segmentation(
     outputs,
-    original_image_sizes=original_image_sizes,
+    target_sizes=target_sizes,
 )
 
 # Visualize the panoptic segmentation mask

docs/source/en/model_doc/ernie4_5.md

@@ -0,0 +1,99 @@
+<!--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="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">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Ernie 4.5
+
+## Overview
+
+The Ernie 4.5 model was released in the [Ernie 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) release by baidu.
+This family of models contains multiple different architectures and model sizes. This model in specific targets the base text
+model without mixture of experts (moe) with 0.3B parameters in total. It uses the standard [Llama](./llama.md) at its core.
+
+Other models from the family can be found at [Ernie 4.5 Moe](./ernie4_5_moe.md).
+
+<div class="flex justify-center">
+    <img src="https://ernie.baidu.com/blog/posts/ernie4.5/overview.png"/>
+</div>
+
+
+## Usage Tips
+
+### Generate text
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-0.3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+This model was contributed by [Anton Vlasjuk](https://huggingface.co/AntonV).
+The original code can be found [here](https://github.com/PaddlePaddle/ERNIE).
+
+
+## Ernie4_5Config
+
+[[autodoc]] Ernie4_5Config
+
+## Ernie4_5Model
+
+[[autodoc]] Ernie4_5Model
+    - forward
+
+## Ernie4_5ForCausalLM
+
+[[autodoc]] Ernie4_5ForCausalLM
+    - forward

docs/source/en/model_doc/ernie4_5_moe.md

@@ -0,0 +1,183 @@
+<!--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="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">
+        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+    </div>
+</div>
+
+# Ernie 4.5 Moe
+
+## Overview
+
+The Ernie 4.5 Moe model was released in the [Ernie 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) release by baidu.
+This family of models contains multiple different architectures and model sizes. This model in specific targets the base text
+model with mixture of experts (moe) - one with 21B total, 3B active parameters and another one with 300B total, 47B active parameters.
+It uses the standard [Llama](./llama.md) at its core combined with a specialized MoE based on [Mixtral](./mixtral.md) with additional shared
+experts.
+
+Other models from the family can be found at [Ernie 4.5](./ernie4_5.md).
+
+<div class="flex justify-center">
+    <img src="https://ernie.baidu.com/blog/posts/ernie4.5/overview.png"/>
+</div>
+
+
+## Usage Tips
+
+### Generate text
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+### Distributed Generation with Tensor Parallelism
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    tp_plan="auto",
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+### Quantization with Bitsandbytes
+
+```python
+import torch
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer
+
+model_name = "baidu/ERNIE-4.5-21B-A3B-PT"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
+)
+
+# prepare the model input
+inputs = tokenizer("Hey, are you conscious? Can you talk to me?", return_tensors="pt")
+prompt = "Hey, are you conscious? Can you talk to me?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+```
+
+This model was contributed by [Anton Vlasjuk](https://huggingface.co/AntonV).
+The original code can be found [here](https://github.com/PaddlePaddle/ERNIE).
+
+
+## Ernie4_5_MoeConfig
+
+[[autodoc]] Ernie4_5_MoeConfig
+
+## Ernie4_5_MoeModel
+
+[[autodoc]] Ernie4_5_MoeModel
+    - forward
+
+## Ernie4_5_MoeForCausalLM
+
+[[autodoc]] Ernie4_5_MoeForCausalLM
+    - forward
+    - generate

docs/source/en/model_doc/evolla.md

@@ -0,0 +1,95 @@
+<!--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.
+
+-->
+
+# Evolla
+
+## Overview
+
+The Evolla model was proposed in [Decoding the Molecular Language of Proteins with Evolla](https://doi.org/10.1101/2025.01.05.630192) by [Zhou et al.](https://doi.org/10.1101/2025.01.05.630192).
+
+Evolla is an advanced 80-billion-parameter protein-language generative model designed to decode the molecular language of proteins. It integrates information from protein sequences, structures, and user queries to generate precise and contextually nuanced insights into protein function. Trained on an unprecedented AI-generated dataset of 546 million protein question-answer pairs and 150 billion word tokens, Evolla significantly advances research in proteomics and functional genomics, providing expert-level insights and shedding light on the molecular logic encoded in proteins.
+
+The abstract from the paper is the following:
+
+*Proteins, nature’s intricate molecular machines, are the products of billions of years of evolution and play fundamental roles in sustaining life. Yet, deciphering their molecular language - that is, understanding how protein sequences and structures encode and determine biological functions - remains a corner-stone challenge in modern biology. Here, we introduce Evolla, an 80 billion frontier protein-language generative model designed to decode the molecular language of proteins. By integrating information from protein sequences, structures, and user queries, Evolla generates precise and contextually nuanced insights into protein function. A key innovation of Evolla lies in its training on an unprecedented AI-generated dataset: 546 million protein question-answer pairs and 150 billion word tokens, designed to reflect the immense complexity and functional diversity of proteins. Post-pretraining, Evolla integrates Direct Preference Optimization (DPO) to refine the model based on preference signals and Retrieval-Augmented Generation (RAG) for external knowledge incorporation, improving response quality and relevance. To evaluate its performance, we propose a novel framework, Instructional Response Space (IRS), demonstrating that Evolla delivers expert-level insights, advancing research in proteomics and functional genomics while shedding light on the molecular logic encoded in proteins. The online demo is available at http://www.chat-protein.com/.*
+
+Examples:
+
+```python
+processor = EvollaProcessor.from_pretrained("westlake-repl/Evolla-10B-DPO-hf")
+model = EvollaForProteinText2Text.from_pretrained("westlake-repl/Evolla-10B-DPO-hf")
+# aa_seq should have same length as foldseek
+protein_inputs = [
+    {
+        
+        "aa_seq": "MATGGRRG...",
+        "foldseek": "###lqpfd...", # hashtag means the low-confidence foldseek tokens
+    },
+    {
+        "aa_seq": "MLPGLALL...",
+        "foldseek": "dfwwkwad...",
+    }
+]
+message_list = [
+    [
+        {
+            "role": "system",
+            "content": "You are an AI expert that can answer any questions about protein.",
+        },
+        {"role": "user", "content": "What is the function of this protein?"},
+    ],
+    [
+        {
+            "role": "system",
+            "content": "You are an AI expert that can answer any questions about protein.",
+        },
+        {"role": "user", "content": "What is the function of this protein?"},
+    ]
+]
+input_dict = processor(
+    protein_informations, messages_list, return_tensors="pt", text_max_length=512, protein_max_length=1024
+)
+with torch.no_grad():
+    generated_ids = hf_model.generate(**input_dict)
+generated_texts = processor.batch_decode(
+    generated_ids, skip_special_tokens=True
+)
+```
+
+Tips:
+
+- This model was contributed by [Xibin Bayes Zhou](https://huggingface.co/XibinBayesZhou).
+- The original code can be found [here](https://github.com/westlake-repl/Evolla).
+
+
+## EvollaConfig
+
+[[autodoc]] EvollaConfig
+
+## EvollaModel
+
+[[autodoc]] EvollaModel
+    - forward
+
+## EvollaForProteinText2Text
+
+[[autodoc]] EvollaForProteinText2Text
+    - forward
+
+## EvollaProcessor
+
+[[autodoc]] EvollaProcessor
+    - __call__

docs/source/en/model_doc/exaone4.md

@@ -0,0 +1,208 @@
+<!--Copyright 2025 The LG AI Research 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.
+
+-->
+
+# EXAONE 4
+
+## Overview
+
+**[EXAONE 4.0](https://github.com/LG-AI-EXAONE/EXAONE-4.0)** model is the language model, which integrates a **Non-reasoning mode** and **Reasoning mode** to achieve both the excellent usability of [EXAONE 3.5](https://github.com/LG-AI-EXAONE/EXAONE-3.5) and the advanced reasoning abilities of [EXAONE Deep](https://github.com/LG-AI-EXAONE/EXAONE-Deep). To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended
+to support Spanish in addition to English and Korean. 
+
+The EXAONE 4.0 model series consists of two sizes: a mid-size **32B** model optimized for high performance, and a small-size **1.2B** model designed for on-device applications.
+
+In the EXAONE 4.0 architecture, we apply new architectural changes compared to previous EXAONE models as below:
+
+1. **Hybrid Attention**: For the 32B model, we adopt hybrid attention scheme, which combines *Local attention (sliding window attention)* with *Global attention (full attention)* in a 3:1 ratio. We do not use RoPE (Rotary Positional Embedding) for global attention for better global context understanding.
+2. **QK-Reorder-Norm**: We reorder the LayerNorm position from the traditional Pre-LN scheme by applying LayerNorm directly to the attention and MLP outputs, and we add RMS normalization right after the Q and K projection. It helps yield better performance on downstream tasks despite consuming more computation.
+
+For more details, please refer to our [technical report](https://arxiv.org/abs/2507.11407), [HuggingFace paper](https://huggingface.co/papers/2507.11407), [blog](https://www.lgresearch.ai/blog/view?seq=576), and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.0).
+
+All model weights including quantized versions are available at [Huggingface Collections](https://huggingface.co/collections/LGAI-EXAONE/exaone-40-686b2e0069800c835ed48375).
+
+
+## Model Details
+
+### Model Specifications
+
+| Model Configuration | 32B | 1.2B |
+|:-------------------|:-----:|:------:|
+| d_model | 5,120 | 2,048 |
+| Number of layers | 64 | 30 |
+| Normalization | QK-Reorder-LN | QK-Reorder-LN |
+| Non-linearity | SwiGLU | SwiGLU |
+| Feedforward dimension | 27,392 | 4,096 |
+| Attention type | Hybrid (3:1 Local-Global) | Global |
+| Head type | GQA | GQA |
+| Number of heads | 40 | 32 |
+| Number of KV heads | 8 | 8 |
+| Head size | 128 | 64 |
+| Max sequence length | 131,072 | 65,536 |
+| RoPE theta | 1,000,000 | 1,000,000 |
+| Tokenizer | BBPE | BBPE |
+| Vocab size | 102,400 | 102,400 |
+| Tied word embedding | False | True |
+| Knowledge cut-off | Nov. 2024 | Nov. 2024 |
+
+
+## Usage tips
+
+### Non-reasoning mode
+
+For general use, you can use the EXAONE 4.0 models with the following example:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "LGAI-EXAONE/EXAONE-4.0-32B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="bfloat16",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# choose your prompt
+prompt = "Explain how wonderful you are"
+prompt = "Explica lo increíble que eres"
+prompt = "너가 얼마나 대단한지 설명해 봐"
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+
+output = model.generate(
+    input_ids.to(model.device),
+    max_new_tokens=128,
+    do_sample=False,
+)
+print(tokenizer.decode(output[0]))
+```
+
+### Reasoning mode
+
+The EXAONE 4.0 models have reasoning capabilities for handling complex problems. You can activate reasoning mode by using the `enable_thinking=True` argument with the tokenizer, which opens a reasoning block that starts with `<think>` tag without closing it.
+
+```python
+messages = [
+    {"role": "user", "content": "Which one is bigger, 3.12 vs 3.9?"}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    enable_thinking=True,
+)
+
+output = model.generate(
+    input_ids.to(model.device),
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95
+)
+print(tokenizer.decode(output[0]))
+```
+
+> [!IMPORTANT]
+> The model generation with reasoning mode can be affected sensitively by sampling parameters, so please refer to the [Usage Guideline](https://github.com/LG-AI-EXAONE/EXAONE-4.0#usage-guideline) on official GitHub page for better quality.
+
+### Agentic tool use
+
+The EXAONE 4.0 models can be used as agents with their tool calling capabilities. You can provide tool schemas to the model for effective tool calling.
+
+```python
+import random
+
+def roll_dice(max_num: int):
+    return random.randint(1, max_num)
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "roll_dice",
+            "description": "Roll a dice with the number 1 to N. User can select the number N.",
+            "parameters": {
+                "type": "object",
+                "required": ["max_num"],
+                "properties": {
+                    "max_num": {
+                        "type": "int",
+                        "description": "Max number of the dice"
+                    }
+                }
+            }
+        }
+    }
+]
+
+messages = [
+    {"role": "user", "content": "Roll D6 dice twice!"}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tools=tools,
+)
+
+output = model.generate(
+    input_ids.to(model.device),
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+)
+print(tokenizer.decode(output[0]))
+```
+
+## Exaone4Config
+
+[[autodoc]] Exaone4Config
+
+## Exaone4Model
+
+[[autodoc]] Exaone4Model
+    - forward
+
+## Exaone4ForCausalLM
+
+[[autodoc]] Exaone4ForCausalLM
+    - forward
+
+## Exaone4ForSequenceClassification
+
+[[autodoc]] Exaone4ForSequenceClassification
+    - forward
+
+## Exaone4ForTokenClassification
+
+[[autodoc]] Exaone4ForTokenClassification
+    - forward
+
+## Exaone4ForQuestionAnswering
+
+[[autodoc]] Exaone4ForQuestionAnswering
+    - forward

docs/source/en/model_doc/falcon_mamba.md

@@ -110,6 +110,13 @@ outputs = model.generate(**inputs, max_new_tokens=100)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
+## FalconMambaCache
+
+[[autodoc]] FalconMambaCache
+    - update_conv_state
+    - update_ssm_state
+    - reset
+
 ## FalconMambaConfig
 
 [[autodoc]] FalconMambaConfig

docs/source/en/model_doc/gemma3.md

@@ -267,3 +267,8 @@ visualizer("<img>What is shown in this image?")
 
 [[autodoc]] Gemma3ForConditionalGeneration
     - forward
+
+## Gemma3ForSequenceClassification
+
+[[autodoc]] Gemma3ForSequenceClassification
+    - forward

docs/source/en/model_doc/gemma3n.md

@@ -29,7 +29,7 @@ rendered properly in your Markdown viewer.
 Gemma3n is a multimodal model with pretrained and instruction-tuned variants, available in E4B and E2B sizes. While
 large portions of the language model architecture are shared with prior Gemma releases, there are many new additions in
 this model, including [Alternating Updates][altup] (AltUp), [Learned Augmented Residual Layer][laurel] (LAuReL),
-[MatFormer][matformer], Per-Layer Embeddings (PLE), activation sparsity, and KV cache sharing. The language model uses
+[MatFormer][matformer], Per-Layer Embeddings (PLE), [Activation Sparsity with Statistical Top-k][spark-transformer], and KV cache sharing. The language model uses
 a similar attention pattern to [Gemma 3](./gemma3.md) with alternating 4 local sliding window self-attention layers for
 every global self-attention layer with a maximum context length of 32k tokens. Gemma 3n introduces
 [MobileNet v5][mobilenetv5] as the vision encoder, using a default resolution of 768x768 pixels, and adds a newly
@@ -121,7 +121,7 @@ echo -e "Plants create energy through a process known as" | transformers run --t
 ## Notes
 
 -   Use [`Gemma3nForConditionalGeneration`] for image-audio-and-text, image-and-text, image-and-audio, audio-and-text,
-    image-only and aduio-only inputs.
+    image-only and audio-only inputs.
 -   Gemma 3n supports multiple images per input, but make sure the images are correctly batched before passing them to
     the processor. Each batch should be a list of one or more images.
 
@@ -201,4 +201,5 @@ echo -e "Plants create energy through a process known as" | transformers run --t
 [gemma3n-collection]: https://huggingface.co/collections/google/gemma-3n
 [laurel]: https://arxiv.org/abs/2411.07501
 [matformer]: https://arxiv.org/abs/2310.07707
+[spark-transformer]: https://arxiv.org/abs/2506.06644
 [usm]: https://arxiv.org/abs/2303.01037

docs/source/en/model_doc/glm4.md

@@ -18,7 +18,37 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-To be released with the official model launch.
+The GLM family welcomes new members [GLM-4-0414](https://arxiv.org/pdf/2406.12793) series models.
+
+The **GLM-4-32B-0414** series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT
+series and DeepSeek’s V3/R1 series. It also supports very user-friendly local deployment features. GLM-4-32B-Base-0414
+was pre-trained on 15T of high-quality data, including substantial reasoning-type synthetic data. This lays the
+foundation for subsequent reinforcement learning extensions. In the post-training stage, we employed human preference
+alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we
+enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening
+the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in engineering code, Artifact
+generation, function calling, search-based Q&A, and report generation. In particular, on several benchmarks, such as
+code generation or specific Q&A tasks, GLM-4-32B-Base-0414 achieves comparable performance with those larger models like
+GPT-4o and DeepSeek-V3-0324 (671B).
+
+**GLM-Z1-32B-0414** is a reasoning model with deep thinking capabilities. This was developed based on GLM-4-32B-0414
+through cold start, extended reinforcement learning, and further training on tasks including mathematics, code, and
+logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to
+solve complex tasks. During training, we also introduced general reinforcement learning based on pairwise ranking
+feedback, which enhances the model's general capabilities.
+
+**GLM-Z1-Rumination-32B-0414** is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research).
+Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking to solve more
+open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future
+development plans). Z1-Rumination is trained through scaling end-to-end reinforcement learning with responses graded by
+the ground truth answers or rubrics and can make use of search tools during its deep thinking process to handle complex
+tasks. The model shows significant improvements in research-style writing and complex tasks.
+
+Finally, **GLM-Z1-9B-0414** is a surprise. We employed all the aforementioned techniques to train a small model (9B).
+GLM-Z1-9B-0414 exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is
+top-ranked among all open-source models of the same size. Especially in resource-constrained scenarios, this model
+achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking
+lightweight deployment.
 
 ## Glm4Config
 

docs/source/en/model_doc/glm4_moe.md

@@ -0,0 +1,35 @@
+<!--Copyright 2025 The ZhipuAI Inc. and The HuggingFace Inc. team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ 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.
+
+-->
+
+# Glm4Moe
+
+## Overview
+
+This will update After model release.
+
+## Glm4MoeConfig
+
+[[autodoc]] Glm4MoeConfig
+
+## Glm4MoeModel
+
+[[autodoc]] Glm4MoeModel
+    - forward
+
+## Glm4MoeForCausalLM
+
+[[autodoc]] Glm4MoeForCausalLM
+    - forward

docs/source/en/model_doc/glm4v.md

@@ -23,6 +23,29 @@ rendered properly in your Markdown viewer.
 
 # GLM-4.1V
 
+## Overview
+
+**GLM-4.1V-9B-Thinking** is a bilingual vision-language model optimized for reasoning, built on GLM-4-9B. It introduces
+a "thinking paradigm" with reinforcement learning, achieving state-of-the-art results among 10B-class models and
+rivaling 72B-scale models. It supports 64k context, 4K resolution, and arbitrary aspect ratios, with an open-source base
+model for further research. You can check our paper [here](https://huggingface.co/papers/2507.01006). and below is a abstract.
+
+*We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal understanding
+and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework.
+We first develop a capable vision foundation model with significant potential through large-scale pre-training, which
+arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum
+Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a
+diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding,
+GUI-based agents, and long document understanding. We open-source GLM-4.1V-9B-Thinking, which achieves state-of-the-art
+performance among models of comparable size. In a comprehensive evaluation across 28 public benchmarks, our model
+outperforms Qwen2.5-VL-7B on nearly all tasks and achieves comparable or even superior performance on 18 benchmarks
+relative to the significantly larger Qwen2.5-VL-72B. Notably, GLM-4.1V-9B-Thinking also demonstrates competitive or
+superior performance compared to closed-source models such as GPT-4o on challenging tasks including long document
+understanding and STEM reasoning, further underscoring its strong capabilities. Code, models and more information
+are released at https://github.com/THUDM/GLM-4.1V-Thinking.*
+
+## Usage
+
 The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
 
 <hfoptions id="usage">

docs/source/en/model_doc/gpt2.md

@@ -57,7 +57,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", torch_dtype=torch.float16, device_map="auto", attn_implementation="sdpa")
 tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
 
-input_ids = tokenzier("Hello, I'm a language model". return_tensors="pt").to("cuda")
+input_ids = tokenizer("Hello, I'm a language model", return_tensors="pt").to("cuda")
 
 output = model.generate(**input_ids, cache_implementation="static")
 print(tokenizer.decode(output[0], skip_special_tokens=True))

docs/source/en/model_doc/granitemoehybrid.md

@@ -48,6 +48,32 @@ for i in output:
 
 This HF implementation is contributed by [Sukriti Sharma](https://huggingface.co/SukritiSharma) and [Alexander Brooks](https://huggingface.co/abrooks9944).
 
+## Notes
+
+- `GraniteMoeHybridForCausalLM` supports padding-free training which concatenates distinct training examples while still processing inputs as separate batches. It can significantly accelerate inference by [~2x](https://github.com/huggingface/transformers/pull/35861#issue-2807873129) (depending on model and data distribution) and reduce memory-usage if there are examples of varying lengths by avoiding unnecessary compute and memory overhead from padding tokens.
+
+  Padding-free training 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`] programmatically generates the set of additional arguments above using `return_seq_idx=True` and `return_flash_attn_kwargs=True`. See the [Improving Hugging Face Training Efficiency Through Packing with Flash Attention](https://huggingface.co/blog/packing-with-FA2) blog post for additional information.
+
+  ```python
+  from transformers import DataCollatorWithFlattening
+
+  # Example of using padding-free training
+  data_collator = DataCollatorWithFlattening(
+      tokenizer=tokenizer,
+      return_seq_idx=True,
+      return_flash_attn_kwargs=True
+  )
+  ```
 
 ## GraniteMoeHybridConfig
 
@@ -61,4 +87,4 @@ This HF implementation is contributed by [Sukriti Sharma](https://huggingface.co
 ## GraniteMoeHybridForCausalLM
 
 [[autodoc]] GraniteMoeHybridForCausalLM
-    - forward
+    - forward

docs/source/en/model_doc/ijepa.md

@@ -1,4 +1,4 @@
-<!--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
@@ -14,53 +14,107 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# I-JEPA
-
-<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="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="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
+# I-JEPA
 
-The I-JEPA model was proposed in [Image-based Joint-Embedding Predictive Architecture](https://huggingface.co/papers/2301.08243) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas.
-I-JEPA is a self-supervised learning method that predicts the representations of one part of an image based on other parts of the same image. This approach focuses on learning semantic features without relying on pre-defined invariances from hand-crafted data transformations, which can bias specific tasks, or on filling in pixel-level details, which often leads to less meaningful representations.
+[I-JEPA](https://huggingface.co/papers/2301.08243) is a self-supervised learning method that learns semantic image representations by predicting parts of an image from other parts of the image. It compares the abstract representations of the image (rather than pixel level comparisons), which avoids the typical pitfalls of data augmentation bias and pixel-level details that don't capture semantic meaning.
 
-The abstract from the paper is the following:
+You can find the original I-JEPA checkpoints under the [AI at Meta](https://huggingface.co/facebook/models?search=ijepa) organization.
+> [!TIP]
+> This model was contributed by [jmtzt](https://huggingface.co/jmtzt).
 
-This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image- based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample tar- get blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transform- ers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/ijepa_architecture.jpg"
-alt="drawing" width="600"/>
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/ijepa_architecture.jpg">
 
-<small> I-JEPA architecture. Taken from the <a href="https://huggingface.co/papers/2301.08243">original paper.</a> </small>
 
-This model was contributed by [jmtzt](https://huggingface.co/jmtzt).
-The original code can be found [here](https://github.com/facebookresearch/ijepa).
+> Click on the I-JEPA models in the right sidebar for more examples of how to apply I-JEPA to different image representation and classification tasks.
 
-## How to use
+The example below demonstrates how to extract image features with [`Pipeline`] or the [`AutoModel`] class.
 
-Here is how to use this model for image feature extraction:
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-```python
+```py
+import torch
+from transformers import pipeline
+feature_extractor = pipeline(
+    task="image-feature-extraction",
+    model="facebook/ijepa_vith14_1k",
+    device=0,
+    torch_dtype=torch.bfloat16
+)
+features = feature_extractor("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", return_tensors=True)  
+
+print(f"Feature shape: {features.shape}")
+
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
 import requests
 import torch
 from PIL import Image
 from torch.nn.functional import cosine_similarity
+from transformers import AutoModel, AutoProcessor  
 
-from transformers import AutoModel, AutoProcessor
+url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"  
+url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
+image_1 = Image.open(requests.get(url_1, stream=True).raw)
+image_2 = Image.open(requests.get(url_2, stream=True).raw)
+
+processor = AutoProcessor.from_pretrained("facebook/ijepa_vith14_1k")  
+model = AutoModel.from_pretrained("facebook/ijepa_vith14_1k", torch_dtype="auto", attn_implementation="sdpa")  
+
+
+def infer(image):  
+    inputs = processor(image, return_tensors="pt")  
+    outputs = model(**inputs)  
+    return outputs.last_hidden_state.mean(dim=1)  
+
+
+embed_1 = infer(image_1)  
+embed_2 = infer(image_2)  
+
+similarity = cosine_similarity(embed_1, embed_2)  
+print(similarity)
+```
+</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 4-bits.
+
+```py
+import torch
+from transformers import BitsAndBytesConfig, AutoModel, AutoProcessor
+from datasets import load_dataset
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
 
 url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
 url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
 image_1 = Image.open(requests.get(url_1, stream=True).raw)
 image_2 = Image.open(requests.get(url_2, stream=True).raw)
 
-model_id = "facebook/ijepa_vith14_1k"
-processor = AutoProcessor.from_pretrained(model_id)
-model = AutoModel.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained("facebook/ijepa_vitg16_22k")
+model = AutoModel.from_pretrained("facebook/ijepa_vitg16_22k", quantization_config=quantization_config, torch_dtype="auto", attn_implementation="sdpa")
+
 
-@torch.no_grad()
 def infer(image):
     inputs = processor(image, return_tensors="pt")
     outputs = model(**inputs)
@@ -74,15 +128,6 @@ similarity = cosine_similarity(embed_1, embed_2)
 print(similarity)
 ```
 
-## Resources
-
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with I-JEPA.
-
-<PipelineTag pipeline="image-classification"/>
-
-- [`IJepaForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
-- See also: [Image classification task guide](../tasks/image_classification)
-
 ## IJepaConfig
 
 [[autodoc]] IJepaConfig
@@ -95,4 +140,5 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 ## IJepaForImageClassification
 
 [[autodoc]] IJepaForImageClassification
-    - forward
+    - forward
+

docs/source/en/model_doc/led.md

@@ -14,62 +14,135 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# LED
-
-<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">
+<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">
+    </div>
 </div>
 
-## Overview
+# LED
 
-The LED model was proposed in [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz
-Beltagy, Matthew E. Peters, Arman Cohan.
+[Longformer-Encoder-Decoder (LED)](https://huggingface.co/papers/2004.05150) is an encoder-decoder  transformer model for sequence-to-sequence tasks like summarization. It extends [Longformer](.longformer), an encoder-only model designed to handle long inputs, by adding a decoder layer. The decoder uses full self-attention on the encoded tokens and previously decoded locations. Because of Longformer's linear self-attention mechanism, LED is more efficient than standard encoder-decoder models when processing long sequences.
 
-The abstract from the paper is the following:
+You can find all the original [LED] checkpoints under the [Ai2](https://huggingface.co/allenai/models?search=led) organization.
 
-*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
-quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
-mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
-longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
-windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
-evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
-contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
-pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
-WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting
-long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization
-dataset.*
+> [!TIP]
+> This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+>
+> Click on the LED models in the right sidebar for more examples of how to apply LED to different language tasks.
 
-## Usage tips
+The example below demonstrates how to summarize text with [`Pipeline`], [`AutoModel`], and from the command line.
 
-- [`LEDForConditionalGeneration`] is an extension of
-  [`BartForConditionalGeneration`] exchanging the traditional *self-attention* layer with
-  *Longformer*'s *chunked self-attention* layer. [`LEDTokenizer`] is an alias of
-  [`BartTokenizer`].
-- LED works very well on long-range *sequence-to-sequence* tasks where the `input_ids` largely exceed a length of
-  1024 tokens.
-- LED pads the `input_ids` to be a multiple of `config.attention_window` if required. Therefore a small speed-up is
-  gained, when [`LEDTokenizer`] is used with the `pad_to_multiple_of` argument.
-- LED makes use of *global attention* by means of the `global_attention_mask` (see
-  [`LongformerModel`]). For summarization, it is advised to put *global attention* only on the first
-  `<s>` token. For question answering, it is advised to put *global attention* on all tokens of the question.
-- To fine-tune LED on all 16384, *gradient checkpointing* can be enabled in case training leads to out-of-memory (OOM)
-  errors. This can be done by executing `model.gradient_checkpointing_enable()`. 
- Moreover, the `use_cache=False`
-  flag can be used to disable the caching mechanism to save memory.
-- LED is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
-  the left.
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+```python
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+    task="summarization",
+    model="allenai/led-base-16384",
+    torch_dtype=torch.float16,
+    device=0
+)
+pipeline("""Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet.
+Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts. In the presence of light, plants absorb carbon dioxide from the atmosphere through small pores in their leaves called stomata, and take in water from the soil through their root systems.
+These ingredients are then transformed into glucose, a type of sugar that serves as a source of chemical energy, and oxygen, which is released as a byproduct into the atmosphere. The glucose produced during photosynthesis is not just used immediately; plants also store it as starch or convert it into other organic compounds like cellulose, which is essential for building their cellular structure.
+This energy reserve allows them to grow, develop leaves, produce flowers, bear fruit, and carry out various physiological processes throughout their lifecycle.""")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "allenai/led-base-16384"
+)
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "allenai/led-base-16384",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+input_text = """Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet.
+Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts. In the presence of light, plants absorb carbon dioxide from the atmosphere through small pores in their leaves called stomata, and take in water from the soil through their root systems.
+These ingredients are then transformed into glucose, a type of sugar that serves as a source of chemical energy, and oxygen, which is released as a byproduct into the atmosphere. The glucose produced during photosynthesis is not just used immediately; plants also store it as starch or convert it into other organic compounds like cellulose, which is essential for building their cellular structure.
+This energy reserve allows them to grow, develop leaves, produce flowers, bear fruit, and carry out various physiological processes throughout their lifecycle."""
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+# Place global attention on the first token
+global_attention_mask = torch.zeros_like(input_ids.input_ids).to("cuda")
+global_attention_mask[:, 0] = 1
+
+output = model.generate(**input_ids, global_attention_mask=global_attention_mask, cache_implementation="static")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+<hfoption id="transformers-cli">
+
+```bash
+!echo -e "Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet. Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts." | transformers-cli run --task summarization --model allenai/led-base-16384 --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
+import torch
+from transformers import BitsAndBytesConfig, AutoModelForSeq2SeqLM, AutoTokenizer
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4"
+)
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "allenai/led-large-16384",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=quantization_config
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "allenai/led-large-16384"
+)
+
+input_text = """Plants are among the most remarkable and essential life forms on Earth, possessing a unique ability to produce their own food through a process known as photosynthesis. This complex biochemical process is fundamental not only to plant life but to virtually all life on the planet.
+Through photosynthesis, plants capture energy from sunlight using a green pigment called chlorophyll, which is located in specialized cell structures called chloroplasts. In the presence of light, plants absorb carbon dioxide from the atmosphere through small pores in their leaves called stomata, and take in water from the soil through their root systems.
+These ingredients are then transformed into glucose, a type of sugar that serves as a source of chemical energy, and oxygen, which is released as a byproduct into the atmosphere. The glucose produced during photosynthesis is not just used immediately; plants also store it as starch or convert it into other organic compounds like cellulose, which is essential for building their cellular structure.
+This energy reserve allows them to grow, develop leaves, produce flowers, bear fruit, and carry out various physiological processes throughout their lifecycle."""
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+# Place global attention on the first token
+global_attention_mask = torch.zeros_like(input_ids.input_ids).to("cuda")
+global_attention_mask[:, 0] = 1
+
+output = model.generate(**input_ids, global_attention_mask=global_attention_mask, cache_implementation="static")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Notes
+
+- [`LEDForConditionalGeneration`] is an extension of [`BartForConditionalGeneration`] exchanging the traditional self-attention layer with Longformer's chunked self-attention layer. [`LEDTokenizer`] is an alias of [`BartTokenizer`].
+- LED pads the `input_ids` to be a multiple of `config.attention_window` if required. A small speedup is gained when [`LEDTokenizer`] is used with the `pad_to_multiple_of` argument.
+- LED works best on long-range sequence-to-sequence tasks where the `input_ids` are significantly longer than 1024 tokens.
+- LED uses global attention by means of the `global_attention_mask` (see [`LongformerModel`]). For summarization, it is advised to put global attention only on the first `<s>` token. For question answering, it is advised to put global attention on all tokens of the question.
+- To fine-tune LED on all 16384 parameters, gradient checkpointing can be enabled in case training leads to out-of-memory (OOM) errors. Enable gradient checkpointing by adding `model.gradient_checkpointing_enable()` and setting `use_cache=False` to disable the caching mechanism to save memory.
+- Inputs should be padded on the right because LED uses absolute position embeddings.
 
 ## Resources
 
-- [A notebook showing how to evaluate LED](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing).
-- [A notebook showing how to fine-tune LED](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing).
-- [Text classification task guide](../tasks/sequence_classification)
-- [Question answering task guide](../tasks/question_answering)
-- [Translation task guide](../tasks/translation)
-- [Summarization task guide](../tasks/summarization)
+- Read the [LED on Arxiv notebook](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing) to see how LED can achieve state-of-the-art performance on Arxiv article summarization.
+- Read the [Fine-tune LED notebook](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing) to learn how to fine-tune LED on PubMed articles.
 
 ## LEDConfig
 

docs/source/en/model_doc/lfm2.md

@@ -0,0 +1,84 @@
+<!--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 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>
+
+# LFM2
+
+## Overview
+
+[LFM2](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models) represents a new generation of Liquid Foundation Models developed by [Liquid AI](https://liquid.ai/), specifically designed for edge AI and on-device deployment. 
+
+The models are available in three sizes (350M, 700M, and 1.2B parameters) and are engineered to run efficiently on CPU, GPU, and NPU hardware, making them particularly well-suited for applications requiring low latency, offline operation, and privacy.
+
+## Architecture
+
+The architecture consists of 16 blocks total: 10 double-gated short-range convolution blocks and 6 blocks of grouped query attention. This design stems from the concept of dynamical systems, where linear operations are modulated by input-dependent gates, allowing for "liquid" dynamics that can adapt in real-time. The short convolutions are particularly optimized for embedded SoC CPUs, making them ideal for devices that require fast, local inference without relying on cloud connectivity.
+
+The key architectural innovation of LFM2 lies in its systematic approach to balancing quality, latency, and memory efficiency through our STAR neural architecture search engine. Using STAR, Liquid AI optimized the models for real-world performance on embedded hardware, measuring actual peak memory usage and inference speed on Qualcomm Snapdragon processors. This results in models that achieve 2x faster decode and prefill performance compared to similar-sized models, while maintaining superior benchmark performance across knowledge, mathematics, instruction following, and multilingual tasks.
+
+## Example
+
+The following example shows how to generate an answer using the `AutoModelForCausalLM` class.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_id = "LiquidAI/LFM2-1.2B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="bfloat16",
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Generate answer
+prompt = "What is C. elegans?"
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.3,
+    min_p=0.15,
+    repetition_penalty=1.05,
+    max_new_tokens=512,
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+```
+
+## Lfm2Config
+
+[[autodoc]] Lfm2Config
+
+## Lfm2Model
+
+[[autodoc]] Lfm2Model
+    - forward
+
+## Lfm2ForCausalLM
+
+[[autodoc]] Lfm2ForCausalLM
+    - forward

docs/source/en/model_doc/lightglue.md

@@ -10,37 +10,31 @@ 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" >
+    </div>
+</div>
+
 # LightGlue
 
-## Overview
+[LightGlue](https://arxiv.org/abs/2306.13643) is a deep neural network that learns to match local features across images. It revisits multiple design decisions of SuperGlue and derives simple but effective improvements. Cumulatively, these improvements make LightGlue more efficient - in terms of both memory and computation, more accurate, and much easier to train. Similar to [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor), this model consists of matching two sets of local features extracted from two images, with the goal of being faster than SuperGlue. Paired with the [SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and estimate the pose between them.
 
-The LightGlue model was proposed in [LightGlue: Local Feature Matching at Light Speed](https://arxiv.org/abs/2306.13643)
-by Philipp Lindenberger, Paul-Edouard Sarlin and Marc Pollefeys.
+You can find all the original LightGlue checkpoints under the [ETH-CVG](https://huggingface.co/ETH-CVG) organization.
 
-Similar to [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor), this model consists of matching
-two sets of local features extracted from two images, its goal is to be faster than SuperGlue. Paired with the 
-[SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and 
-estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc.
+> [!TIP]
+> This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
+>
+> Click on the LightGlue models in the right sidebar for more examples of how to apply LightGlue to different computer vision tasks.
 
-The abstract from the paper is the following:
+The example below demonstrates how to match keypoints between two images with the [`AutoModel`] class.
 
-*We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple
-design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improvements. 
-Cumulatively, they make LightGlue more efficient - in terms of both memory and computation, more accurate, and much
-easier to train. One key property is that LightGlue is adaptive to the difficulty of the problem: the inference is much
-faster on image pairs that are intuitively easy to match, for example because of a larger visual overlap or limited
-appearance change. This opens up exciting prospects for deploying deep matchers in latency-sensitive applications like
-3D reconstruction. The code and trained models are publicly available at this [https URL](https://github.com/cvg/LightGlue)*
+<hfoptions id="usage">
+<hfoption id="AutoModel">
 
-## How to use
-
-Here is a quick example of using the model. Since this model is an image matching model, it requires pairs of images to be matched. 
-The raw outputs contain the list of keypoints detected by the keypoint detector as well as the list of matches with their corresponding 
-matching scores.
-```python
+```py
 from transformers import AutoImageProcessor, AutoModel
 import torch
 from PIL import Image
@@ -59,31 +53,70 @@ model = AutoModel.from_pretrained("ETH-CVG/lightglue_superpoint")
 inputs = processor(images, return_tensors="pt")
 with torch.no_grad():
     outputs = model(**inputs)
-```
 
-You can use the `post_process_keypoint_matching` method from the `LightGlueImageProcessor` to get the keypoints and matches in a readable format:
-```python
+# Post-process to get keypoints and matches
 image_sizes = [[(image.height, image.width) for image in images]]
-outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
-for i, output in enumerate(outputs):
-    print("For the image pair", i)
-    for keypoint0, keypoint1, matching_score in zip(
-            output["keypoints0"], output["keypoints1"], output["matching_scores"]
-    ):
-        print(
-            f"Keypoint at coordinate {keypoint0.numpy()} in the first image matches with keypoint at coordinate {keypoint1.numpy()} in the second image with a score of {matching_score}."
-        )
+processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
 ```
 
-You can visualize the matches between the images by providing the original images as well as the outputs to this method:
-```python
-processor.plot_keypoint_matching(images, outputs)
-```
+</hfoption>
+</hfoptions>
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/duPp09ty8NRZlMZS18ccP.png)
+## Notes
 
-This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
-The original code can be found [here](https://github.com/cvg/LightGlue).
+- LightGlue is adaptive to the task difficulty. Inference is much faster on image pairs that are intuitively easy to match, for example, because of a larger visual overlap or limited appearance change.
+
+    ```py
+    from transformers import AutoImageProcessor, AutoModel
+    import torch
+    from PIL import Image
+    import requests
+    
+    processor = AutoImageProcessor.from_pretrained("ETH-CVG/lightglue_superpoint")
+    model = AutoModel.from_pretrained("ETH-CVG/lightglue_superpoint")
+    
+    # LightGlue requires pairs of images
+    images = [image1, image2]
+    inputs = processor(images, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    # Extract matching information
+    keypoints0 = outputs.keypoints0  # Keypoints in first image
+    keypoints1 = outputs.keypoints1  # Keypoints in second image
+    matches = outputs.matches        # Matching indices
+    matching_scores = outputs.matching_scores  # Confidence scores
+    ```
+
+- The model outputs matching indices, keypoints, and confidence scores for each match, similar to SuperGlue but with improved efficiency.
+- For better visualization and analysis, use the [`LightGlueImageProcessor.post_process_keypoint_matching`] method to get matches in a more readable format.
+
+    ```py
+    # Process outputs for visualization
+    image_sizes = [[(image.height, image.width) for image in images]]
+    processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
+    
+    for i, output in enumerate(processed_outputs):
+        print(f"For the image pair {i}")
+        for keypoint0, keypoint1, matching_score in zip(
+                output["keypoints0"], output["keypoints1"], output["matching_scores"]
+        ):
+            print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
+    ```
+
+- Visualize the matches between the images using the built-in plotting functionality.
+
+    ```py
+    # Easy visualization using the built-in plotting method
+    processor.plot_keypoint_matching(images, processed_outputs)
+    ```
+
+<div class="flex justify-center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/duPp09ty8NRZlMZS18ccP.png">
+</div>
+
+## Resources
+
+- Refer to the [original LightGlue repository](https://github.com/cvg/LightGlue) for more examples and implementation details.
 
 ## LightGlueConfig
 
@@ -97,8 +130,13 @@ The original code can be found [here](https://github.com/cvg/LightGlue).
 - post_process_keypoint_matching
 - plot_keypoint_matching
 
+<frameworkcontent>
+<pt>
 ## LightGlueForKeypointMatching
 
 [[autodoc]] LightGlueForKeypointMatching
 
 - forward
+
+</pt>
+</frameworkcontent>

docs/source/en/model_doc/llava_next.md

@@ -14,287 +14,178 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# LLaVA-NeXT
-
-<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="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="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
+# LLaVA-NeXT
 
-The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa](llava) by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning.
+[LLaVA‑NeXT](https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/) improves on [Llava](./llava) by increasing the input image resolution by 4x more pixels and supporting 3 aspect ratios (up to 672x672, 336x1344, 1344x336) to better grasp visual details. It is also trained on an improved visual instruction tuning dataset covering more scenarios and applications to improve OCR and common sense reasoning.
 
-The introduction from the blog is the following:
+You can find all the original LLaVA‑NeXT checkpoints under the [LLaVA-NeXT](https://huggingface.co/collections/llava-hf/llava-next-65f75c4afac77fd37dbbe6cf) collection.
 
-*In October 2023, we released LLaVA-1.5 with a simple and efficient design along with great performance on a benchmark suite of 12 datasets. It has since served as the foundation of many comprehensive studies of data, model, and capabilities of large multimodal models (LMM), and has enabled various new applications.
+> [!TIP]
+> This model was contributed by [nielsr](https://huggingface.co/nielsr).
+>
+> Click on the LLaVA‑NeXT models in the right sidebar for more examples of how to apply Llava-NeXT to different multimodal tasks.
 
-Today, we are thrilled to present LLaVA-NeXT, with improved reasoning, OCR, and world knowledge. LLaVA-NeXT even exceeds Gemini Pro on several benchmarks.
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
 
-Compared with LLaVA-1.5, LLaVA-NeXT has several improvements:
+<hfoptions id="usage">
 
-Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details. It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution.
-Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.
-Better visual conversation for more scenarios, covering different applications. Better world knowledge and logical reasoning.
-Efficient deployment and inference with SGLang.
-Along with performance improvements, LLaVA-NeXT maintains the minimalist design and data efficiency of LLaVA-1.5. It re-uses the pretrained connector of LLaVA-1.5, and still uses less than 1M visual instruction tuning samples. The largest 34B variant finishes training in ~1 day with 32 A100s.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava_next_overview.png"
-alt="drawing" width="600"/>
-
-<small> LLaVa-NeXT incorporates a higher input resolution by encoding various patches of the input image. Taken from the <a href="https://huggingface.co/papers/2310.03744">original paper.</a> </small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr).
-The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/main).
-
-## Usage tips
-
-- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
-
-<Tip warning={true}>
-
-- Llava-Next uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is "left-padding" if model is in `eval()` mode, otherwise "right-padding".
-
-</Tip>
-
-
-> [!NOTE]
-> LLaVA models after release v4.46 will raise warnings about adding `processor.patch_size = {{patch_size}}`, `processor.num_additional_image_tokens = {{num_additional_image_tokens}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by you.
-Adding these attributes means that LLaVA will try to infer the number of image tokens required per image and expand the text with as many `<image>` placeholders as there will be tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there will be failure when merging the embeddings.
-The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches.
-
-
-### Formatting Prompts with Chat Templates  
-
-Each **checkpoint** is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.  
-
-**Important:**  
-- You must construct a conversation history — passing a plain string won't work.  
-- Each message should be a dictionary with `"role"` and `"content"` keys.  
-- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.  
-
-
-Here’s an example of how to structure your input. We will use [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) and a conversation history of text and image.
+<hfoption id="Pipeline">
 
 ```python
-from transformers import LlavaNextProcessor
+import torch  
+from transformers import pipeline  
 
-processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
-
-conversation = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": "What’s shown in this image?"},
-        ],
-    },
-    {
-        "role": "assistant",
-        "content": [{"type": "text", "text": "This image shows a red stop sign."},]
-    },
-    {
-
-        "role": "user",
-        "content": [
-            {"type": "text", "text": "Describe the image in more details."},
-        ],
-    },
-]
-
-text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
-
-# Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your images
-print(text_prompt)
->>> "[INST] <image>\nWhat's shown in this image? [/INST] This image shows a red stop sign. [INST] Describe the image in more details. [/INST]"
+pipeline = pipeline(  
+    task="image-text-to-text",  
+    model="llava-hf/llava-v1.6-mistral-7b-hf",  
+    device=0,  
+    torch_dtype=torch.bfloat16  
+)  
+messages = [  
+    {  
+        "role": "user",  
+        "content": [  
+            {  
+                "type": "image",  
+                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",  
+            },  
+            { "type": "text", "text": "Describe this image."},  
+        ]  
+    }  
+]  
+pipeline(text=messages, max_new_tokens=20, return_full_text=False)
 ```
 
-- If you want to construct a chat prompt yourself, below is a list of possible formats
-.
-[llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) requires the following format:
-```bash
-"[INST] <image>\nWhat is shown in this image? [/INST]"
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+import torch  
+import requests  
+from PIL import Image  
+from transformers import AutoProcessor, LlavaNextForConditionalGeneration  
+
+processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")  
+model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16).to("cuda")  
+
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"  
+image = Image.open(requests.get(url, stream=True).raw)  
+
+conversation = [  
+    {  
+        "role": "user",  
+        "content": [  
+            {"type": "image"},  
+            {"type": "text", "text": "What is shown in this image?"},  
+        ],  
+    },  
+]  
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)  
+inputs = processor(image, prompt, return_tensors="pt").to("cuda")  
+output = model.generate(**inputs, max_new_tokens=100)  
+print(processor.decode(output[0], skip_special_tokens=True))  
 ```
 
-[llava-v1.6-vicuna-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-vicuna-7b-hf) and [llava-v1.6-vicuna-13b-hf](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) require the following format:
-```bash
-"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
+</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
+import torch  
+import requests  
+from PIL import Image  
+from transformers import AutoModelForImageTextToText, AutoProcessor, BitsAndBytesConfig  
+
+quant_config = BitsAndBytesConfig(  
+    load_in_4bit=True,  
+    bnb_4bit_compute_dtype=torch.float16,  
+    bnb_4bit_quant_type="nf4"  
+)  
+
+processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")  
+model = AutoModelForImageTextToText.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", quantization_config=quant_config, device_map="auto")  
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava_next_ocr.png"  
+image = Image.open(requests.get(url, stream=True).raw)  
+
+conversation = [  
+    {  
+        "role": "user",  
+        "content": [  
+            {"type": "image"},  
+            {"type": "text", "text": "What does this chart show?"},  
+        ],  
+    },  
+]  
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)  
+inputs = processor(image, prompt, return_tensors="pt").to("cuda")  
+
+with torch.inference_mode():  
+    output = model.generate(**inputs, max_new_tokens=100)  
+print(processor.decode(output[0], skip_special_tokens=True))  
 ```
 
-[llava-v1.6-34b-hf](https://huggingface.co/llava-hf/llava-v1.6-34b-hf) requires the following format:
-```bash
-"<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
+
+## Notes
+
+* Different checkpoints (Mistral, Vicuna, etc.) require a specific prompt format depending on the underlying LLM. Always use [`~ProcessorMixin.apply_chat_template`] to ensure correct formatting. Refer to the [Templates](../chat_templating) guide for more details.
+
+* Set `padding_side="left"` during batched generation for more accurate results.
+
+```py
+processor.tokenizer.padding_side = "left"
 ```
 
-[llama3-llava-next-8b-hf](https://huggingface.co/llava-hf/llava-next-8b-hf) requires the following format:
+* LLaVA-NeXT uses different numbers of patches for images and pads the inputs inside the modeling code except when padding is done during processing. The default setting is *left-padding* if the model is in `eval()` mode, otherwise it is *right-padding*.
 
-```bash
-"<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.<|eot_id|><|start_header_id|><|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat is shown in this image?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-```
+* LLaVA models after v4.46 raises warnings about adding `processor.patch_size = {{patch_size}}`, `processor.num_additional_image_tokens = {{num_additional_image_tokens}}`, and `processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. It is strongly recommended to add these attributes to the processor if you own the model checkpoint or open a PR if it isn't.
 
-[llava-next-72b-hf](https://huggingface.co/llava-hf/llava-next-72b-hf) and [llava-next-110b-hf](https://huggingface.co/llava-hf/llava-next-110b-hf) require the following format:
+  Adding these attributes means LLaVA will try to infer the number of image tokens required per image and expand the text with the same number of `<image>` token placeholders. There are usually ~500 tokens per image, so make sure the text is not truncated because it will cause a failure when merging the embeddings. The attributes can be found in `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`.
 
-```bash
-"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
-```
+  The `num_additional_image_tokens` should be `1` if the vision backbone adds a `CLS` token or `0` if nothing extra is added.
 
-🚀 **Bonus:** If you're using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the **Usage Examples** below for more details on how to use it.
-
-
-
-## Usage example
-
-### Single image inference
-
-Here's how to load the model and perform inference in half-precision (`torch.float16`):
+* The example below demonstrates inference with multiple input images.
 
 ```python
 from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
-import torch
 from PIL import Image
-import requests
+import requests, torch
 
 processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+model = LlavaNextForConditionalGeneration.from_pretrained(
+    "llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16
+).to("cuda")
 
-model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16)
-model.to("cuda:0")
+# Load multiple images
+url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava_next_ocr.png"
+url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava_next_comparison.png"
 
-# prepare image and text prompt, using the appropriate prompt template
-url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
-image = Image.open(requests.get(url, stream=True).raw)
+image1 = Image.open(requests.get(url1, stream=True).raw)
+image2 = Image.open(requests.get(url2, stream=True).raw)
 
 conversation = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": "What is shown in this image?"},
-        ],
-    },
+    {"role": "user", "content": [{"type": "image"}, {"type": "image"}, {"type": "text", "text": "Compare these two images and describe the differences."}]}
 ]
 prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
-inputs = processor(image, prompt, return_tensors="pt").to("cuda:0")
+inputs = processor([image1, image2], prompt, return_tensors="pt").to("cuda")
 
-# autoregressively complete prompt
 output = model.generate(**inputs, max_new_tokens=100)
-
 print(processor.decode(output[0], skip_special_tokens=True))
 ```
 
-### Multi image inference
-
-LLaVa-Next can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). Here is how you can do it:
-
-```python
-import requests
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoModelForImageTextToText
-
-# Load the model in half-precision
-model = AutoModelForImageTextToText.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16, device_map="auto")
-processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
-
-# Get three different images
-url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-image_stop = Image.open(requests.get(url, stream=True).raw)
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image_cats = Image.open(requests.get(url, stream=True).raw)
-
-url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
-image_snowman = Image.open(requests.get(url, stream=True).raw)
-
-# Prepare a batch of two prompts, where the first one is a multi-turn conversation and the second is not
-conversation_1 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": "What is shown in this image?"},
-            ],
-    },
-    {
-        "role": "assistant",
-        "content": [
-            {"type": "text", "text": "There is a red stop sign in the image."},
-            ],
-    },
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": "What about this image? How many cats do you see?"},
-            ],
-    },
-]
-
-conversation_2 = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": "What is shown in this image?"},
-            ],
-    },
-]
-
-prompt_1 = processor.apply_chat_template(conversation_1, add_generation_prompt=True)
-prompt_2 = processor.apply_chat_template(conversation_2, add_generation_prompt=True)
-prompts = [prompt_1, prompt_2]
-
-# We can simply feed images in the order they have to be used in the text prompt
-# Each "<image>" token uses one image leaving the next for the subsequent "<image>" tokens
-inputs = processor(images=[image_stop, image_cats, image_snowman], text=prompts, padding=True, return_tensors="pt").to(model.device)
-
-# Generate
-generate_ids = model.generate(**inputs, max_new_tokens=30)
-processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-```
-
-## Model optimization
-
-### Quantization using Bitsandbytes
-
-The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes`, and to have access to a GPU/accelerator that is supported by the library.
-
-<Tip>
-
-bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
-
-We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
-
-</Tip>
-
-Simply change the snippet above with:
-
-```python
-from transformers import AutoModelForImageTextToText, BitsAndBytesConfig
-
-# specify how to quantize the model
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.float16,
-)
-
-model = AutoModelForImageTextToText.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", quantization_config=quantization_config, device_map="auto")
-```
-
-### Use Flash-Attention 2 to further speed-up generation
-
-First make sure to install flash-attn. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
-
-```python
-from transformers import AutoModelForImageTextToText
-
-model = AutoModelForImageTextToText.from_pretrained(
-    model_id,
-    torch_dtype=torch.float16,
-    use_flash_attention_2=True
-).to(0)
-```
 
 ## LlavaNextConfig
 

docs/source/en/model_doc/mamba.md

@@ -28,6 +28,7 @@ You can find all the original Mamba checkpoints under the [State Space Models](h
 
 
 > [!TIP]
+> This model was contributed by [Molbap](https://huggingface.co/Molbap) and [AntonV](https://huggingface.co/AntonV).
 > Click on the Mamba models in the right sidebar for more examples of how to apply Mamba to different language tasks.
 
 The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
@@ -115,6 +116,13 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
   trainer.train()
    ```
 
+## MambaCache
+
+[[autodoc]] MambaCache
+    - update_conv_state
+    - update_ssm_state
+    - reset
+
 ## MambaConfig
 
 [[autodoc]] MambaConfig

docs/source/en/model_doc/mamba2.md

@@ -26,6 +26,7 @@ rendered properly in your Markdown viewer.
 You can find all the original Mamba 2 checkpoints under the [State Space Models](https://huggingface.co/state-spaces) organization, but the examples shown below use [mistralai/Mamba-Codestral-7B-v0.1](https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1) because a Hugging Face implementation isn't supported yet for the original checkpoints.
 
 > [!TIP]
+> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ).
 > Click on the Mamba models in the right sidebar for more examples of how to apply Mamba to different language tasks.
 
 The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.

docs/source/en/model_doc/marian.md

@@ -14,159 +14,138 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# MarianMT
-
-<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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
+<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">
+        <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
-
-A framework for translation models, using the same models as BART. Translations should be similar, but not identical to output in the test set linked to in each model card.
-This model was contributed by [sshleifer](https://huggingface.co/sshleifer).
+# MarianMT
 
 
-## Implementation Notes
 
-- Each model is about 298 MB on disk, there are more than 1,000 models.
-- The list of supported language pairs can be found [here](https://huggingface.co/Helsinki-NLP).
-- Models were originally trained by [Jörg Tiedemann](https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann) using the [Marian](https://marian-nmt.github.io/) C++ library, which supports fast training and translation.
-- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented
-  in a model card.
-- The 80 opus models that require BPE preprocessing are not supported.
-- The modeling code is the same as [`BartForConditionalGeneration`] with a few minor modifications:
+[MarianMT](https://huggingface.co/papers/1804.00344) is a machine translation model trained with the Marian framework which is written in pure C++. The framework includes its own custom auto-differentiation engine and efficient meta-algorithms to train encoder-decoder models like BART.
 
-  - static (sinusoid) positional embeddings (`MarianConfig.static_position_embeddings=True`)
-  - no layernorm_embedding (`MarianConfig.normalize_embedding=False`)
-  - the model starts generating with `pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
-    `<s/>`),
-- Code to bulk convert models can be found in `convert_marian_to_pytorch.py`.
+All MarianMT models are transformer encoder-decoders with 6 layers in each component, use static sinusoidal positional embeddings, don't have a layernorm embedding, and the model starts generating with the prefix `pad_token_id` instead of `<s/>`.
 
 
-## Naming
 
-- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`
-- The language codes used to name models are inconsistent. Two digit codes can usually be found [here](https://developers.google.com/admin-sdk/directory/v1/languages), three digit codes require googling "language
-  code {code}".
-- Codes formatted like `es_AR` are usually `code_{region}`. That one is Spanish from Argentina.
-- The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second
-  group use a combination of ISO-639-5 codes and ISO-639-2 codes.
+You can find all the original MarianMT checkpoints under the [Language Technology Research Group at the University of Helsinki](https://huggingface.co/Helsinki-NLP/models?search=opus-mt) organization.
 
 
-## Examples
+> [!TIP]
+> This model was contributed by [sshleifer](https://huggingface.co/sshleifer).
+>
+> Click on the MarianMT models in the right sidebar for more examples of how to apply MarianMT to translation tasks.
 
-- Since Marian models are smaller than many other translation models available in the library, they can be useful for
-  fine-tuning experiments and integration tests.
-- [Fine-tune on GPU](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/train_distil_marian_enro.sh)
 
-## Multilingual Models
+The example below demonstrates how to translate text using [`Pipeline`] or the [`AutoModel`] class.
 
-- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`:
-- If a model can output multiple languages, and you should specify a language code by prepending the desired output
-  language to the `src_text`.
-- You can see a models's supported language codes in its model card, under target constituents, like in [opus-mt-en-roa](https://huggingface.co/Helsinki-NLP/opus-mt-en-roa).
-- Note that if a model is only multilingual on the source side, like `Helsinki-NLP/opus-mt-roa-en`, no language
-  codes are required.
-
-New multi-lingual models from the [Tatoeba-Challenge repo](https://github.com/Helsinki-NLP/Tatoeba-Challenge)
-require 3 character language codes:
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
 ```python
->>> from transformers import MarianMTModel, MarianTokenizer
 
->>> src_text = [
-...     ">>fra<< this is a sentence in english that we want to translate to french",
-...     ">>por<< This should go to portuguese",
-...     ">>esp<< And this to Spanish",
-... ]
+import torch
+from transformers import pipeline
 
->>> model_name = "Helsinki-NLP/opus-mt-en-roa"
->>> tokenizer = MarianTokenizer.from_pretrained(model_name)
->>> print(tokenizer.supported_language_codes)
-['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']
+pipeline = pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de", torch_dtype=torch.float16, device=0)
+pipeline("Hello, how are you?")
 
->>> model = MarianMTModel.from_pretrained(model_name)
->>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
->>> [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-["c'est une phrase en anglais que nous voulons traduire en français",
- 'Isto deve ir para o português.',
- 'Y esto al español']
 ```
 
-Here is the code to see all available pretrained models on the hub:
+</hfoption>
+
+<hfoption id="AutoModel">
 
 ```python
-from huggingface_hub import list_models
 
-model_list = list_models()
-org = "Helsinki-NLP"
-model_ids = [x.id for x in model_list if x.id.startswith(org)]
-suffix = [x.split("/")[1] for x in model_ids]
-old_style_multi_models = [f"{org}/{s}" for s in suffix if s != s.lower()]
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de", torch_dtype=torch.float16, attn_implementation="sdpa", device_map="auto")
+
+inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, cache_implementation="static")
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
 ```
 
-## Old Style Multi-Lingual Models
+</hfoption>
+</hfoptions>
 
-These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
-group:
-
-```python no-style
-['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
- 'Helsinki-NLP/opus-mt-ROMANCE-en',
- 'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
- 'Helsinki-NLP/opus-mt-de-ZH',
- 'Helsinki-NLP/opus-mt-en-CELTIC',
- 'Helsinki-NLP/opus-mt-en-ROMANCE',
- 'Helsinki-NLP/opus-mt-es-NORWAY',
- 'Helsinki-NLP/opus-mt-fi-NORWAY',
- 'Helsinki-NLP/opus-mt-fi-ZH',
- 'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
- 'Helsinki-NLP/opus-mt-sv-NORWAY',
- 'Helsinki-NLP/opus-mt-sv-ZH']
-GROUP_MEMBERS = {
- 'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
- 'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
- 'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
- 'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
- 'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
- 'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
- 'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
-}
-```
-
-Example of translating english to many romance languages, using old-style 2 character language codes
 
+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.
 
 ```python
->>> from transformers import MarianMTModel, MarianTokenizer
+from transformers.utils.attention_visualizer import AttentionMaskVisualizer
 
->>> src_text = [
-...     ">>fr<< this is a sentence in english that we want to translate to french",
-...     ">>pt<< This should go to portuguese",
-...     ">>es<< And this to Spanish",
-... ]
-
->>> model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"
->>> tokenizer = MarianTokenizer.from_pretrained(model_name)
-
->>> model = MarianMTModel.from_pretrained(model_name)
->>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
->>> tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-["c'est une phrase en anglais que nous voulons traduire en français",
- 'Isto deve ir para o português.',
- 'Y esto al español']
+visualizer = AttentionMaskVisualizer("Helsinki-NLP/opus-mt-en-de")
+visualizer("Hello, how are you?")
 ```
+<div class="flex justify-center">
+   <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/marianmt-attn-mask.png"/>
+</div>
 
-## Resources
+## Notes
 
-- [Translation task guide](../tasks/translation)
-- [Summarization task guide](../tasks/summarization)
-- [Causal language modeling task guide](../tasks/language_modeling)
+- MarianMT models are ~298MB on disk and there are more than 1000 models. Check this [list](https://huggingface.co/Helsinki-NLP) for supported language pairs. The language codes may be inconsistent. Two digit codes can be found [here](https://developers.google.com/admin-sdk/directory/v1/languages) while three digit codes may require further searching.
+- Models that require BPE preprocessing are not supported.
+- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`. Language codes formatted like `es_AR` usually refer to the `code_{region}`. For example, `es_AR` refers to Spanish from Argentina.
+- If a model can output multiple languages, prepend the desired output language to `src_txt` as shown below. New multilingual models from the [Tatoeba-Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge) require 3 character language codes.
+
+```python
+
+from transformers import MarianMTModel, MarianTokenizer
+
+# Model trained on multiple source languages → multiple target languages
+# Example: multilingual to Arabic (arb)
+model_name = "Helsinki-NLP/opus-mt-mul-mul"  # Tatoeba Challenge model
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Prepend the desired output language code (3-letter ISO 639-3)
+src_texts = ["arb>> Hello, how are you today?"]
+
+# Tokenize and translate
+inputs = tokenizer(src_texts, return_tensors="pt", padding=True, truncation=True)
+translated = model.generate(**inputs)
+
+# Decode and print result
+translated_texts = tokenizer.batch_decode(translated, skip_special_tokens=True)
+print(translated_texts[0])
+
+```
+   
+- Older multilingual models use 2 character language codes.
+
+```python
+
+from transformers import MarianMTModel, MarianTokenizer
+
+# Example: older multilingual model (like en → many)
+model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"  # English → French, Spanish, Italian, etc.
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Prepend the 2-letter ISO 639-1 target language code (older format)
+src_texts = [">>fr<< Hello, how are you today?"]
+
+# Tokenize and translate
+inputs = tokenizer(src_texts, return_tensors="pt", padding=True, truncation=True)
+translated = model.generate(**inputs)
+
+# Decode and print result
+translated_texts = tokenizer.batch_decode(translated, skip_special_tokens=True)
+print(translated_texts[0])
+
+```
 
 ## MarianConfig
 

docs/source/en/model_doc/mask2former.md

@@ -77,4 +77,12 @@ The resource should ideally demonstrate something new instead of duplicating an
     - encode_inputs
     - post_process_semantic_segmentation
     - post_process_instance_segmentation
+    - post_process_panoptic_segmentation
+
+## Mask2FormerImageProcessorFast
+
+[[autodoc]] Mask2FormerImageProcessorFast
+    - preprocess
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
     - post_process_panoptic_segmentation

docs/source/en/model_doc/maskformer.md

@@ -76,6 +76,14 @@ This model was contributed by [francesco](https://huggingface.co/francesco). The
     - post_process_instance_segmentation
     - post_process_panoptic_segmentation
 
+## MaskFormerImageProcessorFast
+
+[[autodoc]] MaskFormerImageProcessorFast
+    - preprocess
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation
+
 ## MaskFormerFeatureExtractor
 
 [[autodoc]] MaskFormerFeatureExtractor

docs/source/en/model_doc/mistral.md

@@ -139,6 +139,10 @@ Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/bl
 
 [[autodoc]] MistralConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## MistralModel
 
 [[autodoc]] MistralModel

docs/source/en/model_doc/mistral3.md

@@ -13,116 +13,125 @@ specific language governing permissions and limitations under the License.
 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&amp;logo=pytorch&amp;logoColor=white">
+    </div>
+</div>
 
-# Mistral3
+# Mistral 3
 
-## Overview
+[Mistral 3](https://mistral.ai/news/mistral-small-3) is a latency optimized model with a lot fewer layers to reduce the time per forward pass. This model adds vision understanding and supports long context lengths of up to 128K tokens without compromising performance.
 
-Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
+You can find the original Mistral 3 checkpoints under the [Mistral AI](https://huggingface.co/mistralai/models?search=mistral-small-3) organization.
 
-It is ideal for:
-- Fast-response conversational agents.
-- Low-latency function calling.
-- Subject matter experts via fine-tuning.
-- Local inference for hobbyists and organizations handling sensitive data.
-- Programming and math reasoning.
-- Long document understanding.
-- Visual understanding.
 
-This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez) and [yonigozlan](https://huggingface.co/yonigozlan).
+> [!TIP]
+> This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez) and [yonigozlan](https://huggingface.co/yonigozlan).
+> Click on the Mistral3 models in the right sidebar for more examples of how to apply Mistral3 to different tasks.
 
-The original code can be found [here](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/pixtral.py) and [here](https://github.com/mistralai/mistral-common).
+The example below demonstrates how to generate text for an image with [`Pipeline`] and the [`AutoModel`] class.
 
-## Usage example
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-### Inference with Pipeline
+```py
+import torch
+from transformers import pipeline
 
-Here is how you can use the `image-text-to-text` pipeline to perform inference with the `Mistral3` 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."}
+        ,]
+    ,}
+,]
 
->>> messages = [
-...     {
-...         "role": "user",
-...         "content": [
-...             {
-...                 "type": "image",
-...                 "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
-...             },
-...             {"type": "text", "text": "Describe this image."},
-...         ],
-...     },
-... ]
+pipeline = pipeline(
+    task="image-text-to-text", 
+    model="mistralai/Mistral-Small-3.1-24B-Instruct-2503", 
+    torch_dtype=torch.bfloat16,
+    device=0
+)
+outputs = pipeline(text=messages, max_new_tokens=50, return_full_text=False)
 
->>> pipe = pipeline("image-text-to-text", model="mistralai/Mistral-Small-3.1-24B-Instruct-2503", torch_dtype=torch.bfloat16)
->>> outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
->>> outputs[0]["generated_text"]
+outputs[0]["generated_text"]
 'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'
 ```
-### Inference on a single image
+</hfoption>
+<hfoption id="AutoModel">
 
-This example demonstrates how to perform inference on a single image with the Mistral3 models using chat templates.
+```py
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText 
 
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
->>> import torch
+torch_device = "cuda"
+model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+processor = AutoProcessor.from_pretrained(model_checkpoint)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_checkpoint, 
+    device_map=torch_device, 
+    torch_dtype=torch.bfloat16
+)
 
->>> torch_device = "cuda"
->>> model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
->>> 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",
+            "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",},
+            {"type": "text", "text": "Describe this image."}
+        ,]
+    ,}
+,]
 
->>> messages = [
-...     {
-...         "role": "user",
-...         "content": [
-...             {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
-...             {"type": "text", "text": "Describe this image"},
-...         ],
-...     }
-... ]
+inputs = processor.apply_chat_template(
+    messages, 
+    add_generation_prompt=True, 
+    tokenize=True, return_dict=True, 
+    return_tensors="pt").to(model.device, dtype=torch.bfloat16)
 
->>> 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=20)
+decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
 
->>> generate_ids = model.generate(**inputs, max_new_tokens=20)
->>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
-
->>> decoded_output
-"The image depicts two cats lying on a pink blanket. The larger cat, which appears to be an"...
+decoded_output
+'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'
 ```
+</hfoption>
+</hfoptions>
 
-### Text-only generation
-This example shows how to generate text using the Mistral3 model without providing any image input.
+## Notes 
 
+- Mistral 3 supports text-only generation. 
+```py 
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
 
-````python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
->>> import torch
+torch_device = "cuda"
+model_checkpoint = ".mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+processor = AutoProcessor.from_pretrained(model_checkpoint)
+model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
 
->>> torch_device = "cuda"
->>> model_checkpoint = ".mistralai/Mistral-Small-3.1-24B-Instruct-2503"
->>> processor = AutoProcessor.from_pretrained(model_checkpoint)
->>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
+SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
+user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
 
->>> SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
->>> user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user", "content": user_prompt},
+]
 
->>> messages = [
-...    {"role": "system", "content": SYSTEM_PROMPT},
-...    {"role": "user", "content": user_prompt},
-... ]
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=text, return_tensors="pt").to(0, dtype=torch.float16)
+generate_ids = model.generate(**inputs, max_new_tokens=50, do_sample=False)
+decoded_output = processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0]
 
->>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
->>> inputs = processor(text=text, return_tensors="pt").to(0, dtype=torch.float16)
->>> generate_ids = model.generate(**inputs, max_new_tokens=50, do_sample=False)
->>> decoded_output = processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0]
-
->>> print(decoded_output)
+print(decoded_output)
 "1. À plus tard!
-2. Salut, à plus!
-3. À toute!
-4. À la prochaine!
-5. Je me casse, à plus!
+ 2. Salut, à plus!
+ 3. À toute!
+ 4. À la prochaine!
+ 5. Je me casse, à plus!
 
 ```
  /\_/\
@@ -131,102 +140,101 @@ This example shows how to generate text using the Mistral3 model without providi
 ```"
 ````
 
-### Batched image and text inputs
-Mistral3 models also support batched image and text inputs.
+- Mistral 3 accepts batched image and text inputs. 
+```py
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
 
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText
->>> import torch
+torch_device = "cuda"
+model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+processor = AutoProcessor.from_pretrained(model_checkpoint)
+model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
 
->>> torch_device = "cuda"
->>> model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
->>> 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"},
-...             ],
-...         },
-...     ],
-... ]
+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)
+ 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)
+ output = model.generate(**inputs, max_new_tokens=25)
 
->>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
->>> decoded_outputs
+ decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
+ decoded_outputs
 ["Write a haiku for this imageCalm waters reflect\nWhispers of the forest's breath\nPeace on wooden path"
 , "Describe this imageThe image depicts a vibrant street scene in what appears to be a Chinatown district. The focal point is a traditional Chinese"]
 ```
 
-### Batched multi-image input and quantization with BitsAndBytes
-This implementation of the Mistral3 models supports batched text-images inputs with different number of images for each text.
-This example also how to use `BitsAndBytes` to load the model in 4bit quantization.
+- Mistral 3 also supported batched image and text inputs with a different number of images for each text. The example below quantizes the model with bitsandbytes. 
 
-```python
->>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
->>> import torch
+```py 
+from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
+import torch
 
->>> torch_device = "cuda"
->>> model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
->>> processor = AutoProcessor.from_pretrained(model_checkpoint)
->>> quantization_config = BitsAndBytesConfig(load_in_4bit=True)
->>> model = AutoModelForImageTextToText.from_pretrained(
-...     model_checkpoint, quantization_config=quantization_config
-... )
+torch_device = "cuda"
+model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+processor = AutoProcessor.from_pretrained(model_checkpoint)
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+model = AutoModelForImageTextToText.from_pretrained(
+     model_checkpoint, quantization_config=quantization_config
+ )
 
->>> 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?"},
-...             ],
-...         },
-...     ],
->>> ]
+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)
+ 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)
+ output = model.generate(**inputs, max_new_tokens=25)
 
->>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
->>> decoded_outputs
+ decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
+ decoded_outputs
 ["Write a haiku for this imageSure, here is a haiku inspired by the image:\n\nCalm lake's wooden path\nSilent forest stands guard\n", "These images depict two different landmarks. Can you identify them? Certainly! The images depict two iconic landmarks:\n\n1. The first image shows the Statue of Liberty in New York City."]
 ```
 
-
 ## Mistral3Config
 
 [[autodoc]] Mistral3Config
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## Mistral3Model
 
 [[autodoc]] Mistral3Model

docs/source/en/model_doc/mixtral.md

@@ -197,6 +197,10 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] MixtralConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## MixtralModel
 
 [[autodoc]] MixtralModel

docs/source/en/model_doc/mobilenet_v2.md

@@ -114,6 +114,7 @@ print(f"The predicted class label is: {predicted_class_label}")
 
 [[autodoc]] MobileNetV2ImageProcessor
     - preprocess
+    - post_process_semantic_segmentation
 
 ## MobileNetV2ImageProcessorFast
 

docs/source/en/model_doc/modernbert-decoder.md

@@ -0,0 +1,188 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+<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="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>
+
+# ModernBERT Decoder
+
+ModernBERT Decoder has the same architecture as [ModernBERT](https://huggingface.co/papers/2412.13663) but it is trained from scratch with a causal language modeling objective from the [Ettin paper](https://huggingface.co/papers/2507.11412). This allows for using the same architecture to compare encoders and decoders. This model is the decoder architecture implementation of ModernBERT, designed for autoregressive text generation tasks.
+
+ModernBERT Decoder uses sliding window attention and rotary positional embeddings for efficiency and to handle longer sequences.
+
+You can find all the original ModernBERT Decoder checkpoints under the [jhu-clsp](https://huggingface.co/collections/jhu-clsp/encoders-vs-decoders-the-ettin-suite-686303e16142257eed8e6aeb) collection.
+
+> [!TIP]
+> This model was contributed by [orionw](https://huggingface.co/orionweller).
+>
+> Click on the ModernBERT Decoder models in the right sidebar for more examples of how to apply ModernBERT Decoder to different text generation tasks.
+
+The example below demonstrates how to use ModernBERT Decoder for text generation with [`Pipeline`], [`AutoModel`] (with and without quantization), and from the command line. 
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+generator = pipeline(
+    task="text-generation",
+    model="jhu-clsp/ettin-decoder-17m",
+    torch_dtype=torch.float16,
+    device=0
+)
+generator("The future of artificial intelligence is", max_length=50, num_return_sequences=1)
+
+# For sequence classification
+classifier = pipeline(
+    task="text-classification",
+    model="jhu-clsp/ettin-decoder-17m",
+    torch_dtype=torch.float16,
+    device=0
+)
+classifier("This movie is really great!")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-17m")
+model = AutoModelForCausalLM.from_pretrained(
+    "jhu-clsp/ettin-decoder-17m",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "The future of artificial intelligence is"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=50,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+
+# For sequence classification
+from transformers import AutoModelForSequenceClassification
+
+classifier_model = AutoModelForSequenceClassification.from_pretrained(
+    "jhu-clsp/ettin-decoder-17m",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    num_labels=2
+)
+
+text = "This movie is really great!"
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = classifier_model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+
+print(f"Predicted class: {predicted_class.item()}")
+print(f"Prediction probabilities: {predictions}")
+```
+
+</hfoption>
+
+<hfoption id="AutoModel (w/quantization)">
+
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_8bit=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-1b")
+model = AutoModelForCausalLM.from_pretrained(
+    "jhu-clsp/ettin-decoder-1b",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    quantization_config=quantization_config
+)
+
+prompt = "The future of artificial intelligence is"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=50,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+```
+</hfoption>
+
+<hfoption id="transformers CLI">
+
+```bash
+echo "The future of artificial intelligence is" | transformers run --task text-generation --model jhu-clsp/ettin-decoder-17m --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+
+## ModernBertDecoderConfig
+
+[[autodoc]] ModernBertDecoderConfig
+
+<frameworkcontent>
+<pt>
+
+## ModernBertDecoderModel
+
+[[autodoc]] ModernBertDecoderModel
+    - forward
+
+## ModernBertDecoderForCausalLM
+
+[[autodoc]] ModernBertDecoderForCausalLM
+    - forward
+
+## ModernBertDecoderForSequenceClassification
+
+[[autodoc]] ModernBertDecoderForSequenceClassification
+    - forward
+
+</pt>
+</frameworkcontent>

docs/source/en/model_doc/olmoe.md

@@ -14,27 +14,89 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# OLMoE
-
+<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="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
+# OLMoE
 
-The OLMoE model was proposed in [OLMoE: Open Mixture-of-Experts Language Models](https://huggingface.co/papers/2409.02060) by Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, Hannaneh Hajishirzi.
+[OLMoE](https://huggingface.co/papers/2409.02060) is a sparse Mixture-of-Experts (MoE) language model with 7B parameters but only 1B parameters are used per input token. It has similar inference costs as dense models but trains ~3x faster. OLMoE uses fine-grained routing with 64 small experts in each layer and uses a dropless token-based routing algorithm.
 
-OLMoE is a series of **O**pen **L**anguage **Mo**dels using sparse **M**ixture-**o**f-**E**xperts designed to enable the science of language models. We release all code, checkpoints, logs, and details involved in training these models.
+You can find all the original OLMoE checkpoints under the [OLMoE](https://huggingface.co/collections/allenai/olmoe-november-2024-66cf678c047657a30c8cd3da) collection.
 
-The abstract from the paper is the following:
+> [!TIP]
+> This model was contributed by [Muennighoff](https://hf.co/Muennighoff).
+>
+> Click on the OLMoE models in the right sidebar for more examples of how to apply OLMoE to different language tasks.
 
-*We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain it on 5 trillion tokens and further adapt it to create OLMoE-1B-7B-Instruct. Our models outperform all available models with similar active parameters, even surpassing larger ones like Llama2-13B-Chat and DeepSeekMoE-16B. We present various experiments on MoE training, analyze routing in our model showing high specialization, and open-source all aspects of our work: model weights, training data, code, and logs.*
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class.
 
-This model was contributed by [Muennighoff](https://hf.co/Muennighoff).
-The original code can be found [here](https://github.com/allenai/OLMoE).
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
+```py
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    task="text-generation",
+    model="allenai/OLMoE-1B-7B-0125",
+    torch_dtype=torch.float16,
+    device=0,
+)
+
+result = pipe("Dionysus is the god of")
+print(result)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", attn_implementation="sdpa", torch_dtype="auto", device_map="auto").to(device)
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+
+inputs = tokenizer("Bitcoin is", return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+output = model.generate(**inputs, max_length=64)
+print(tokenizer.decode(output[0]))
+```
+
+## Quantization
+
+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.
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+quantization_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_compute_dtype=torch.float16,
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_quant_type="nf4"
+)
+
+model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", attn_implementation="sdpa", torch_dtype="auto", device_map="auto", quantization_config=quantization_config).to(device)
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+
+inputs = tokenizer("Bitcoin is", return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+output = model.generate(**inputs, max_length=64)
+print(tokenizer.decode(output[0]))
+```
 
 ## OlmoeConfig
 

docs/source/en/model_doc/oneformer.md

@@ -38,7 +38,7 @@ This model was contributed by [Jitesh Jain](https://huggingface.co/praeclarumjj3
 
 ## Usage tips
 
--  OneFormer requires two inputs during inference: *image* and *task token*. 
+-  OneFormer requires two inputs during inference: *image* and *task token*.
 - During training, OneFormer only uses panoptic annotations.
 - If you want to train the model in a distributed environment across multiple nodes, then one should update the
   `get_num_masks` function inside in the `OneFormerLoss` class of `modeling_oneformer.py`. When training on multiple nodes, this should be
@@ -69,7 +69,14 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 [[autodoc]] OneFormerImageProcessor
     - preprocess
-    - encode_inputs
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation
+
+## OneFormerImageProcessorFast
+
+[[autodoc]] OneFormerImageProcessorFast
+    - preprocess
     - post_process_semantic_segmentation
     - post_process_instance_segmentation
     - post_process_panoptic_segmentation
@@ -87,4 +94,3 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 [[autodoc]] OneFormerForUniversalSegmentation
     - forward
-    

docs/source/en/model_doc/opt.md

@@ -1,194 +1,101 @@
-<!--Copyright 2022 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="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,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29vpccbklWK8PVa0AS6ghW63O498vYa+lsdKz1NDRt9Kw1c672tbD3tnAxt7R6OHp5vDe7OrDyuDn6vLl6/EAQKak0MgATakkppo3ZK/Bz9y8w9yzu9jey97axdvHzeG21NHH4trTwthKZrVGZLSUSpuPQJiGAI+GAI8SWKydycLL4d7f2OTi1+S9xNzL0ePT6OLGzeEAo5U0qJw/aLEAo5JFa7JBabEAp5Y4qZ2QxLyKmsm3kL2xoMOehrRNb7RIbbOZgrGre68AUqwAqZqNN5aKJ5N9lMq+qsd8kMa4pcWzh7muhLMEV69juq2kbKqgUaOTR5uMMZWLLZSGAI5VAIdEAH+ovNDHuNCnxcy3qcaYx8K8msGplrx+wLahjbYdXrV6vbMvYK9DrZ8QrZ8tqJuFmsnSos6sw8ecy8RffsNVeMCvmb43aLltv7Q4Y7EZWK4QWa1gt6meZKUdr6GOAZVeA4xPAISyveLUwtivxtKTpNJ2jcqfvcltiMiwwcfAoMVxhL+Kx7xjdrqTe60tsaNQsKKaRKACrJ6UTZwkqpqTL5pkHY4AloSgsd2ptNXPvNOOncuxxsqFl8lmg8apt8FJcr9EbryGxLqlkrkrY7dRa7ZGZLQ5t6iXUZ6PPpgVpZeJCJFKAIGareTa0+KJod3H0deY2M+esM25usmYu8d2zsJOdcBVvrCLbqcAOaaHaKQAMaScSKSBqKCXMJ2RHpiLF5NmJZAdAHN2kta11dKu1M+DkcZLdb+Mcql3TppyRJdzQ5ZtNZNlIY+DF4+voCOQAAAAZ3RSTlMABAT+MEEJ/RH+/TP+Zlv+pUo6Ifz8+fco/fz6+evr39S9nJmOilQaF/7+/f38+smmoYp6b1T+/v7++vj189zU0tDJxsGzsrKSfv34+Pf27dDOysG9t6+n/vv6+vr59uzr1tG+tZ6Qg9Ym3QAABR5JREFUSMeNlVVUG1EQhpcuxEspXqS0SKEtxQp1d3d332STTRpIQhIISQgJhODu7lAoDoUCpe7u7u7+1puGpqnCPOyZvffbOXPm/PsP9JfQgyCC+tmTABTOcbxDz/heENS7/1F+9nhvkHePG0wNDLbGWwdXL+rbLWvpmZHXD82gMfBjTh+aSe6Gnn7lwQIOTR0c8wfX3PWgv7avbdKwf/ZoBp1Gp/PvuvXW3vw5ib7emnTW4OR+3D4jB9vjNJ/7gNvfWWeH/TO/JyYrsiKCRjVEZA3UB+96kON+DxOQ/NLE8PE5iUYgIXjFnCOlxEQMaSGVxjg4gxOnEycGz8bptuNjVx08LscIgrzH3umcn+KKtiBIyvzOO2O99aAdR8cF19oZalnCtbREUw79tCd5sow1g1UKM6kXqUx4T8wsi3sTjJ3yzDmmhenLXLpo8u45eG5y4Vvbk6kkC4LLtJMowkSQxmk4ggVJEG+7c6QpHT8vvW9X7/o7+3ELmiJi2mEzZJiz8cT6TBlanBk70cB5GGIGC1gRDdZ00yADLW1FL6gqhtvNXNG5S9gdSrk4M1qu7JAsmYshzDS4peoMrU/gT7qQdqYGZaYhxZmVbGJAm/CS/HloWyhRUlknQ9KYcExTwS80d3VNOxUZJpITYyspl0LbhArhpZCD9cRWEQuhYkNGMHToQ/2Cs6swJlb39CsllxdXX6IUKh/H5jbnSsPKjgmoaFQ1f8wRLR0UnGE/RcDEjj2jXG1WVTwUs8+zxfcrVO+vSsuOpVKxCfYZiQ0/aPKuxQbQ8lIz+DClxC8u+snlcJ7Yr1z1JPqUH0V+GDXbOwAib931Y4Imaq0NTIXPXY+N5L18GJ37SVWu+hwXff8l72Ds9XuwYIBaXPq6Shm4l+Vl/5QiOlV+uTk6YR9PxKsI9xNJny31ygK1e+nIRC1N97EGkFPI+jCpiHe5PCEy7oWqWSwRrpOvhFzcbTWMbm3ZJAOn1rUKpYIt/lDhW/5RHHteeWFN60qo98YJuoq1nK3uW5AabyspC1BcIEpOhft+SZAShYoLSvnmSfnYADUERP5jJn2h5XtsgCRuhYQqAvwTwn33+YWEKUI72HX5AtfSAZDe8F2DtPPm77afhl0EkthzuCQU0BWApgQIH9+KB0JhopMM7bJrdTRoleM2JAVNMyPF+wdoaz+XJpGoVAQ7WXUkcV7gT3oUZyi/ISIJAVKhgNp+4b4veCFhYVJw4locdSjZCp9cPUhLF9EZ3KKzURepMEtCDPP3VcWFx4UIiZIklIpFNfHpdEafIF2aRmOcrUmjohbT2WUllbmRvgfbythbQO3222fpDJoufaQPncYYuqoGtUEsCJZL6/3PR5b4syeSjZMQG/T2maGANlXT2v8S4AULWaUkCxfLyW8iW4kdka+nEMjxpL2NCwsYNBp+Q61PF43zyDg9Bm9+3NNySn78jMZUUkumqE4Gp7JmFOdP1vc8PpRrzj9+wPinCy8K1PiJ4aYbnTYpCCbDkBSbzhu2QJ1Gd82t8jI8TH51+OzvXoWbnXUOBkNW+0mWFwGcGOUVpU81/n3TOHb5oMt2FgYGjzau0Nif0Ss7Q3XB33hjjQHjHA5E5aOyIQc8CBrLdQSs3j92VG23nNEjbkbdbBr9zm04ruvw37vh0QKOdeGIkckc80fX3KH/h7PT4BOjgCty8VZ5ux1MoO5Cf5naca2LAsEgehI+drX8o/0Nu+W0m6K/I9gGPd/dfx/EN/wN62AhsBWuAAAAAElFTkSuQmCC
+">
+           <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>
 
 # OPT
 
-<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>
+[OPT](https://huggingface.co/papers/2205.01068) is a suite of open-source decoder-only pre-trained transformers whose parameters range from 125M to 175B. OPT models are designed for casual language modeling and aim to enable responsible and reproducible research at scale. OPT-175B is comparable in performance to GPT-3 with only 1/7th the carbon footprint.
 
-## Overview
+You can find all the original OPT checkpoints under the [OPT](https://huggingface.co/collections/facebook/opt-66ed00e15599f02966818844) collection.
 
-The OPT model was proposed in [Open Pre-trained Transformer Language Models](https://huggingface.co/papers/2205.01068) by Meta AI.
-OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3.
+> [!TIP]
+> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ), [ybelkada](https://huggingface.co/ybelkada), and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+>
+> Click on the OPT models in the right sidebar for more examples of how to apply OPT to different language tasks.
 
-The abstract from the paper is the following:
+The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
 
-*Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.*
 
-This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Younes Belkada](https://huggingface.co/ybelkada), and [Patrick Von Platen](https://huggingface.co/patrickvonplaten).
-The original code can be found [here](https://github.com/facebookresearch/metaseq).
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+  
+```py  
+import torch
+from transformers import pipeline
 
-Tips:
-- OPT has the same architecture as [`BartDecoder`].
-- Contrary to GPT2, OPT adds the EOS token `</s>` to the beginning of every prompt.
+pipeline = pipeline(task="text-generation", model="facebook/opt-125m", torch_dtype=torch.float16, device=0)
+pipeline("Once upon a time, in a land far, far away,", max_length=50, num_return_sequences=1)
+```
 
-> [!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")`
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"
+
+model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16, attn_implementation="sdpa")
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+prompt = ("Once upon a time, in a land far, far away, ")
+
+model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
+model.to(device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
+tokenizer.batch_decode(generated_ids)[0]
+```
+</hfoption>
+<hfoption id="transformers CLI">
+
+```py
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model facebook/opt-125m --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 quantize the weights to 8-bits.
+
+```py
+import torch
+from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda"
+
+bnb_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16, attn_implementation="sdpa", quantization_config=bnb_config)
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b")
+
+prompt = ("Once upon a time, in a land far, far away, ")
+
+model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
+model.to(device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
+tokenizer.batch_decode(generated_ids)[0]
+```
+
+## Notes
+
+- OPT adds an `EOS` token `</s>` to the beginning of every prompt.
+
+- The `head_mask` argument is ignored if the attention implementation isn't `"eager"`. Set `attn_implementation="eager"` to enable the `head_mask`.
 
 ## Resources
 
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OPT. If you're
-interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
-The resource should ideally demonstrate something new instead of duplicating an existing resource.
-
-<PipelineTag pipeline="text-generation" />
-
-- A notebook on [fine-tuning OPT with PEFT, bitsandbytes, and Transformers](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing). 🌎
-- A blog post on [decoding strategies with OPT](https://huggingface.co/blog/introducing-csearch#62-example-two---opt).
-- [Causal language modeling](https://huggingface.co/course/en/chapter7/6?fw=pt#training-a-causal-language-model-from-scratch) chapter of the 🤗 Hugging Face Course.
-- [`OPTForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
-- [`TFOPTForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_clmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
-- [`FlaxOPTForCausalLM`] is supported by this [causal language modeling example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#causal-language-modeling).
-
-<PipelineTag pipeline="text-classification" />
-
-- [Text classification task guide](sequence_classification.md)
-- [`OPTForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).
-
-<PipelineTag pipeline="question-answering" />
-
-- [`OPTForQuestionAnswering`] is supported by this [question answering example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).
-- [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter
-  of the 🤗 Hugging Face Course.
-
-⚡️ Inference
-
-- A blog post on [How 🤗 Accelerate runs very large models thanks to PyTorch](https://huggingface.co/blog/accelerate-large-models) with OPT.
-
-
-## Combining OPT and Flash Attention 2
-
-First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
-
-```bash
-pip install -U flash-attn --no-build-isolation
-```
-
-Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)
-
-To load and run a model using Flash Attention 2, refer to the snippet below:
-
-```python
->>> import torch
->>> from transformers import OPTForCausalLM, GPT2Tokenizer
->>> device = "cuda" # the device to load the model onto
-
->>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
->>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
-
->>> prompt = ("A chat between a curious human and the Statue of Liberty.\n\nHuman: What is your name?\nStatue: I am the "
-              "Statue of Liberty.\nHuman: Where do you live?\nStatue: New York City.\nHuman: How long have you lived "
-              "there?")
-
->>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
->>> model.to(device)
-
->>> generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
->>> tokenizer.batch_decode(generated_ids)[0]
-'</s>A chat between a curious human and the Statue of Liberty.\n\nHuman: What is your name?\nStatue: I am the Statue of Liberty.\nHuman: Where do you live?\nStatue: New York City.\nHuman: How long have you lived there?\nStatue: I have lived here for about a year.\nHuman: What is your favorite place to eat?\nStatue: I love'
-```
-
-### Expected speedups
-
-Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `facebook/opt-2.7b` checkpoint and the Flash Attention 2 version of the model using two different sequence lengths.
-
-<div style="text-align: center">
-<img src="https://user-images.githubusercontent.com/49240599/281101546-d2fca6d2-ee44-48f3-9534-ba8d5bee4531.png">
-</div>
-
-Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `facebook/opt-350m` checkpoint and the Flash Attention 2 version of the model using two different sequence lengths.
-
-<div style="text-align: center">
-<img src="https://user-images.githubusercontent.com/49240599/281101682-d1144e90-0dbc-46f4-8fc8-c6206cb793c9.png">
-</div>
-
-
-### Using Scaled Dot Product Attention (SDPA)
-PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
-encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
-[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
-or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
-page for more information.
-
-SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
-`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
-
-```python
-from transformers import OPTForCausalLM
-model = OPTForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16, attn_implementation="sdpa")
-...
-```
-
-For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
-
-On a local benchmark (L40S-45GB, PyTorch 2.4.0, OS Debian GNU/Linux 11) using `float16` with
-[facebook/opt-350m](https://huggingface.co/facebook/opt-350m), we saw the
-following speedups during training and inference.
-
-### Training
-
-|    batch_size |    seq_len |  Time per batch (eager - s)   |    Time per batch (sdpa - s) |  Speedup (%)   |  Eager peak mem (MB)   |    sdpa peak mem (MB) |  Mem saving (%)   |
-|--------------:|-----------:|:------------------------------|-----------------------------:|:---------------|:-----------------------|----------------------:|:------------------|
-|             1 |        128 | 0.047                         |                        0.037 | 26.360         | 1474.611               |               1474.32 | 0.019             |
-|             1 |        256 | 0.046                         |                        0.037 | 24.335         | 1498.541               |               1499.49 | -0.063            |
-|             1 |        512 | 0.046                         |                        0.037 | 24.959         | 1973.544               |               1551.35 | 27.215            |
-|             1 |       1024 | 0.062                         |                        0.038 | 65.135         | 4867.113               |               1698.35 | 186.578           |
-|             1 |       2048 | 0.230                         |                        0.039 | 483.933        | 15662.224              |               2715.75 | 476.718           |
-|             2 |        128 | 0.045                         |                        0.037 | 20.455         | 1498.164               |               1499.49 | -0.089            |
-|             2 |        256 | 0.046                         |                        0.037 | 24.027         | 1569.367               |               1551.35 | 1.161             |
-|             2 |        512 | 0.045                         |                        0.037 | 20.965         | 3257.074               |               1698.35 | 91.778            |
-|             2 |       1024 | 0.122                         |                        0.038 | 225.958        | 9054.405               |               2715.75 | 233.403           |
-|             2 |       2048 | 0.464                         |                        0.067 | 593.646        | 30572.058              |               4750.55 | 543.548           |
-|             4 |        128 | 0.045                         |                        0.037 | 21.918         | 1549.448               |               1551.35 | -0.123            |
-|             4 |        256 | 0.044                         |                        0.038 | 18.084         | 2451.768               |               1698.35 | 44.361            |
-|             4 |        512 | 0.069                         |                        0.037 | 84.421         | 5833.180               |               2715.75 | 114.791           |
-|             4 |       1024 | 0.262                         |                        0.062 | 319.475        | 17427.842              |               4750.55 | 266.860           |
-|             4 |       2048 | OOM                           |                        0.062 | Eager OOM      | OOM                    |               4750.55 | Eager OOM         |
-|             8 |        128 | 0.044                         |                        0.037 | 18.436         | 2049.115               |               1697.78 | 20.694            |
-|             8 |        256 | 0.048                         |                        0.036 | 32.887         | 4222.567               |               2715.75 | 55.484            |
-|             8 |        512 | 0.153                         |                        0.06  | 154.862        | 10985.391              |               4750.55 | 131.245           |
-|             8 |       1024 | 0.526                         |                        0.122 | 330.697        | 34175.763              |               8821.18 | 287.428           |
-|             8 |       2048 | OOM                           |                        0.122 | Eager OOM      | OOM                    |               8821.18 | Eager OOM         |
-
-### Inference
-
-|    batch_size |    seq_len |    Per token latency eager (ms) |    Per token latency SDPA (ms) |    Speedup (%) |    Mem eager (MB) |    Mem BT (MB) |    Mem saved (%) |
-|--------------:|-----------:|--------------------------------:|-------------------------------:|---------------:|------------------:|---------------:|-----------------:|
-|             1 |        128 |                          11.634 |                          8.647 |         34.546 |           717.676 |        717.674 |            0     |
-|             1 |        256 |                          11.593 |                          8.86  |         30.851 |           742.852 |        742.845 |            0.001 |
-|             1 |        512 |                          11.515 |                          8.816 |         30.614 |           798.232 |        799.593 |           -0.17  |
-|             1 |       1024 |                          11.556 |                          8.915 |         29.628 |           917.265 |        895.538 |            2.426 |
-|             2 |        128 |                          12.724 |                         11.002 |         15.659 |           762.434 |        762.431 |            0     |
-|             2 |        256 |                          12.704 |                         11.063 |         14.83  |           816.809 |        816.733 |            0.009 |
-|             2 |        512 |                          12.757 |                         10.947 |         16.535 |           917.383 |        918.339 |           -0.104 |
-|             2 |       1024 |                          13.018 |                         11.018 |         18.147 |          1162.65  |       1114.81  |            4.291 |
-|             4 |        128 |                          12.739 |                         10.959 |         16.243 |           856.335 |        856.483 |           -0.017 |
-|             4 |        256 |                          12.718 |                         10.837 |         17.355 |           957.298 |        957.674 |           -0.039 |
-|             4 |        512 |                          12.813 |                         10.822 |         18.393 |          1158.44  |       1158.45  |           -0.001 |
-|             4 |       1024 |                          13.416 |                         11.06  |         21.301 |          1653.42  |       1557.19  |            6.18  |
-|             8 |        128 |                          12.763 |                         10.891 |         17.193 |          1036.13  |       1036.51  |           -0.036 |
-|             8 |        256 |                          12.89  |                         11.104 |         16.085 |          1236.98  |       1236.87  |            0.01  |
-|             8 |        512 |                          13.327 |                         10.939 |         21.836 |          1642.29  |       1641.78  |            0.031 |
-|             8 |       1024 |                          15.181 |                         11.175 |         35.848 |          2634.98  |       2443.35  |            7.843 |
+- Refer to this [notebook](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing) for an example of fine-tuning OPT with PEFT, bitsandbytes, and Transformers.
+- The [How 🤗 Accelerate runs very large models thanks to PyTorch](https://huggingface.co/blog/accelerate-large-models) blog post demonstrates how to run OPT for inference.
 
 ## OPTConfig
 

docs/source/en/model_doc/owlv2.md

@@ -106,6 +106,13 @@ Usage of OWLv2 is identical to [OWL-ViT](owlvit) with a new, updated image proce
     - post_process_object_detection
     - post_process_image_guided_detection
 
+## Owlv2ImageProcessorFast
+
+[[autodoc]] Owlv2ImageProcessorFast
+    - preprocess
+    - post_process_object_detection
+    - post_process_image_guided_detection
+
 ## Owlv2Processor
 
 [[autodoc]] Owlv2Processor

docs/source/en/model_doc/perception_lm.md

@@ -0,0 +1,68 @@
+<!--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.
+
+-->
+
+# PerceptionLM
+
+## Overview
+
+The PerceptionLM model was proposed in [PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding](https://ai.meta.com/research/publications/perceptionlm-open-access-data-and-models-for-detailed-visual-understanding/) by Jang Hyun Cho et al. It's a fully open, reproducible model for transparent research in image and video understanding. PLM consists of
+a vision encoder with a small scale (<8B parameters) LLM decoder.
+
+The abstract from the paper is the following:
+
+*Vision-language models are integral to computer vision research, yet many high-performing models
+remain closed-source, obscuring their data, design and training recipe. The research community
+has responded by using distillation from black-box models to label training data, achieving strong
+benchmark results, at the cost of measurable scientific progress. However, without knowing the details
+of the teacher model and its data sources, scientific progress remains difficult to measure. In this
+paper, we study building a Perception Language Model (PLM) in a fully open and reproducible
+framework for transparent research in image and video understanding. We analyze standard training
+pipelines without distillation from proprietary models and explore large-scale synthetic data to identify
+critical data gaps, particularly in detailed video understanding. To bridge these gaps, we release 2.8M
+human-labeled instances of fine-grained video question-answer pairs and spatio-temporally grounded
+video captions. Additionally, we introduce PLM–VideoBench, a suite for evaluating challenging video
+understanding tasks focusing on the ability to reason about “what”, “where”, “when”, and “how” of a
+video. We make our work fully reproducible by providing data, training recipes, code & models.*
+
+
+This model was contributed by [shumingh](https://huggingface.co/shumingh).
+The original code can be found [here](https://github.com/facebookresearch/perception_models).
+
+
+## PerceptionLMConfig
+
+[[autodoc]] PerceptionLMConfig
+
+## PerceptionLMProcessor
+
+[[autodoc]] PerceptionLMProcessor
+
+## PerceptionLMImageProcessorFast
+
+[[autodoc]] PerceptionLMImageProcessorFast
+
+## PerceptionLMVideoProcessor
+
+[[autodoc]] PerceptionLMVideoProcessor
+
+## PerceptionLMModel
+
+[[autodoc]] PerceptionLMModel
+
+## PerceptionLMForConditionalGeneration
+
+[[autodoc]] PerceptionLMForConditionalGeneration
+    - forward

docs/source/en/model_doc/phi4_multimodal.md

@@ -9,44 +9,53 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 -->
 
-# Phi4 Multimodal
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-EE4C2C?logo=pytorch&logoColor=white&style=flat">
+  </div>
+</div>
 
-## Overview
+## Phi4 Multimodal
 
-Phi4 Multimodal is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following:
+[Phi4 Multimodal](https://huggingface.co/papers/2503.01743) is a multimodal model capable of text, image, and speech and audio inputs or any combination of these. It features a mixture of LoRA adapters for handling different inputs, and each input is routed to the appropriate encoder.
 
-- Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian
-- Vision: English
-- Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese
+You can find all the original Phi4 Multimodal checkpoints under the [Phi4](https://huggingface.co/collections/microsoft/phi-4-677e9380e514feb5577a40e4) collection.
 
-This model was contributed by [Cyril Vallez](https://huggingface.co/cyrilvallez). The most recent code can be
-found [here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py).
+> [!TIP]
+> This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez).
+>
+> Click on the Phi-4 Multimodal in the right sidebar for more examples of how to apply Phi-4 Multimodal to different tasks.
 
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
 
-## Usage tips
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-`Phi4-multimodal-instruct` can be found on the [Huggingface Hub](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)
+```python
+from transformers import pipeline
+generator = pipeline("text-generation", model="microsoft/Phi-4-multimodal-instruct", torch_dtype="auto", device=0)
 
-In the following, we demonstrate how to use it for inference depending on the input modalities (text, image, audio).
+prompt = "Explain the concept of multimodal AI in simple terms."
+
+result = generator(prompt, max_length=50)
+print(result[0]['generated_text'])
+```
+
+</hfoption>
+<hfoption id="AutoModel">
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
 
-
-# Define model path
 model_path = "microsoft/Phi-4-multimodal-instruct"
 device = "cuda:0"
 
-# Load model and processor
 processor = AutoProcessor.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device,  torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device, torch_dtype=torch.float16)
 
-# Optional: load the adapters (note that without them, the base model will very likely not work well)
-model.load_adapter(model_path, adapter_name="speech", device_map=device, adapter_kwargs={"subfolder": 'speech-lora'})
 model.load_adapter(model_path, adapter_name="vision", device_map=device, adapter_kwargs={"subfolder": 'vision-lora'})
 
-# Part : Image Processing
 messages = [
     {
         "role": "user",
@@ -57,7 +66,7 @@ messages = [
     },
 ]
 
-model.set_adapter("vision") # if loaded, activate the vision adapter
+model.set_adapter("vision")
 inputs = processor.apply_chat_template(
     messages,
     add_generation_prompt=True,
@@ -66,7 +75,6 @@ inputs = processor.apply_chat_template(
     return_tensors="pt",
 ).to(device)
 
-# Generate response
 generate_ids = model.generate(
     **inputs,
     max_new_tokens=1000,
@@ -77,10 +85,27 @@ response = processor.batch_decode(
     generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )[0]
 print(f'>>> Response\n{response}')
+```
 
+</hfoption>
+</hfoptions>
 
-# Part 2: Audio Processing
-model.set_adapter("speech") # if loaded, activate the speech adapter
+## Notes
+
+The example below demonstrates inference with an audio and text input.
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+
+model_path = "microsoft/Phi-4-multimodal-instruct"
+device = "cuda:0"
+
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device,  torch_dtype=torch.float16)
+
+model.load_adapter(model_path, adapter_name="speech", device_map=device, adapter_kwargs={"subfolder": 'speech-lora'})
+model.set_adapter("speech")
 audio_url = "https://upload.wikimedia.org/wikipedia/commons/b/b0/Barbara_Sahakian_BBC_Radio4_The_Life_Scientific_29_May_2012_b01j5j24.flac"
 messages = [
     {
@@ -110,6 +135,7 @@ response = processor.batch_decode(
     generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )[0]
 print(f'>>> Response\n{response}')
+
 ```
 
 ## Phi4MultimodalFeatureExtractor

docs/source/en/model_doc/pixtral.md

@@ -86,6 +86,10 @@ output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up
 
 [[autodoc]] PixtralVisionConfig
 
+## MistralCommonTokenizer
+
+[[autodoc]] MistralCommonTokenizer
+
 ## PixtralVisionModel
 
 [[autodoc]] PixtralVisionModel

docs/source/en/model_doc/sam.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 SAM (Segment Anything Model) was proposed in [Segment Anything](https://huggingface.co/papers/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
 
-The model can be used to predict segmentation masks of any object of interest given an input image. 
+The model can be used to predict segmentation masks of any object of interest given an input image.
 
 ![example image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-output.png)
 
@@ -37,9 +37,9 @@ Tips:
 
 - The model predicts binary masks that states the presence or not of the object of interest given an image.
 - The model predicts much better results if input 2D points and/or input bounding boxes are provided
-- You can prompt multiple points for the same image, and predict a single mask. 
+- You can prompt multiple points for the same image, and predict a single mask.
 - Fine-tuning the model is not supported yet
-- According to the paper, textual input should be also supported. However, at this time of writing this seems not to be supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844). 
+- According to the paper, textual input should be also supported. However, at this time of writing this seems not to be supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).
 
 
 This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
@@ -149,6 +149,11 @@ alt="drawing" width="900"/>
 [[autodoc]] SamImageProcessor
 
 
+## SamImageProcessorFast
+
+[[autodoc]] SamImageProcessorFast
+
+
 ## SamVisionModel
 
 [[autodoc]] SamVisionModel

docs/source/en/model_doc/speech_to_text_2.md

@@ -61,19 +61,16 @@ predicted token ids.
 - Step-by-step Speech Translation
 
 ```python
->>> import torch
 >>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
 >>> from datasets import load_dataset
->>> import soundfile as sf
 
 >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
 >>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
 
 
->>> def map_to_array(batch):
-...     speech, _ = sf.read(batch["file"])
-...     batch["speech"] = speech
-...     return batch
+>>> def map_to_array(example):
+...     example["speech"] = example["audio"]["array"]
+...     return example
 
 
 >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

docs/source/en/model_doc/superglue.md

@@ -10,40 +10,31 @@ 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" >
+    </div>
+</div>
+
 # SuperGlue
 
-<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>
+[SuperGlue](https://huggingface.co/papers/1911.11763) is a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. SuperGlue introduces a flexible context aggregation mechanism based on attention, enabling it to reason about the underlying 3D scene and feature assignments jointly. Paired with the [SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc.
 
-## Overview
+You can find all the original SuperGlue checkpoints under the [Magic Leap Community](https://huggingface.co/magic-leap-community) organization.
 
-The SuperGlue model was proposed in [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://huggingface.co/papers/1911.11763) by Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich.
+> [!TIP]
+> This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
+>
+> Click on the SuperGlue models in the right sidebar for more examples of how to apply SuperGlue to different computer vision tasks.
 
-This model consists of matching two sets of interest points detected in an image. Paired with the 
-[SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and 
-estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc.
+The example below demonstrates how to match keypoints between two images with the [`AutoModel`] class.
 
-The abstract from the paper is the following:
+<hfoptions id="usage">
+<hfoption id="AutoModel">
 
-*This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences 
-and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs 
-are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling 
-SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, 
-our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image 
-pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in 
-challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and 
-can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at this [URL](https://github.com/magicleap/SuperGluePretrainedNetwork).*
-
-## How to use
-
-Here is a quick example of using the model. Since this model is an image matching model, it requires pairs of images to be matched. 
-The raw outputs contain the list of keypoints detected by the keypoint detector as well as the list of matches with their corresponding 
-matching scores.
-```python
+```py
 from transformers import AutoImageProcessor, AutoModel
 import torch
 from PIL import Image
@@ -52,7 +43,7 @@ import requests
 url_image1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
 image1 = Image.open(requests.get(url_image1, stream=True).raw)
 url_image2 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"
-image_2 = Image.open(requests.get(url_image2, stream=True).raw)
+image2 = Image.open(requests.get(url_image2, stream=True).raw)
 
 images = [image1, image2]
 
@@ -62,67 +53,97 @@ model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")
 inputs = processor(images, return_tensors="pt")
 with torch.no_grad():
     outputs = model(**inputs)
-```
 
-You can use the `post_process_keypoint_matching` method from the `SuperGlueImageProcessor` to get the keypoints and matches in a more readable format:
-
-```python
+# Post-process to get keypoints and matches
 image_sizes = [[(image.height, image.width) for image in images]]
-outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
-for i, output in enumerate(outputs):
-    print("For the image pair", i)
-    for keypoint0, keypoint1, matching_score in zip(
-            output["keypoints0"], output["keypoints1"], output["matching_scores"]
-    ):
-        print(
-            f"Keypoint at coordinate {keypoint0.numpy()} in the first image matches with keypoint at coordinate {keypoint1.numpy()} in the second image with a score of {matching_score}."
+processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
+```
+
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- SuperGlue performs feature matching between two images simultaneously, requiring pairs of images as input.
+
+    ```python
+    from transformers import AutoImageProcessor, AutoModel
+    import torch
+    from PIL import Image
+    import requests
+    
+    processor = AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor")
+    model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")
+    
+    # SuperGlue requires pairs of images
+    images = [image1, image2]
+    inputs = processor(images, return_tensors="pt")
+    outputs = model(**inputs)
+    
+    # Extract matching information
+    keypoints0 = outputs.keypoints0  # Keypoints in first image
+    keypoints1 = outputs.keypoints1  # Keypoints in second image
+    matches = outputs.matches        # Matching indices
+    matching_scores = outputs.matching_scores  # Confidence scores
+    ```
+
+- The model outputs matching indices, keypoints, and confidence scores for each match.
+- For better visualization and analysis, use the [`SuperGlueImageProcessor.post_process_keypoint_matching`] method to get matches in a more readable format.
+
+    ```py
+    # Process outputs for visualization
+    image_sizes = [[(image.height, image.width) for image in images]]
+    processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
+    
+    for i, output in enumerate(processed_outputs):
+        print(f"For the image pair {i}")
+        for keypoint0, keypoint1, matching_score in zip(
+                output["keypoints0"], output["keypoints1"], output["matching_scores"]
+        ):
+            print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
+    ```
+
+- The example below demonstrates how to visualize matches between two images.
+
+    ```py
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    # Create side by side image
+    merged_image = np.zeros((max(image1.height, image2.height), image1.width + image2.width, 3))
+    merged_image[: image1.height, : image1.width] = np.array(image1) / 255.0
+    merged_image[: image2.height, image1.width :] = np.array(image2) / 255.0
+    plt.imshow(merged_image)
+    plt.axis("off")
+
+    # Retrieve the keypoints and matches
+    output = processed_outputs[0]
+    keypoints0 = output["keypoints0"]
+    keypoints1 = output["keypoints1"]
+    matching_scores = output["matching_scores"]
+
+    # Plot the matches
+    for keypoint0, keypoint1, matching_score in zip(keypoints0, keypoints1, matching_scores):
+        plt.plot(
+            [keypoint0[0], keypoint1[0] + image1.width],
+            [keypoint0[1], keypoint1[1]],
+            color=plt.get_cmap("RdYlGn")(matching_score.item()),
+            alpha=0.9,
+            linewidth=0.5,
         )
+        plt.scatter(keypoint0[0], keypoint0[1], c="black", s=2)
+        plt.scatter(keypoint1[0] + image1.width, keypoint1[1], c="black", s=2)
 
-```
+    plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
+    ```
 
-From the outputs, you can visualize the matches between the two images using the following code:
-```python
-import matplotlib.pyplot as plt
-import numpy as np
+<div class="flex justify-center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png">
+</div>
 
-# Create side by side image
-merged_image = np.zeros((max(image1.height, image2.height), image1.width + image2.width, 3))
-merged_image[: image1.height, : image1.width] = np.array(image1) / 255.0
-merged_image[: image2.height, image1.width :] = np.array(image2) / 255.0
-plt.imshow(merged_image)
-plt.axis("off")
+## Resources
 
-# Retrieve the keypoints and matches
-output = outputs[0]
-keypoints0 = output["keypoints0"]
-keypoints1 = output["keypoints1"]
-matching_scores = output["matching_scores"]
-keypoints0_x, keypoints0_y = keypoints0[:, 0].numpy(), keypoints0[:, 1].numpy()
-keypoints1_x, keypoints1_y = keypoints1[:, 0].numpy(), keypoints1[:, 1].numpy()
-
-# Plot the matches
-for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
-        keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, matching_scores
-):
-    plt.plot(
-        [keypoint0_x, keypoint1_x + image1.width],
-        [keypoint0_y, keypoint1_y],
-        color=plt.get_cmap("RdYlGn")(matching_score.item()),
-        alpha=0.9,
-        linewidth=0.5,
-    )
-    plt.scatter(keypoint0_x, keypoint0_y, c="black", s=2)
-    plt.scatter(keypoint1_x + image1.width, keypoint1_y, c="black", s=2)
-
-# Save the plot
-plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
-plt.close()
-```
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png)
-
-This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
-The original code can be found [here](https://github.com/magicleap/SuperGluePretrainedNetwork).
+- Refer to the [original SuperGlue repository](https://github.com/magicleap/SuperGluePretrainedNetwork) for more examples and implementation details.
 
 ## SuperGlueConfig
 
@@ -133,10 +154,15 @@ The original code can be found [here](https://github.com/magicleap/SuperGluePret
 [[autodoc]] SuperGlueImageProcessor
 
 - preprocess
+- post_process_keypoint_matching
 
+<frameworkcontent>
+<pt>
 ## SuperGlueForKeypointMatching
 
 [[autodoc]] SuperGlueForKeypointMatching
 
 - forward
-- post_process_keypoint_matching
+
+</pt>
+</frameworkcontent>

docs/source/en/model_doc/switch_transformers.md

@@ -14,35 +14,90 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# SwitchTransformers
-
-<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 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>
 
-## Overview
+# Switch Transformers
 
-The SwitchTransformers model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://huggingface.co/papers/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
+[Switch Transformers](https://huggingface.co/papers/2101.03961) is a sparse T5 model where the MLP layer is replaced by a Mixture-of-Experts (MoE). A routing mechanism associates each token with an expert and each expert is a dense MLP. Sparsity enables better scaling and the routing mechanism allows the model to select relevant weights on the fly which increases model capacity.
 
-The Switch Transformer model uses a sparse T5 encoder-decoder architecture, where the MLP are replaced by a Mixture of Experts (MoE). A routing mechanism (top 1 in this case) associates each token to one of the expert, where each expert is a dense MLP. While switch transformers have a lot more weights than their equivalent dense models, the sparsity allows better scaling and better finetuning performance at scale.
-During a forward pass, only a fraction of the weights are used. The routing mechanism allows the model to select relevant weights on the fly which increases the model capacity without increasing the number of operations.
+You can find all the original Switch Transformers checkpoints under the [Switch Transformer](https://huggingface.co/collections/google/switch-transformers-release-6548c35c6507968374b56d1f) collection.
 
-The abstract from the paper is the following:
 
-*In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.*
+> [!TIP]
+> This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
+>
+> Click on the Switch Transformers models in the right sidebar for more examples of how to apply Switch Transformers to different natural language tasks.
 
-This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ).
-The original code can be found [here](https://github.com/google/flaxformer/tree/main/flaxformer/architectures/moe).
+The example below demonstrates how to predict the masked token with [`Pipeline`], [`AutoModel`], and from the command line.
 
-## Usage tips
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-- SwitchTransformers uses the [`T5Tokenizer`], which can be loaded directly from each model's repository.
-- The released weights are pretrained on English [Masked Language Modeling](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19323/en/glossary#general-terms) task, and should be finetuned.
+```python
+import torch
+from transformers import pipeline
 
-## Resources
+pipeline = pipeline(
+    task="text2text-generation", 
+    model="google/switch-base-8",
+    torch_dtype=torch.float16,
+    device=0
+)
+print(pipeline("The capital of France is <extra_id_0>."))
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/switch-base-8", device_map="auto", torch_dtype=torch.float16)
+
+input_text = "The capital of France is <extra_id_0>."
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
+
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "The capital of France is <extra_id_0>." | transformers run --task text2text-generation --model google/switch-base-8 --device 0
+# [{'generated_text': 'Paris.'}]
+```
+
+</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 8-bits.
+
+```py
+# pip install bitsandbytes
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig
+
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForSeq2SeqLM.from_pretrained("google/switch-base-8", device_map="auto", quantization_config=quantization_config)
+
+input_text = "The capital of France is <extra_id_0>."
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
+
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-- [Translation task guide](../tasks/translation)
-- [Summarization task guide](../tasks/summarization)
 
 ## SwitchTransformersConfig
 

docs/source/en/model_doc/t5gemma.md

@@ -14,16 +14,25 @@ specific language governing permissions and limitations under the License.
 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="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>
 
 # T5Gemma
 
-T5Gemma (aka encoder-decoder Gemma) was proposed in a [research paper](https://arxiv.org/abs/2504.06225) by Google. It is a family of encoder-decoder large langauge models, developed by adapting pretrained decoder-only models into encoder-decoder. T5Gemma includes pretrained and instruction-tuned variants. The architecture is based on transformer encoder-decoder design following T5, with improvements from Gemma 2: GQA, RoPE, GeGLU activation, RMSNorm, and interleaved local/global attention.
+T5Gemma (aka encoder-decoder Gemma) was proposed in a [research paper](https://arxiv.org/abs/2504.06225) by Google. It is a family of encoder-decoder large language models, developed by adapting pretrained decoder-only models into encoder-decoder. T5Gemma includes pretrained and instruction-tuned variants. The architecture is based on transformer encoder-decoder design following T5, with improvements from Gemma 2: GQA, RoPE, GeGLU activation, RMSNorm, and interleaved local/global attention.
 
 T5Gemma has two groups of model sizes: 1) [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2) sizes (2B-2B, 9B-2B, and 9B-9B), which are based on the offical Gemma 2 models (2B and 9B); and 2) [T5](https://arxiv.org/abs/1910.10683) sizes (Small, Base, Large, and XL), where are pretrained under the Gemma 2 framework following T5 configuration. In addition, we also provide a model at ML size (medium large, ~2B in total), which is in-between T5 Large and T5 XL.
 
 The pretrained varaints are trained with two objectives: prefix language modeling with knowledge distillation (PrefixLM) and UL2, separately. We release both variants for each model size. The instruction-turned varaints was post-trained with supervised fine-tuning and reinforcement learning.
 
+> [!TIP]
+> Click on the T5Gemma models in the right sidebar for more examples of how to apply T5Gemma to different language tasks.
+
 The example below demonstrates how to chat with the model with [`Pipeline`] or the [`AutoModel`] class, and from the command line.
 
 <hfoptions id="usage">
@@ -35,43 +44,52 @@ import torch
 from transformers import pipeline
 
 pipe = pipeline(
-    task="text2text-generation",
-    model="google/t5gemma-placeholder",
+    "text2text-generation",
+    model="google/t5gemma-2b-2b-prefixlm-it",
     torch_dtype=torch.bfloat16,
-    device="cuda",
+    device="cuda",  # replace with "mps" to run on a Mac device
 )
 
-pipe("Question: Why is the sky blue?\nAnswer:", max_new_tokens=50)
+messages = [
+    {"role": "user", "content": "Tell me an unknown interesting biology fact about the brain."},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+pipe(prompt, max_new_tokens=32)
 ```
 
 </hfoption>
 <hfoption id="AutoModel">
 
 ```python
-import torch
+# pip install accelerate
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
-tokenizer = AutoTokenizer.from_pretrained("google/t5gemma-placeholder")
+tokenizer = AutoTokenizer.from_pretrained("google/t5gemma-2b-2b-prefixlm-it")
 model = AutoModelForSeq2SeqLM.from_pretrained(
-    "google/t5gemma-placeholder",
+    "google/t5gemma-2b-2b-prefixlm-it",
+    device_map="auto",
     torch_dtype=torch.bfloat16,
-    device_map="auto"
 )
 
-input_text = "Question: Why is the sky blue?\nAnswer:"
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+messages = [
+    {"role": "user", "content": "Tell me an unknown interesting biology fact about the brain."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True).to("cuda")
 
 outputs = model.generate(**input_ids, max_new_tokens=32)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
+print(tokenizer.decode(outputs[0]))
 ```
 
 </hfoption>
 <hfoption id="transformers CLI">
 
 ```
-echo -e "Question: Why is the sky blue? Answer:" | transformers run --task text2text-generation --model google/t5gemma-placeholder --device 0
+echo -e "Write me a poem about Machine Learning. Answer:" | transformers run --task text2text-generation --model google/t5gemma-2b-2b-prefixlm --device 0
 ```
+</hfoption>
+</hfoptions>
 
 ## T5GemmaConfig
 

docs/source/en/model_doc/timesfm.md

@@ -37,6 +37,7 @@ The original code can be found [here](https://github.com/google-research/timesfm
 To use the model:
 
 ```python
+import numpy as np
 import torch
 from transformers import TimesFmModelForPrediction
 

docs/source/en/model_doc/vitpose.md

@@ -10,52 +10,39 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# ViTPose
-
-<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 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>
 
-## Overview
+# ViTPose
 
-The ViTPose model was proposed in [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://huggingface.co/papers/2204.12484) by Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao. ViTPose employs a standard, non-hierarchical [Vision Transformer](vit) as backbone for the task of keypoint estimation. A simple decoder head is added on top to predict the heatmaps from a given image. Despite its simplicity, the model gets state-of-the-art results on the challenging MS COCO Keypoint Detection benchmark. The model was further improved in [ViTPose++: Vision Transformer for Generic Body Pose Estimation](https://huggingface.co/papers/2212.04246) where the authors employ
-a mixture-of-experts (MoE) module in the ViT backbone along with pre-training on more data, which further enhances the performance.
+[ViTPose](https://huggingface.co/papers/2204.12484) is a vision transformer-based model for keypoint (pose) estimation. It uses a simple, non-hierarchical [ViT](./vit) backbone and a lightweight decoder head. This architecture simplifies model design, takes advantage of transformer scalability, and can be adapted to different training strategies.
 
-The abstract from the paper is the following:
-
-*Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose. Specifically, ViTPose employs plain and non-hierarchical vision transformers as backbones to extract features for a given person instance and a lightweight decoder for pose estimation. It can be scaled up from 100M to 1B parameters by taking the advantages of the scalable model capacity and high parallelism of transformers, setting a new Pareto front between throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, pre-training and finetuning strategy, as well as dealing with multiple pose tasks. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our basic ViTPose model outperforms representative methods on the challenging MS COCO Keypoint Detection benchmark, while the largest model sets a new state-of-the-art.*
+[ViTPose++](https://huggingface.co/papers/2212.04246) improves on ViTPose by incorporating a mixture-of-experts (MoE) module in the backbone and using more diverse pretraining data.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitpose-architecture.png"
 alt="drawing" width="600"/>
 
-<small> ViTPose architecture. Taken from the <a href="https://huggingface.co/papers/2204.12484">original paper.</a> </small>
+You can find all ViTPose and ViTPose++ checkpoints under the [ViTPose collection](https://huggingface.co/collections/usyd-community/vitpose-677fcfd0a0b2b5c8f79c4335).
 
-This model was contributed by [nielsr](https://huggingface.co/nielsr) and [sangbumchoi](https://github.com/SangbumChoi).
-The original code can be found [here](https://github.com/ViTAE-Transformer/ViTPose).
-
-## Usage Tips
-
-ViTPose is a so-called top-down keypoint detection model. This means that one first uses an object detector, like [RT-DETR](rt_detr.md), to detect people (or other instances) in an image. Next, ViTPose takes the cropped images as input and predicts the keypoints for each of them.
+The example below demonstrates pose estimation with the [`VitPoseForPoseEstimation`] class.
 
 ```py
 import torch
 import requests
 import numpy as np
-
+import supervision as sv
 from PIL import Image
-
 from transformers import AutoProcessor, RTDetrForObjectDetection, VitPoseForPoseEstimation
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-url = "http://images.cocodataset.org/val2017/000000000139.jpg"
+url = "https://www.fcbarcelona.com/fcbarcelona/photo/2021/01/31/3c55a19f-dfc1-4451-885e-afd14e890a11/mini_2021-01-31-BARCELONA-ATHLETIC-BILBAOI-30.JPG"
 image = Image.open(requests.get(url, stream=True).raw)
 
-# ------------------------------------------------------------------------
-# Stage 1. Detect humans on the image
-# ------------------------------------------------------------------------
-
-# You can choose any detector of your choice
+# Detect humans in the image
 person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
 person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map=device)
 
@@ -67,7 +54,7 @@ with torch.no_grad():
 results = person_image_processor.post_process_object_detection(
     outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
 )
-result = results[0]  # take first image results
+result = results[0]
 
 # Human label refers 0 index in COCO dataset
 person_boxes = result["boxes"][result["labels"] == 0]
@@ -77,10 +64,7 @@ person_boxes = person_boxes.cpu().numpy()
 person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
 person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
 
-# ------------------------------------------------------------------------
-# Stage 2. Detect keypoints for each person found
-# ------------------------------------------------------------------------
-
+# Detect keypoints for each person found
 image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-base-simple")
 model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map=device)
 
@@ -90,54 +74,7 @@ with torch.no_grad():
     outputs = model(**inputs)
 
 pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes])
-image_pose_result = pose_results[0]  # results for first image
-```
-
-### ViTPose++ models
-
-The best [checkpoints](https://huggingface.co/collections/usyd-community/vitpose-677fcfd0a0b2b5c8f79c4335) are those of the [ViTPose++ paper](https://huggingface.co/papers/2212.04246). ViTPose++ models employ a so-called [Mixture-of-Experts (MoE)](https://huggingface.co/blog/moe) architecture for the ViT backbone, resulting in better performance.
-
-The ViTPose+ checkpoints use 6 experts, hence 6 different dataset indices can be passed. 
-An overview of the various dataset indices is provided below:
-
-- 0: [COCO validation 2017](https://cocodataset.org/#overview) dataset, using an object detector that gets 56 AP on the "person" class
-- 1: [AiC](https://github.com/fabbrimatteo/AiC-Dataset) dataset
-- 2: [MPII](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/software-and-datasets/mpii-human-pose-dataset) dataset
-- 3: [AP-10K](https://github.com/AlexTheBad/AP-10K) dataset
-- 4: [APT-36K](https://github.com/pandorgan/APT-36K) dataset
-- 5: [COCO-WholeBody](https://github.com/jin-s13/COCO-WholeBody) dataset
-
-Pass the `dataset_index` argument in the forward of the model to indicate which experts to use for each example in the batch. Example usage is shown below:
-
-```python
-image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-base")
-model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-base", device=device)
-
-inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)
-
-dataset_index = torch.tensor([0], device=device) # must be a tensor of shape (batch_size,)
-
-with torch.no_grad():
-    outputs = model(**inputs, dataset_index=dataset_index)
-```
-
-The ViTPose+ checkpoints use 6 experts, hence 6 different dataset indices can be passed. 
-An overview of the various dataset indices is provided below:
-
-- 0: [COCO validation 2017](https://cocodataset.org/#overview) dataset, using an object detector that gets 56 AP on the "person" class
-- 1: [AiC](https://github.com/fabbrimatteo/AiC-Dataset) dataset
-- 2: [MPII](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/software-and-datasets/mpii-human-pose-dataset) dataset
-- 3: [AP-10K](https://github.com/AlexTheBad/AP-10K) dataset
-- 4: [APT-36K](https://github.com/pandorgan/APT-36K) dataset
-- 5: [COCO-WholeBody](https://github.com/jin-s13/COCO-WholeBody) dataset
-
-
-### Visualization
-
-To visualize the various keypoints, one can either leverage the `supervision` [library](https://github.com/roboflow/supervision (requires `pip install supervision`):
-
-```python
-import supervision as sv
+image_pose_result = pose_results[0]
 
 xy = torch.stack([pose_result['keypoints'] for pose_result in image_pose_result]).cpu().numpy()
 scores = torch.stack([pose_result['scores'] for pose_result in image_pose_result]).cpu().numpy()
@@ -162,119 +99,192 @@ annotated_frame = vertex_annotator.annotate(
     scene=annotated_frame,
     key_points=key_points
 )
+annotated_frame
 ```
 
-Alternatively, one can also visualize the keypoints using [OpenCV](https://opencv.org/) (requires `pip install opencv-python`):
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitpose.png"/>
+</div>
 
-```python
-import math
-import cv2
+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.
 
-def draw_points(image, keypoints, scores, pose_keypoint_color, keypoint_score_threshold, radius, show_keypoint_weight):
-    if pose_keypoint_color is not None:
-        assert len(pose_keypoint_color) == len(keypoints)
-    for kid, (kpt, kpt_score) in enumerate(zip(keypoints, scores)):
-        x_coord, y_coord = int(kpt[0]), int(kpt[1])
-        if kpt_score > keypoint_score_threshold:
-            color = tuple(int(c) for c in pose_keypoint_color[kid])
-            if show_keypoint_weight:
-                cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
-                transparency = max(0, min(1, kpt_score))
-                cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
-            else:
-                cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
+The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
 
-def draw_links(image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold, thickness, show_keypoint_weight, stick_width = 2):
-    height, width, _ = image.shape
-    if keypoint_edges is not None and link_colors is not None:
-        assert len(link_colors) == len(keypoint_edges)
-        for sk_id, sk in enumerate(keypoint_edges):
-            x1, y1, score1 = (int(keypoints[sk[0], 0]), int(keypoints[sk[0], 1]), scores[sk[0]])
-            x2, y2, score2 = (int(keypoints[sk[1], 0]), int(keypoints[sk[1], 1]), scores[sk[1]])
-            if (
-                x1 > 0
-                and x1 < width
-                and y1 > 0
-                and y1 < height
-                and x2 > 0
-                and x2 < width
-                and y2 > 0
-                and y2 < height
-                and score1 > keypoint_score_threshold
-                and score2 > keypoint_score_threshold
-            ):
-                color = tuple(int(c) for c in link_colors[sk_id])
+```py
+# pip install torchao
+import torch
+import requests
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, RTDetrForObjectDetection, VitPoseForPoseEstimation, TorchAoConfig
+
+url = "https://www.fcbarcelona.com/fcbarcelona/photo/2021/01/31/3c55a19f-dfc1-4451-885e-afd14e890a11/mini_2021-01-31-BARCELONA-ATHLETIC-BILBAOI-30.JPG"
+image = Image.open(requests.get(url, stream=True).raw)
+
+person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map=device)
+
+inputs = person_image_processor(images=image, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = person_model(**inputs)
+
+results = person_image_processor.post_process_object_detection(
+    outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
+)
+result = results[0]
+
+person_boxes = result["boxes"][result["labels"] == 0]
+person_boxes = person_boxes.cpu().numpy()
+
+person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
+person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
+
+quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
+
+image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-huge")
+model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-huge", device_map=device, quantization_config=quantization_config)
+
+inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes])
+image_pose_result = pose_results[0]
+```
+
+## Notes
+
+- Use [`AutoProcessor`] to automatically prepare bounding box and image inputs.
+- ViTPose is a top-down pose estimator. It uses a object detector to detect individuals first before keypoint prediction.
+- ViTPose++ has 6 different MoE expert heads (COCO validation `0`, AiC `1`, MPII `2`, AP-10K `3`, APT-36K `4`, COCO-WholeBody `5`) which supports 6 different datasets. Pass a specific value corresponding to the dataset to the `dataset_index` to indicate which expert to use.
+
+    ```py
+    from transformers import AutoProcessor, VitPoseForPoseEstimation
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-base")
+    model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-base", device=device)
+
+    inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(device)
+    dataset_index = torch.tensor([0], device=device) # must be a tensor of shape (batch_size,)
+
+    with torch.no_grad():
+        outputs = model(**inputs, dataset_index=dataset_index)
+    ```
+
+- [OpenCV](https://opencv.org/) is an alternative option for visualizing the estimated pose.
+
+    ```py
+    # pip install opencv-python
+    import math
+    import cv2
+
+    def draw_points(image, keypoints, scores, pose_keypoint_color, keypoint_score_threshold, radius, show_keypoint_weight):
+        if pose_keypoint_color is not None:
+            assert len(pose_keypoint_color) == len(keypoints)
+        for kid, (kpt, kpt_score) in enumerate(zip(keypoints, scores)):
+            x_coord, y_coord = int(kpt[0]), int(kpt[1])
+            if kpt_score > keypoint_score_threshold:
+                color = tuple(int(c) for c in pose_keypoint_color[kid])
                 if show_keypoint_weight:
-                    X = (x1, x2)
-                    Y = (y1, y2)
-                    mean_x = np.mean(X)
-                    mean_y = np.mean(Y)
-                    length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
-                    angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
-                    polygon = cv2.ellipse2Poly(
-                        (int(mean_x), int(mean_y)), (int(length / 2), int(stick_width)), int(angle), 0, 360, 1
-                    )
-                    cv2.fillConvexPoly(image, polygon, color)
-                    transparency = max(0, min(1, 0.5 * (keypoints[sk[0], 2] + keypoints[sk[1], 2])))
+                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
+                    transparency = max(0, min(1, kpt_score))
                     cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
                 else:
-                    cv2.line(image, (x1, y1), (x2, y2), color, thickness=thickness)
+                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
 
+    def draw_links(image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold, thickness, show_keypoint_weight, stick_width = 2):
+        height, width, _ = image.shape
+        if keypoint_edges is not None and link_colors is not None:
+            assert len(link_colors) == len(keypoint_edges)
+            for sk_id, sk in enumerate(keypoint_edges):
+                x1, y1, score1 = (int(keypoints[sk[0], 0]), int(keypoints[sk[0], 1]), scores[sk[0]])
+                x2, y2, score2 = (int(keypoints[sk[1], 0]), int(keypoints[sk[1], 1]), scores[sk[1]])
+                if (
+                    x1 > 0
+                    and x1 < width
+                    and y1 > 0
+                    and y1 < height
+                    and x2 > 0
+                    and x2 < width
+                    and y2 > 0
+                    and y2 < height
+                    and score1 > keypoint_score_threshold
+                    and score2 > keypoint_score_threshold
+                ):
+                    color = tuple(int(c) for c in link_colors[sk_id])
+                    if show_keypoint_weight:
+                        X = (x1, x2)
+                        Y = (y1, y2)
+                        mean_x = np.mean(X)
+                        mean_y = np.mean(Y)
+                        length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
+                        angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
+                        polygon = cv2.ellipse2Poly(
+                            (int(mean_x), int(mean_y)), (int(length / 2), int(stick_width)), int(angle), 0, 360, 1
+                        )
+                        cv2.fillConvexPoly(image, polygon, color)
+                        transparency = max(0, min(1, 0.5 * (keypoints[sk[0], 2] + keypoints[sk[1], 2])))
+                        cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
+                    else:
+                        cv2.line(image, (x1, y1), (x2, y2), color, thickness=thickness)
 
-# Note: keypoint_edges and color palette are dataset-specific
-keypoint_edges = model.config.edges
+    # Note: keypoint_edges and color palette are dataset-specific
+    keypoint_edges = model.config.edges
 
-palette = np.array(
-    [
-        [255, 128, 0],
-        [255, 153, 51],
-        [255, 178, 102],
-        [230, 230, 0],
-        [255, 153, 255],
-        [153, 204, 255],
-        [255, 102, 255],
-        [255, 51, 255],
-        [102, 178, 255],
-        [51, 153, 255],
-        [255, 153, 153],
-        [255, 102, 102],
-        [255, 51, 51],
-        [153, 255, 153],
-        [102, 255, 102],
-        [51, 255, 51],
-        [0, 255, 0],
-        [0, 0, 255],
-        [255, 0, 0],
-        [255, 255, 255],
-    ]
-)
+    palette = np.array(
+        [
+            [255, 128, 0],
+            [255, 153, 51],
+            [255, 178, 102],
+            [230, 230, 0],
+            [255, 153, 255],
+            [153, 204, 255],
+            [255, 102, 255],
+            [255, 51, 255],
+            [102, 178, 255],
+            [51, 153, 255],
+            [255, 153, 153],
+            [255, 102, 102],
+            [255, 51, 51],
+            [153, 255, 153],
+            [102, 255, 102],
+            [51, 255, 51],
+            [0, 255, 0],
+            [0, 0, 255],
+            [255, 0, 0],
+            [255, 255, 255],
+        ]
+    )
 
-link_colors = palette[[0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16]]
-keypoint_colors = palette[[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]]
+    link_colors = palette[[0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16]]
+    keypoint_colors = palette[[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]]
 
-numpy_image = np.array(image)
+    numpy_image = np.array(image)
 
-for pose_result in image_pose_result:
-    scores = np.array(pose_result["scores"])
-    keypoints = np.array(pose_result["keypoints"])
+    for pose_result in image_pose_result:
+        scores = np.array(pose_result["scores"])
+        keypoints = np.array(pose_result["keypoints"])
 
-    # draw each point on image
-    draw_points(numpy_image, keypoints, scores, keypoint_colors, keypoint_score_threshold=0.3, radius=4, show_keypoint_weight=False)
+        # draw each point on image
+        draw_points(numpy_image, keypoints, scores, keypoint_colors, keypoint_score_threshold=0.3, radius=4, show_keypoint_weight=False)
 
-    # draw links
-    draw_links(numpy_image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold=0.3, thickness=1, show_keypoint_weight=False)
+        # draw links
+        draw_links(numpy_image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold=0.3, thickness=1, show_keypoint_weight=False)
 
-pose_image = Image.fromarray(numpy_image)
-pose_image
-```
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitpose-coco.jpg" alt="drawing" width="600"/>
+    pose_image = Image.fromarray(numpy_image)
+    pose_image
+    ```
 
 ## Resources
 
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViTPose. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+Refer to resources below to learn more about using ViTPose.
 
-- A demo of ViTPose on images and video can be found [here](https://huggingface.co/spaces/hysts/ViTPose-transformers).
-- A notebook illustrating inference and visualization can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/ViTPose/Inference_with_ViTPose_for_human_pose_estimation.ipynb).
+- This [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/ViTPose/Inference_with_ViTPose_for_body_pose_estimation.ipynb) demonstrates inference and visualization.
+- This [Space](https://huggingface.co/spaces/hysts/ViTPose-transformers) demonstrates ViTPose on images and video.
 
 ## VitPoseImageProcessor
 

docs/source/en/model_doc/voxtral.md

@@ -0,0 +1,351 @@
+<!--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.
+
+-->
+
+# Voxtral
+
+Voxtral is an upgrade of [Ministral 3B and Mistral Small 3B](https://mistral.ai/news/ministraux), extending its language capabilities with audio input support. It is designed to handle tasks such as speech transcription, translation, and audio understanding.
+
+You can read more in Mistral's [realease blog post](https://mistral.ai/news/voxtral).
+
+The model is available in two checkpoints:
+- 3B: [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)
+- 24B: [mistralai/Voxtral-Small-24B-2507](https://huggingface.co/mistralai/Voxtral-Small-24B-2507)
+
+## Key Features
+
+Voxtral builds on Ministral-3B by adding audio processing capabilities:
+
+- **Transcription mode**: Includes a dedicated mode for speech transcription. By default, Voxtral detects the spoken language and transcribes it accordingly.  
+- **Long-form context**: With a 32k token context window, Voxtral can process up to 30 minutes of audio for transcription or 40 minutes for broader audio understanding.  
+- **Integrated Q&A and summarization**: Supports querying audio directly and producing structured summaries without relying on separate ASR and language models.  
+- **Multilingual support**: Automatically detects language and performs well across several widely spoken languages, including English, Spanish, French, Portuguese, Hindi, German, Dutch, and Italian.  
+- **Function calling via voice**: Can trigger functions or workflows directly from spoken input based on detected user intent.  
+- **Text capabilities**: Maintains the strong text processing performance of its Ministral-3B foundation.
+
+## Usage
+
+### Audio Instruct Mode
+
+The model supports audio-text instructions, including multi-turn and multi-audio interactions, all processed in batches.
+
+➡️ audio + text instruction
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "url": "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/dude_where_is_my_car.wav",
+            },
+            {"type": "text", "text": "What can you tell me about this audio?"},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(conversation)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated response:")
+print("=" * 80)
+print(decoded_outputs[0])
+print("=" * 80)
+```
+
+➡️ multi-audio + text instruction 
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3",
+            },
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
+            },
+            {"type": "text", "text": "What sport and what nursery rhyme are referenced?"},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(conversation)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated response:")
+print("=" * 80)
+print(decoded_outputs[0])
+print("=" * 80)
+```
+
+➡️ multi-turn:
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
+            },
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
+            },
+            {"type": "text", "text": "Describe briefly what you can hear."},
+        ],
+    },
+    {
+        "role": "assistant",
+        "content": "The audio begins with the speaker delivering a farewell address in Chicago, reflecting on his eight years as president and expressing gratitude to the American people. The audio then transitions to a weather report, stating that it was 35 degrees in Barcelona the previous day, but the temperature would drop to minus 20 degrees the following day.",
+    },
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
+            },
+            {"type": "text", "text": "Ok, now compare this new audio with the previous one."},
+        ],
+    },
+]
+
+inputs = processor.apply_chat_template(conversation)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated response:")
+print("=" * 80)
+print(decoded_outputs[0])
+print("=" * 80)
+```
+
+➡️ text only:
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "What if a cyber brain could possibly generate its own ghost, and create a soul all by itself?",
+            },
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(conversation)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated response:")
+print("=" * 80)
+print(decoded_outputs[0])
+print("=" * 80)
+```
+
+➡️ audio only:
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
+            },
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(conversation)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated response:")
+print("=" * 80)
+print(decoded_outputs[0])
+print("=" * 80)
+```
+
+➡️ batched inference!
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+conversations = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
+                },
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
+                },
+                {
+                    "type": "text",
+                    "text": "Who's speaking in the speach and what city's weather is being discussed?",
+                },
+            ],
+        }
+    ],
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
+                },
+                {"type": "text", "text": "What can you tell me about this audio?"},
+            ],
+        }
+    ],
+]
+
+inputs = processor.apply_chat_template(conversations)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated responses:")
+print("=" * 80)
+for decoded_output in decoded_outputs:
+    print(decoded_output)
+    print("=" * 80)
+```
+
+### Transcription Mode
+
+Use the model to transcribe audio (supports English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian)!
+
+```python
+from transformers import VoxtralForConditionalGeneration, AutoProcessor
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "mistralai/Voxtral-Mini-3B-2507"
+
+processor = AutoProcessor.from_pretrained(repo_id)
+model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
+
+inputs = processor.apply_transcription_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=repo_id)
+inputs = inputs.to(device, dtype=torch.bfloat16)
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("\nGenerated responses:")
+print("=" * 80)
+for decoded_output in decoded_outputs:
+    print(decoded_output)
+    print("=" * 80)
+```
+
+This model was contributed by [Eustache Le Bihan](https://huggingface.co/eustlb).
+
+## VoxtralConfig
+
+[[autodoc]] VoxtralConfig
+
+## VoxtralEncoderConfig
+
+[[autodoc]] VoxtralEncoderConfig
+
+## VoxtralProcessor
+
+[[autodoc]] VoxtralProcessor
+
+## VoxtralEncoder
+
+[[autodoc]] VoxtralEncoder
+    - forward
+
+## VoxtralForConditionalGeneration
+
+[[autodoc]] VoxtralForConditionalGeneration
+    - forward

docs/source/en/model_doc/wav2vec2.md

@@ -172,9 +172,9 @@ Otherwise, [`~Wav2Vec2ProcessorWithLM.batch_decode`] performance will be slower
 >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
 
 
->>> def map_to_array(batch):
-...     batch["speech"] = batch["audio"]["array"]
-...     return batch
+>>> def map_to_array(example):
+...     example["speech"] = example["audio"]["array"]
+...     return example
 
 
 >>> # prepare speech data for batch inference

docs/source/en/model_doc/xlstm.md

@@ -0,0 +1,47 @@
+<!--Copyright 2025 NXAI GmbH. 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.
+
+-->
+
+
+# xLSTM
+
+## Overview
+
+The xLSTM model was proposed in [xLSTM: Extended Long Short-Term Memory](https://openreview.net/forum?id=ARAxPPIAhq) by Maximilian Beck*, Korbinian Pöppel*, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter and Sepp Hochreiter.
+xLSTM updates the original LSTM architecture to be competitive with Transformer models by introducing exponential gating, matrix memory expansion, and parallelizable training and ingestion.
+
+The [7B model](https://hf.co/NX-AI/xLSTM-7b) variant was trained by the xLSTM team Maximilian Beck, Korbinian Pöppel, Phillip Lippe, Richard Kurle, Patrick Blies, Sebastian Böck and Sepp Hochreiter at NXAI.
+
+The abstract from the paper is the following:
+
+*In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale. We now raise a simple question: How far do we get in language modeling when scaling LSTMs to billions of parameters, leveraging the latest techniques from modern LLMs, but mitigating known limitations of LSTMs? Firstly, we introduce exponential gating with appropriate normalization and stabilization techniques. Secondly, we modify the LSTM memory structure, obtaining: (i) sLSTM with a scalar memory, a scalar update, and new memory mixing, (ii) mLSTM that is fully parallelizable with a matrix memory and a covariance update rule. Integrating these LSTM extensions into residual block backbones yields xLSTM blocks that are then residually stacked into xLSTM architectures. Exponential gating and modified memory structures boost xLSTM capabilities to perform favorably when compared to state-of-the-art Transformers and State Space Models, both in performance and scaling.*
+
+This model was contributed by [NX-AI](https://huggingface.co/NX-AI).
+The original code can be found [here](https://github.com/NX-AI/xlstm).
+
+
+## xLSTMConfig
+
+[[autodoc]] xLSTMConfig
+
+## xLSTMModel
+
+[[autodoc]] xLSTMModel
+    - forward
+
+## xLSTMLMHeadModel
+
+[[autodoc]] xLSTMForCausalLM
+    - forward

docs/source/en/model_doc/yolos.md

@@ -13,76 +13,95 @@ specific language governing permissions and limitations under the License.
 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="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>
 
 # YOLOS
 
-<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="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>
+[YOLOS](https://huggingface.co/papers/2106.00666) uses a [Vision Transformer (ViT)](./vit) for object detection with minimal modifications and region priors. It can achieve performance comparable to specialized object detection models and frameworks with knowledge about 2D spatial structures.
 
-## Overview
 
-The YOLOS model was proposed in [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://huggingface.co/papers/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
-YOLOS proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
+You can find all the original YOLOS checkpoints under the [HUST Vision Lab](https://huggingface.co/hustvl/models?search=yolos) organization.
 
-The abstract from the paper is the following:
-
-*Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
-alt="drawing" width="600"/>
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png" alt="drawing" width="600"/>
 
 <small> YOLOS architecture. Taken from the <a href="https://huggingface.co/papers/2106.00666">original paper</a>.</small>
 
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
 
-## Using Scaled Dot Product Attention (SDPA)
+> [!TIP]
+> This model wasa contributed by [nielsr](https://huggingface.co/nielsr).
+> Click on the YOLOS models in the right sidebar for more examples of how to apply YOLOS to different object detection tasks.
 
-PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function 
-encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the 
-[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) 
-or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
-page for more information.
+The example below demonstrates how to detect objects with [`Pipeline`] or the [`AutoModel`] class.
 
-SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set 
-`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
+<hfoptions id="usage">
+<hfoption id="Pipeline">
 
-```
-from transformers import AutoModelForObjectDetection
-model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", attn_implementation="sdpa", torch_dtype=torch.float16)
-...
+```py
+import torch
+from transformers import pipeline
+
+detector = pipeline(
+    task="object-detection",
+    model="hustvl/yolos-base",
+    torch_dtype=torch.float16,
+    device=0
+)
+detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
 ```
 
-For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
+</hfoption>
+<hfoption id="Automodel">
 
-On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32` and `hustvl/yolos-base` model, we saw the following speedups during inference.
+```py
+import torch
+from PIL import Image
+import requests
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
-|   Batch size |   Average inference time (ms), eager mode |   Average inference time (ms), sdpa model |   Speed up, Sdpa / Eager (x) |
-|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
-|            1 |                                       106 |                                        76 |                      1.39 |
-|            2 |                                       154 |                                        90 |                      1.71 |
-|            4 |                                       222 |                                       116 |                      1.91 |
-|            8 |                                       368 |                                       168 |                      2.19 |
+processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base")
+model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", torch_dtype=torch.float16, attn_implementation="sdpa").to("cuda")
+
+url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+inputs = processor(images=image, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+logits = outputs.logits.softmax(-1)
+scores, labels = logits[..., :-1].max(-1)
+boxes = outputs.pred_boxes
+
+threshold = 0.3
+keep = scores[0] > threshold
+
+filtered_scores = scores[0][keep]
+filtered_labels = labels[0][keep]
+filtered_boxes  = boxes[0][keep]
+
+width, height = image.size
+pixel_boxes = filtered_boxes * torch.tensor([width, height, width, height], device=boxes.device)
+
+for score, label, box in zip(filtered_scores, filtered_labels, pixel_boxes):
+    x0, y0, x1, y1 = box.tolist()
+    print(f"Label {model.config.id2label[label.item()]}: {score:.2f} at [{x0:.0f}, {y0:.0f}, {x1:.0f}, {y1:.0f}]")
+```
+
+</hfoption>
+</hfoptions>
+
+
+## Notes
+- Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](./detr), YOLOS doesn't require a `pixel_mask`.
 
 ## Resources
 
-A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.
-
-<PipelineTag pipeline="object-detection"/>
-
-- All example notebooks illustrating inference + fine-tuning [`YolosForObjectDetection`] on a custom dataset can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
-- Scripts for finetuning [`YolosForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
-- See also: [Object detection task guide](../tasks/object_detection)
-
-If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
-
-<Tip>
-
-Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
-
-</Tip>
+- Refer to these [notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS) for inference and fine-tuning with [`YolosForObjectDetection`] on a custom dataset.
 
 ## YolosConfig
 

docs/source/en/model_sharing.md

@@ -28,7 +28,7 @@ To share a model to the Hub, you need a Hugging Face [account](https://hf.co/joi
 <hfoption id="huggingface-CLI">
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 </hfoption>

docs/source/en/modular_transformers.md

@@ -94,7 +94,7 @@ ValueError: You defined `RobertaEmbeddings` in the modular_roberta.py, it should
 
 ## Implementing a modular file
 
-The easiest way to start is by browsing Transformers for a model similar to yours in order to inherit from it. Some good starting points are [Mistral](./model_doc/mistral), [Qwen2](./model_doc/qwen2), [Cohere](./model_doc/cohere) and [Cohere](./model_doc/cohere2), and [Llama](./model_doc/llama). Refer to the table below for components your model might be using and where you can inherit from.
+The easiest way to start is by browsing Transformers for a model similar to yours in order to inherit from it. Some good starting points are [Mistral](./model_doc/mistral), [Qwen2](./model_doc/qwen2), [Cohere](./model_doc/cohere) and [Cohere2](./model_doc/cohere2), and [Llama](./model_doc/llama). Refer to the table below for components your model might be using and where you can inherit from.
 
 | Component | Model |
 |---|---|

docs/source/en/optimizers.md

@@ -164,7 +164,7 @@ args = TrainingArguments(
     output_dir="./test-schedulefree",
     max_steps=1000,
     per_device_train_batch_size=4,
-+   optim="schedule_free_radamw,
++   optim="schedule_free_radamw",
 +   lr_scheduler_type="constant",
     gradient_checkpointing=True,
     logging_strategy="steps",
@@ -174,3 +174,29 @@ args = TrainingArguments(
     run_name="sfo",
 )
 ```
+
+## StableAdamW
+
+```bash
+pip install torch-optimi
+```
+
+[StableAdamW](https://arxiv.org/pdf/2304.13013) is a hybrid between AdamW and AdaFactor. It ports AdaFactor's update clipping into AdamW, which removes the need for gradient clipping. Otherwise, it behaves as a drop-in replacement for AdamW.
+
+> [!TIP]
+> If training on large batch sizes or still observing training loss spikes, consider reducing beta_2 between [0.95, 0.99].
+
+```diff
+args = TrainingArguments(
+    output_dir="./test-stable-adamw",
+    max_steps=1000,
+    per_device_train_batch_size=4,
++   optim="stable_adamw",
+    gradient_checkpointing=True,
+    logging_strategy="steps",
+    logging_steps=1,
+    learning_rate=2e-6,
+    save_strategy="no",
+    run_name="stable-adamw",
+)
+```

docs/source/en/perf_hardware.md

@@ -15,7 +15,7 @@ rendered properly in your Markdown viewer.
 
 # Build your own machine
 
-One of the most important consideration when building a machine for deep learning is the GPU choice. GPUs are the standard workhorse for deep learning owing to their tensor cores for performing very efficient matrix multiplication and high memory bandwidth. To train large models, you either need a more powerful GPU, multiple GPUs, or take advantage of techniques that offload some of the load to the CPU or NVMe.
+One of the most important considerations when building a machine for deep learning is the GPU choice. GPUs are the standard workhorse for deep learning owing to their tensor cores for performing very efficient matrix multiplication and high memory bandwidth. To train large models, you either need a more powerful GPU, multiple GPUs, or take advantage of techniques that offload some of the load to the CPU or NVMe.
 
 This guide provides some practical tips for setting up a GPU for deep learning. For a more detailed discussion and comparison of GPUs, take a look at the [Which GPU(s) to Get for Deep Learning](https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/) blog post.
 
@@ -25,11 +25,11 @@ High-end consumer GPUs may have two or three PCIe 8-pin power sockets, and you s
 
 Each PCIe 8-pin power cable should be connected to a 12V rail on the power supply unit (PSU) and can deliver up to 150W. Other GPUs may use a PCIe 12-pin connector which can deliver up to 500-600W. Lower-end GPUs may only use a PCIe 6-pin connector which supplies up to 75W.
 
-It is important the PSU has stable voltage otherwise it may not be able to supply the GPU with enough power to function properly during peak usage.
+It is important that the PSU maintains stable voltage; otherwise, it may fail to supply the GPU with enough power during peak usage.
 
 ## Cooling
 
-An overheated GPU throttles its performance and can even shutdown if it's too hot to prevent damage. Keeping the GPU temperature low, anywhere between 158 - 167F, is essential for delivering full performance and maintaining its lifespan. Once temperatures reach 183 - 194F, the GPU may begin to throttle performance.
+An overheated GPU throttles its performance and can even shutdown if it's too hot to prevent damage. Keeping the GPU temperature low, anywhere between 158–167°F, is essential for delivering full performance and maintaining its lifespan. Once temperatures reach 183 - 194°F, the GPU may begin to throttle performance.
 
 ## Multi-GPU connectivity