register pressure estimation and pruning for h200/b200

* use custom triton kernels for entropy from logits and selective softmax

* PR comments fixes

* fix out of bounds, include tests, include benchmarks

* chore: lint

.github/CONTRIBUTING.md

@@ -68,7 +68,12 @@ You can skip certain CI checks by including specific keywords in your commit mes
 
 ### Code Style
 
-axolotl uses [{codestyle}]({URLofCodestyle}) as its code style guide. Please ensure that your code follows these guidelines.
+axolotl uses [Ruff](https://docs.astral.sh/ruff/) as its code style guide. Please ensure that your code follows these guidelines.
+
+Use the pre-commit linter to ensure that your code is formatted consistently.
+```bash
+pre-commit run --all-files
+```
 
 ### Commit Messages
 
@@ -78,6 +83,6 @@ Write clear and concise commit messages that briefly describe the changes made i
 
 - [GitHub Help](https://help.github.com/)
 - [GitHub Pull Request Documentation](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests)
-- [{codestyle}]({URLofCodestyle})
+- [Ruff](https://docs.astral.sh/ruff/)
 
 Thank you once again for your interest in contributing to axolotl. We look forward to collaborating with you and creating an even better project together!

.github/workflows/base.yml

@@ -15,6 +15,9 @@ on:
       - '.github/workflows/base.yml'
   workflow_dispatch:
 
+permissions:
+  contents: read
+
 jobs:
   build-base:
     if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
@@ -51,14 +54,30 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
+          - cuda: "128"
+            cuda_version: 12.8.1
             cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
+            python_version: "3.11"
+            pytorch: 2.10.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.12"
+            pytorch: 2.10.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
+            platforms: "linux/amd64,linux/arm64"
+#          - cuda: "129"
+#            cuda_version: 12.9.1
+#            cudnn_version: ""
+#            python_version: "3.12"
+#            pytorch: 2.9.1
+#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+#            dockerfile: "Dockerfile-base"
+#            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -75,6 +94,14 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-base"
             platforms: "linux/amd64,linux/arm64"
+          - cuda: "130"
+            cuda_version: 13.0.0
+            cudnn_version: ""
+            python_version: "3.12"
+            pytorch: 2.10.0
+            torch_cuda_arch_list: "9.0+PTX"
+            dockerfile: "Dockerfile-base"
+            platforms: "linux/amd64,linux/arm64"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -100,7 +127,7 @@ jobs:
           images: |
             axolotlai/axolotl-base
       - name: Login to Docker Hub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -108,7 +135,7 @@ jobs:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Build
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
@@ -152,19 +179,43 @@ jobs:
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
-            cudnn_version: ""
             python_version: "3.12"
             pytorch: 2.9.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.10.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.12"
+            pytorch: 2.10.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+            platforms: "linux/amd64,linux/arm64"
+#          - cuda: "129"
+#            cuda_version: 12.9.1
+#            cudnn_version: ""
+#            python_version: "3.12"
+#            pytorch: 2.9.1
+#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+#            dockerfile: "Dockerfile-uv-base"
+#            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -181,6 +232,14 @@ jobs:
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
+          - cuda: "130"
+            cuda_version: 13.0.0
+            cudnn_version: ""
+            python_version: "3.12"
+            pytorch: 2.10.0
+            torch_cuda_arch_list: "9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+            platforms: "linux/amd64,linux/arm64"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -191,7 +250,7 @@ jobs:
           images: |
             axolotlai/axolotl-base-uv
       - name: Login to Docker Hub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -199,7 +258,7 @@ jobs:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Build
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}

.github/workflows/lint.yml

@@ -13,6 +13,9 @@ on:
        - ".pre-commit-config.yaml"
   workflow_dispatch:
 
+permissions:
+  contents: read
+
 jobs:
   pre-commit:
     name: pre-commit

.github/workflows/main.yml

@@ -8,6 +8,9 @@ on:
       - "v*"
   workflow_dispatch:
 
+permissions:
+  contents: read
+
 jobs:
   build-axolotl:
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
@@ -34,16 +37,28 @@ jobs:
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
             is_latest: true
-          - cuda: 129
-            cuda_version: 12.9.1
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+#          - cuda: 129
+#            cuda_version: 12.9.1
+#            python_version: "3.12"
+#            pytorch: 2.9.1
+#            axolotl_extras:
+#            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
+            python_version: "3.12"
+            pytorch: 2.10.0
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
@@ -86,11 +101,89 @@ jobs:
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
 
+  build-axolotl-uv:
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.12"
+            pytorch: 2.9.1
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+            is_latest: true
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+    runs-on: axolotl-gpu-runner
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Docker metadata
+        id: metadata
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+            axolotlai/axolotl-uv
+          tags: |
+            type=ref,event=branch
+            type=pep440,pattern={{version}}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      # guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
+      - name: Build and export to Docker
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: ${{ matrix.platforms }}
+          build-args: |
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            CUDA=${{ matrix.cuda }}
+            PYTORCH_VERSION=${{ matrix.pytorch }}
+            AXOLOTL_ARGS=${{ matrix.axolotl_args }}
+            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
+          file: ./docker/Dockerfile-uv
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: |
+            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
+          labels: ${{ steps.metadata.outputs.labels }}
+
   build-axolotl-cloud:
     needs: build-axolotl
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
+      fail-fast: false
       matrix:
         include:
           - cuda: 128
@@ -112,16 +205,28 @@ jobs:
             axolotl_extras:
             is_latest: true
             platforms: "linux/amd64,linux/arm64"
-          - cuda: 129
-            cuda_version: 12.9.1
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+#          - cuda: 129
+#            cuda_version: 12.9.1
+#            python_version: "3.12"
+#            pytorch: 2.9.1
+#            axolotl_extras:
+#            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
           - cuda: 130
             cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
+            python_version: "3.12"
+            pytorch: 2.10.0
             axolotl_extras:
             platforms: "linux/amd64,linux/arm64"
     runs-on: axolotl-gpu-runner
@@ -159,11 +264,86 @@ jobs:
              ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
           labels: ${{ steps.metadata.outputs.labels }}
 
+  build-axolotl-cloud-uv:
+    needs: build-axolotl-uv
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    # this job needs to be run on self-hosted GPU runners...
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.12"
+            pytorch: 2.9.1
+            axolotl_extras:
+            is_latest: true
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.12"
+            pytorch: 2.10.0
+            axolotl_extras:
+            platforms: "linux/amd64,linux/arm64"
+    runs-on: axolotl-gpu-runner
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Docker metadata
+        id: metadata
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+            axolotlai/axolotl-cloud-uv
+          tags: |
+            type=ref,event=branch
+            type=pep440,pattern={{version}}
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Build
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: ${{ matrix.platforms }}
+          build-args: |
+            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            CUDA=${{ matrix.cuda }}
+          file: ./docker/Dockerfile-cloud-uv
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: |
+             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
+          labels: ${{ steps.metadata.outputs.labels }}
+
   build-axolotl-cloud-no-tmux:
     needs: build-axolotl
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
+      fail-fast: false
       matrix:
         include:
           - cuda: 128

.github/workflows/multi-gpu-e2e.yml

@@ -8,6 +8,7 @@ on:
       - 'setup.py'
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
+      - 'scripts/cutcrossentropy_install.py'
       - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
       - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
@@ -19,6 +20,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
+permissions:
+  contents: read
+
 env:
   MODAL_IMAGE_BUILDER_VERSION: "2025.06"
 
@@ -35,19 +39,13 @@ jobs:
             pytorch: 2.8.0
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
-            num_gpus: 2
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
-            num_gpus: 2
-            dockerfile: "Dockerfile-uv.jinja"
+#          - cuda: 129
+#            cuda_version: 12.9.1
+#            python_version: "3.12"
+#            pytorch: 2.9.1
+#            axolotl_extras: "fbgemm-gpu"
+#            num_gpus: 2
+#            dockerfile: "Dockerfile-uv.jinja"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
@@ -55,6 +53,13 @@ jobs:
             axolotl_extras:
 #            axolotl_extras: fbgemm-gpu
             num_gpus: 2
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.10.0
+            axolotl_extras: "fbgemm-gpu"
+            num_gpus: 2
+            dockerfile: "Dockerfile-uv.jinja"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -76,8 +81,9 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
           echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
           modal run -m cicd.multigpu

.github/workflows/nightlies.yml

@@ -5,6 +5,9 @@ on:
   schedule:
     - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
 
+permissions:
+  contents: read
+
 jobs:
   build-axolotl:
     if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}

.github/workflows/precommit-autoupdate.yml

@@ -5,6 +5,8 @@ on:
     - cron: '0 0 1 * *'  # Run monthly
   workflow_dispatch:  # Manual kickoff
 
+permissions: {}
+
 jobs:
   auto-update:
     runs-on: ubuntu-latest

.github/workflows/preview-docs.yml

@@ -14,14 +14,8 @@ on:
       - .github/workflows/preview-docs.yml
 
 permissions:
-  checks: write
-  contents: write
-  deployments: write
-  issues: write
-  discussions: write
-  pages: write
+  contents: read
   pull-requests: write
-  statuses: write
 
 jobs:
   preview:

.github/workflows/pypi.yml

@@ -3,9 +3,11 @@ name: publish pypi
 on:
   push:
     tags:
-      - 'v*'
+      - "v*"
   workflow_dispatch:
 
+permissions: {}
+
 jobs:
   setup_release:
     name: Create Release
@@ -28,7 +30,8 @@ jobs:
       name: pypi
       url: https://pypi.org/p/axolotl
     permissions:
-      id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
+      contents: read
+      id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
         uses: actions/checkout@v4
@@ -46,7 +49,7 @@ jobs:
 
       - name: Extract tag name
         id: tag
-        run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
+        run: echo "TAG_NAME=$(echo $GITHUB_REF | cut -d / -f 3)" >> "$GITHUB_OUTPUT"
 
       - name: Update version in VERSION file
         run: |

.github/workflows/tests-nightly.yml

@@ -3,6 +3,13 @@ on:
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+    paths:
+      - '.github/workflows/tests-nightly.yml'
+
+permissions:
+  contents: read
 
 jobs:
   pre-commit:
@@ -18,15 +25,26 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
+  prime-cdn-s3-cache:
+    name: Prefetch S3 once to prime the CDN cache
+    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft }}
+    timeout-minutes: 10
+    steps:
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          curl -v -H "Range: bytes=0-1023" -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst > /dev/null
+
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
+    needs: [prime-cdn-s3-cache]
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
+        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
     timeout-minutes: 20
 
     steps:
@@ -37,7 +55,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p /home/runner/.cache/huggingface/hub
-          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
+          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -48,7 +66,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging==26.0 setuptools==78.1.1 wheel
 
       - name: Install PyTorch
         run: |
@@ -102,16 +120,23 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
+            pytorch: 2.10.0
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 130
+            cuda_version: 13.0.0
+            python_version: "3.12"
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
+            dockerfile: "Dockerfile-uv.jinja"
             nightly_build: "true"
     steps:
       - name: Checkout
@@ -132,9 +157,11 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
           modal run cicd.e2e_tests
   docker-e2e-multigpu-tests:
@@ -175,7 +202,8 @@ jobs:
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
           modal run cicd.multigpu

.github/workflows/tests.yml

@@ -28,6 +28,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
+permissions:
+  contents: read
+
 env:
   TRANSFORMERS_IS_CI: "yes"
 
@@ -46,21 +49,32 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
+  prime-cdn-s3-cache:
+    name: Prefetch S3 once to prime the CDN cache
+    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft }}
+    timeout-minutes: 10
+    steps:
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          curl -v -H "Range: bytes=0-1023" -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst > /dev/null
+
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
     if: ${{ !github.event.pull_request.draft }}
-#    needs: [preload-cache]
+    needs: [prime-cdn-s3-cache]
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
+        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
+#        exclude:
+#          - python_version: "3.14"
+#            pytorch_version: "2.8.0"
+#          - python_version: "3.14"
+#            pytorch_version: "2.9.1"
     timeout-minutes: 20
 
     steps:
@@ -75,7 +89,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p ~/.cache/huggingface/hub
-          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
+          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
           ls -ltr ~/.cache/huggingface/hub/
 
       - name: Setup Python
@@ -146,17 +160,18 @@ jobs:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
     if: ${{ !github.event.pull_request.draft }}
+    needs: [prime-cdn-s3-cache]
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
-    timeout-minutes: 20
+        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        pytorch_version: ["2.8.0", "2.9.1", "2.10.0"]
+#        exclude:
+#          - python_version: "3.14"
+#            pytorch_version: "2.8.0"
+#          - python_version: "3.14"
+#            pytorch_version: "2.9.1"
+    timeout-minutes: 30
 
     steps:
       - name: cleanup node
@@ -170,7 +185,7 @@ jobs:
         id: hf-cache-restore-s3
         run: |
           mkdir -p ~/.cache/huggingface/hub
-          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
+          curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
           ls -ltr ~/.cache/huggingface/hub/
 
       - name: Setup Python
@@ -264,8 +279,8 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
+          - cuda: 130
+            cuda_version: 13.0.0
             python_version: "3.12"
             pytorch: 2.9.1
             num_gpus: 1
@@ -291,9 +306,10 @@ jobs:
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
           echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
           modal run cicd.e2e_tests
 
@@ -326,6 +342,12 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.10.0
+            num_gpus: 1
+            axolotl_extras:
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
@@ -353,9 +375,10 @@ jobs:
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
           echo "GPU_TYPE=${{ matrix.gpu_type || 'L40S'}}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
           echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
           modal run cicd.e2e_tests
 
@@ -369,9 +392,9 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
@@ -395,7 +418,6 @@ jobs:
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
           modal run cicd.cleanup

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.10
+    rev: v0.15.4
     hooks:
     -   id: ruff
         args: [--fix]
@@ -26,7 +26,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.9.2
+    rev: 1.9.4
     hooks:
     -   id: bandit
         args: [

.runpod/README.md

@@ -123,7 +123,7 @@ datasets:
 | --------------------------------- | -------------------------- | ----------------------------------- |
 | `dataset_prepared_path`           | `"data/last_run_prepared"` | Path for prepared dataset           |
 | `push_dataset_to_hub`             | `""`                       | Push dataset to HF hub              |
-| `dataset_processes`               | `4`                        | Number of preprocessing processes   |
+| `dataset_num_proc`                | `4`                        | Number of preprocessing processes   |
 | `dataset_keep_in_memory`          | `false`                    | Keep dataset in memory              |
 | `shuffle_merged_datasets`         | `true`                     | Shuffle merged datasets             |
 | `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |

.runpod/src/config/config.yaml

@@ -39,7 +39,6 @@
 #     type: # linear | dynamic
 #     factor: # float
 
-
 # # Whether you are training a 4-bit GPTQ quantized model
 # gptq: true
 # gptq_groupsize: 128 # group size
@@ -107,7 +106,7 @@
 # push_dataset_to_hub: # repo path
 # # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # # if not set.
-# dataset_processes: # defaults to os.cpu_count() if not set
+# dataset_num_proc: # defaults to os.cpu_count() if not set
 # # push checkpoints to hub
 # hub_model_id: # repo path to push finetuned model
 # # how to push checkpoints to hub
@@ -349,8 +348,6 @@
 # # Allow overwrite yml config using from cli
 # strict:
 
-
-
 base_model: ${BASE_MODEL}
 base_model_ignore_patterns: ${BASE_MODEL_IGNORE_PATTERNS}
 base_model_config: ${BASE_MODEL_CONFIG}
@@ -409,7 +406,7 @@ chat_template_jinja: ${CHAT_TEMPLATE_JINJA}
 default_system_message: ${DEFAULT_SYSTEM_MESSAGE}
 dataset_prepared_path: ${DATASET_PREPARED_PATH}
 push_dataset_to_hub: ${PUSH_DATASET_TO_HUB}
-dataset_processes: ${DATASET_PROCESSES}
+dataset_num_proc: ${DATASET_NUM_PROC}
 dataset_keep_in_memory: ${DATASET_KEEP_IN_MEMORY}
 hub_model_id: ${HUB_MODEL_ID}
 hub_strategy: ${HUB_STRATEGY}

README.md

@@ -29,8 +29,23 @@
 
 ## 🎉 Latest Updates
 
-- 2025/12: Axolotl now includes support for [Kimi-Linear](https://docs.axolotl.ai/docs/models/kimi-linear.html), [Plano-Orchestrator](https://docs.axolotl.ai/docs/models/plano.html), [MiMo](https://docs.axolotl.ai/docs/models/mimo.html), [InternVL 3.5](https://docs.axolotl.ai/docs/models/internvl3_5.html), [Olmo3](https://docs.axolotl.ai/docs/models/olmo3.html), [Trinity](https://docs.axolotl.ai/docs/models/trinity.html), and [Ministral3](https://docs.axolotl.ai/docs/models/ministral3.html).
+- 2026/03:
+  - New model support has been added in Axolotl for [Mistral Small 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/mistral4), [Qwen3.5, Qwen3.5 MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5), [GLM-4.7-Flash](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash), [GLM-4.6V](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v), and [GLM-4.5-Air](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45).
+  - [MoE expert quantization](https://docs.axolotl.ai/docs/expert_quantization.html) support (via `quantize_moe_experts: true`) greatly reduces VRAM when training MoE models (FSDP2 compat).
+- 2026/02:
+  - [ScatterMoE LoRA](https://github.com/axolotl-ai-cloud/axolotl/pull/3410) support. LoRA fine-tuning directly on MoE expert weights using custom Triton kernels.
+  - Axolotl now has support for [SageAttention](https://github.com/axolotl-ai-cloud/axolotl/pull/2823) and [GDPO](https://github.com/axolotl-ai-cloud/axolotl/pull/3353) (Generalized DPO).
+- 2026/01:
+  - New integration for [EAFT](https://github.com/axolotl-ai-cloud/axolotl/pull/3366) (Entropy-Aware Focal Training), weights loss by entropy of the top-k logit distribution, and [Scalable Softmax](https://github.com/axolotl-ai-cloud/axolotl/pull/3338), improves long context in attention.
+- 2025/12:
+  - Axolotl now includes support for [Kimi-Linear](https://docs.axolotl.ai/docs/models/kimi-linear.html), [Plano-Orchestrator](https://docs.axolotl.ai/docs/models/plano.html), [MiMo](https://docs.axolotl.ai/docs/models/mimo.html), [InternVL 3.5](https://docs.axolotl.ai/docs/models/internvl3_5.html), [Olmo3](https://docs.axolotl.ai/docs/models/olmo3.html), [Trinity](https://docs.axolotl.ai/docs/models/trinity.html), and [Ministral3](https://docs.axolotl.ai/docs/models/ministral3.html).
+  - [Distributed Muon Optimizer](https://github.com/axolotl-ai-cloud/axolotl/pull/3264) support has been added for FSDP2 pretraining.
 - 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://docs.axolotl.ai/docs/models/qwen3-next.html), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://docs.axolotl.ai/docs/models/qwen3.html), [Granite 4](https://docs.axolotl.ai/docs/models/granite4.html), [HunYuan](https://docs.axolotl.ai/docs/models/hunyuan.html), [Magistral 2509](https://docs.axolotl.ai/docs/models/magistral/vision.html), [Apertus](https://docs.axolotl.ai/docs/models/apertus.html), and [Seed-OSS](https://docs.axolotl.ai/docs/models/seed-oss.html).
+
+<details>
+
+<summary>Expand older updates</summary>
+
 - 2025/09: Axolotl now has text diffusion training. Read more [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion).
 - 2025/08: QAT has been updated to include NVFP4 support. See [PR](https://github.com/axolotl-ai-cloud/axolotl/pull/3107).
 - 2025/07:
@@ -39,15 +54,10 @@
   - FP8 finetuning with fp8 gather op is now possible in Axolotl via `torchao`. Get started [here](https://docs.axolotl.ai/docs/mixed_precision.html#sec-fp8)!
   - [Voxtral](https://docs.axolotl.ai/docs/models/voxtral.html), [Magistral 1.1](https://docs.axolotl.ai/docs/models/magistral.html), and [Devstral](https://docs.axolotl.ai/docs/models/devstral.html) with mistral-common tokenizer support has been integrated in Axolotl!
   - TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!
-- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
-
-<details>
-
-<summary>Expand older updates</summary>
-
-- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 - 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [docs](https://docs.axolotl.ai/docs/models/magistral.html) to start training your own Magistral models with Axolotl!
+- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
 - 2025/04: Llama 4 support has been added in Axolotl. See [docs](https://docs.axolotl.ai/docs/models/llama-4.html) to start training your own Llama 4 models with Axolotl's linearized version!
+- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 - 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!
 - 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try.
 - 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun!
@@ -62,10 +72,10 @@ Axolotl is a free and open-source tool designed to streamline post-training and
 Features:
 
 - **Multiple Model Support**: Train various models like GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many more models available on the Hugging Face Hub.
-- **Multimodal Training**: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, and audio models like Voxtral with image, video, and audio support.
-- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).
+- **Multimodal Training**: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, GLM-4.6V, InternVL 3.5, Gemma 3n, and audio models like Voxtral with image, video, and audio support.
+- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO, GDPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).
 - **Easy Configuration**: Re-use a single YAML configuration file across the full fine-tuning pipeline: dataset preprocessing, training, evaluation, quantization, and inference.
-- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
+- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention 2/3/4](https://docs.axolotl.ai/docs/attention.html#flash-attention), [Xformers](https://docs.axolotl.ai/docs/attention.html#xformers), [Flex Attention](https://docs.axolotl.ai/docs/attention.html#flex-attention), [SageAttention](https://docs.axolotl.ai/docs/attention.html#sageattention), [Liger Kernel](https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels), [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy), [ScatterMoE](https://docs.axolotl.ai/docs/custom_integrations.html#kernels-integration), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
 - **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
 - **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.
 

VERSION

@@ -1 +1 @@
-0.15.0.dev0
+0.16.0.dev0

_quarto.yml

@@ -251,7 +251,6 @@ website:
                 - docs/models/olmo3.qmd
                 - docs/models/trinity.qmd
                 - docs/models/arcee.qmd
-                - docs/models/mistral.qmd
                 - section: "Ministral3"
                   contents:
                     - docs/models/ministral3.qmd
@@ -266,6 +265,7 @@ website:
                 - docs/models/mistral-small.qmd
                 - docs/models/voxtral.qmd
                 - docs/models/devstral.qmd
+                - docs/models/mistral.qmd
                 - docs/models/llama-4.qmd
                 - docs/models/llama-2.qmd
                 - docs/models/qwen3-next.qmd
@@ -320,6 +320,7 @@ website:
             - docs/multipack.qmd
             - docs/mixed_precision.qmd
             - docs/optimizers.qmd
+            - docs/attention.qmd
 
         - section: "Advanced Features"
           contents:
@@ -330,6 +331,7 @@ website:
             - docs/sequence_parallelism.qmd
             - docs/gradient_checkpointing.qmd
             - docs/nd_parallelism.qmd
+            - docs/expert_quantization.qmd
 
         - section: "Troubleshooting"
           contents:

benchmarks/bench_entropy.py

@@ -0,0 +1,208 @@
+"""Benchmark for entropy_from_logits Triton kernel vs original chunked implementation.
+
+Usage: CUDA_VISIBLE_DEVICES=0 python benchmarks/bench_entropy.py
+"""
+
+import gc
+import statistics
+
+import torch
+import torch.nn.functional as F
+
+from axolotl.monkeypatch.trainer.utils import entropy_from_logits
+
+V = 151936  # Qwen vocab
+WARMUP = 5
+BENCH_ITERS = 20
+MEM_ITERS = 10
+
+
+def entropy_from_logits_original(logits: torch.Tensor, chunk_size: int = 128):
+    """Original chunked implementation (reference)."""
+    original_shape = logits.shape[:-1]
+    num_classes = logits.shape[-1]
+    flat_logits = logits.reshape(-1, num_classes)
+    entropies = []
+    for chunk in flat_logits.split(chunk_size, dim=0):
+        logps = F.log_softmax(chunk, dim=-1)
+        chunk_entropy = -(torch.exp(logps) * logps).sum(-1)
+        entropies.append(chunk_entropy)
+    return torch.cat(entropies, dim=0).reshape(original_shape)
+
+
+def _clean_gpu():
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_peak_memory_stats()
+    torch.cuda.reset_accumulated_memory_stats()
+    torch.cuda.synchronize()
+
+
+def profile_time(fn, logits, n_iters=BENCH_ITERS):
+    for _ in range(WARMUP):
+        out = fn(logits, chunk_size=128)
+        del out
+    torch.cuda.synchronize()
+
+    times = []
+    for _ in range(n_iters):
+        s = torch.cuda.Event(enable_timing=True)
+        e = torch.cuda.Event(enable_timing=True)
+        s.record()
+        out = fn(logits, chunk_size=128)
+        e.record()
+        torch.cuda.synchronize()
+        times.append(s.elapsed_time(e))
+        del out
+    return times
+
+
+def profile_memory(fn, logits, n_iters=MEM_ITERS):
+    for _ in range(WARMUP):
+        out = fn(logits, chunk_size=128)
+        del out
+    torch.cuda.synchronize()
+
+    peaks = []
+    for _ in range(n_iters):
+        _clean_gpu()
+        base = torch.cuda.max_memory_allocated()
+        out = fn(logits, chunk_size=128)
+        torch.cuda.synchronize()
+        peaks.append(torch.cuda.max_memory_allocated() - base)
+        del out
+    return [p / 1e6 for p in peaks]
+
+
+def fmt(values, unit=""):
+    mean = statistics.mean(values)
+    std = statistics.stdev(values) if len(values) > 1 else 0.0
+    return f"{mean:8.2f} ± {std:5.2f} {unit}  [min={min(values):.2f}, max={max(values):.2f}]"
+
+
+def benchmark_contiguous():
+    print("=" * 60)
+    print(
+        f"CONTIGUOUS BENCHMARK  (warmup={WARMUP}, time={BENCH_ITERS}, mem={MEM_ITERS})"
+    )
+    print("=" * 60)
+
+    configs = [
+        (1, 2048),
+        (1, 8192),
+        (1, 16384),
+        (4, 4096),
+        (8, 2048),
+        (16, 2048),
+        (16, 4096),
+    ]
+
+    for B, L in configs:
+        mem_gb = B * L * V * 2 / 1e9
+        if mem_gb > 28:
+            print(f"\n  skip B={B}, L={L} ({mem_gb:.1f} GB)")
+            continue
+
+        N = B * L
+        print(f"\n{'─' * 60}")
+        print(f"B={B:2d}, L={L:5d}  ({N:6d} rows, logits {mem_gb:.2f} GB)")
+        print(f"{'─' * 60}")
+
+        torch.manual_seed(42)
+        logits = torch.randn(B, L, V, device="cuda", dtype=torch.bfloat16)
+
+        t_orig = profile_time(entropy_from_logits_original, logits)
+        t_triton = profile_time(entropy_from_logits, logits)
+        orig_mean = statistics.mean(t_orig)
+        triton_mean = statistics.mean(t_triton)
+
+        print("  TIME (ms):")
+        print(f"    original: {fmt(t_orig, 'ms')}")
+        print(f"    triton:   {fmt(t_triton, 'ms')}")
+        print(f"    speedup:  {orig_mean / triton_mean:.2f}x")
+
+        m_orig = profile_memory(entropy_from_logits_original, logits)
+        m_triton = profile_memory(entropy_from_logits, logits)
+        orig_peak = statistics.mean(m_orig)
+        triton_peak = statistics.mean(m_triton)
+
+        print("  MEMORY (peak overhead):")
+        print(f"    original: {fmt(m_orig, 'MB')}")
+        print(f"    triton:   {fmt(m_triton, 'MB')}")
+        print(f"    saved:    {orig_peak - triton_peak:.1f} MB")
+
+        del logits
+        _clean_gpu()
+
+
+def benchmark_noncontiguous():
+    print("\n" + "=" * 60)
+    print(
+        f"NON-CONTIGUOUS BENCHMARK  (warmup={WARMUP}, time={BENCH_ITERS}, mem={MEM_ITERS})"
+    )
+    print("=" * 60)
+
+    configs = [
+        (4, 2048, "transpose"),
+        (4, 8192, "transpose"),
+        (8, 2048, "transpose"),
+        (4, 4096, "slice_batch"),
+    ]
+
+    for B, L, method in configs:
+        torch.manual_seed(42)
+
+        if method == "transpose":
+            raw = torch.randn(L, B, V, device="cuda", dtype=torch.bfloat16)
+            logits_nc = raw.transpose(0, 1)
+            raw_gb = L * B * V * 2 / 1e9
+        elif method == "slice_batch":
+            raw = torch.randn(B * 2, L, V, device="cuda", dtype=torch.bfloat16)
+            logits_nc = raw[::2]
+            raw_gb = B * 2 * L * V * 2 / 1e9
+        else:
+            continue
+
+        if raw_gb > 28:
+            print(f"\n  skip B={B}, L={L}, {method} ({raw_gb:.1f} GB)")
+            del raw, logits_nc
+            torch.cuda.empty_cache()
+            continue
+
+        N = B * L
+        print(f"\n{'─' * 60}")
+        print(f"B={B}, L={L}  {method}  ({N} rows, raw {raw_gb:.2f} GB)")
+        print(f"{'─' * 60}")
+
+        def original_with_copy(logits, chunk_size=128):
+            return entropy_from_logits_original(
+                logits.contiguous(), chunk_size=chunk_size
+            )
+
+        t_orig = profile_time(original_with_copy, logits_nc)
+        t_triton = profile_time(entropy_from_logits, logits_nc)
+        orig_mean = statistics.mean(t_orig)
+        triton_mean = statistics.mean(t_triton)
+
+        print("  TIME (ms):")
+        print(f"    orig+copy:     {fmt(t_orig, 'ms')}")
+        print(f"    triton-strided:{fmt(t_triton, 'ms')}")
+        print(f"    speedup:       {orig_mean / triton_mean:.2f}x")
+
+        m_orig = profile_memory(original_with_copy, logits_nc)
+        m_triton = profile_memory(entropy_from_logits, logits_nc)
+        orig_peak = statistics.mean(m_orig)
+        triton_peak = statistics.mean(m_triton)
+
+        print("  MEMORY (peak overhead):")
+        print(f"    orig+copy:     {fmt(m_orig, 'MB')}")
+        print(f"    triton-strided:{fmt(m_triton, 'MB')}")
+        print(f"    saved:         {orig_peak - triton_peak:.1f} MB")
+
+        del raw, logits_nc
+        _clean_gpu()
+
+
+if __name__ == "__main__":
+    benchmark_contiguous()
+    benchmark_noncontiguous()

benchmarks/bench_scattermoe_lora.py

@@ -0,0 +1,284 @@
+"""Benchmark for ScatterMoE LoRA Triton kernels.
+
+Measures forward, backward dX, and backward dA/dB kernels at common MoE
+model shapes. Reports per-kernel timings, LoRA overhead vs base scatter2scatter,
+and full fwd+bwd autograd throughput.
+
+Usage:
+  CUDA_VISIBLE_DEVICES=0 python benchmarks/bench_scattermoe_lora.py
+  CUDA_VISIBLE_DEVICES=0 python benchmarks/bench_scattermoe_lora.py --ranks 16 64
+  CUDA_VISIBLE_DEVICES=0 python benchmarks/bench_scattermoe_lora.py --models Qwen/Qwen3.5-35B-A3B
+"""
+
+import argparse
+import gc
+import time
+from functools import partial
+
+import torch
+
+from axolotl.integrations.kernels.libs.scattermoe_lora.kernels import (
+    lora_ops,
+    ops as base_ops,
+)
+from axolotl.integrations.kernels.libs.scattermoe_lora.parallel_experts import (
+    flatten_sort_count,
+)
+from axolotl.integrations.kernels.libs.scattermoe_lora.parallel_linear_lora import (
+    ScatterMoELoRA,
+)
+
+DEVICE = "cuda"
+DTYPE = torch.bfloat16
+WARMUP = 5
+ITERS = 20
+
+# ─── Model configs ──────────────────────────────────────────────────────────
+
+BUILTIN_CONFIGS = {
+    "Qwen3.5-35B-A3B": (256, 2048, 512, 8),  # E, H, I, k
+    "Qwen3-30B-A3B": (128, 2048, 768, 8),
+    "OLMoE-1B-7B": (64, 2048, 1024, 8),
+    "Mixtral-8x7B": (8, 4096, 14336, 2),
+}
+
+
+def _resolve_config(spec):
+    """Resolve a model spec to (E, H, I, k). Accepts builtin names or HF IDs."""
+    key = spec.lower().replace("/", "-")
+    for name, cfg in BUILTIN_CONFIGS.items():
+        if key in name.lower() or name.lower() in key:
+            return name, cfg
+
+    from transformers import AutoConfig
+
+    hf_cfg = AutoConfig.from_pretrained(spec, trust_remote_code=True)
+    if callable(getattr(hf_cfg, "get_text_config", None)):
+        tc = hf_cfg.get_text_config()
+        if hasattr(tc, "model_type") and tc.model_type != hf_cfg.model_type:
+            hf_cfg = tc
+    hidden = hf_cfg.hidden_size
+    inter = getattr(hf_cfg, "moe_intermediate_size", None) or hf_cfg.intermediate_size
+    experts = (
+        getattr(hf_cfg, "num_experts", None)
+        or getattr(hf_cfg, "num_local_experts", None)
+        or getattr(hf_cfg, "n_routed_experts", None)
+    )
+    top_k = (
+        getattr(hf_cfg, "num_experts_per_tok", None)
+        or getattr(hf_cfg, "num_experts_per_token", None)
+        or 2
+    )
+    name = spec.split("/")[-1]
+    return name, (experts, hidden, inter, top_k)
+
+
+# ─── Benchmark helpers ──────────────────────────────────────────────────────
+
+
+def _clean():
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+
+
+def _bench(fn, warmup=WARMUP, iters=ITERS):
+    for _ in range(warmup):
+        fn()
+    torch.cuda.synchronize()
+    times = []
+    for _ in range(iters):
+        torch.cuda.synchronize()
+        t0 = time.perf_counter()
+        fn()
+        torch.cuda.synchronize()
+        times.append((time.perf_counter() - t0) * 1000)
+    times.sort()
+    return times[len(times) // 2]
+
+
+def _setup(num_experts, K, N, T, top_k, R):
+    torch.manual_seed(42)
+    x = torch.randn(T, K, device=DEVICE, dtype=DTYPE)
+    W = torch.randn(num_experts, K, N, device=DEVICE, dtype=DTYPE) * 0.02
+    lora_A = torch.randn(R * num_experts, K, device=DEVICE, dtype=DTYPE) * 0.01
+    lora_B = torch.randn(N, R * num_experts, device=DEVICE, dtype=DTYPE) * 0.01
+    logits = torch.randn(T, num_experts, device=DEVICE)
+    _, top_idx = torch.topk(torch.softmax(logits, dim=-1), top_k, dim=-1)
+    sei, ssi, eo = flatten_sort_count(top_idx, num_experts)
+    gx = base_ops.group(x, ssi, fan_out=top_k)
+    dy = torch.randn(gx.size(0), N, device=DEVICE, dtype=DTYPE)
+    return x, W, lora_A, lora_B, sei, ssi, eo, gx, dy
+
+
+# ─── Kernel wrappers (avoid B023 loop-variable capture) ──────────────────────
+
+
+def _call_fwd(x, W, sei, ssi, top_k, lA, lB):
+    return lora_ops.scatter2scatter_lora(
+        X=x,
+        W=W,
+        sorted_expert_idxs=sei,
+        sorted_scattered_idxs=ssi,
+        k=top_k,
+        lora_A=lA,
+        lora_B=lB,
+        scaling=2.0,
+    )
+
+
+def _call_base(x, W, sei, ssi, top_k):
+    return base_ops.scatter2scatter(
+        X=x,
+        W=W,
+        sorted_expert_idxs=sei,
+        sorted_scattered_idxs=ssi,
+        k=top_k,
+    )
+
+
+def _call_dx(dy, W, sei, ssi, lA, lB):
+    return lora_ops.scatter2scatter_lora_dX(
+        DY=dy,
+        W=W,
+        sorted_expert_idxs=sei,
+        sorted_scattered_idxs=ssi,
+        k=1,
+        lora_A=lA,
+        lora_B=lB,
+        scaling=2.0,
+        dy_grouped=True,
+        dx_grouped=False,
+    )
+
+
+def _call_bwd(dy, gx, lA, lB, eo, num_experts):
+    return lora_ops.group_bwd_lora(
+        DY=dy,
+        X=gx,
+        lora_A=lA,
+        lora_B=lB,
+        expert_offsets=eo,
+        E=num_experts,
+        scaling=2.0,
+    )
+
+
+# ─── Main ────────────────────────────────────────────────────────────────────
+
+
+def main():
+    parser = argparse.ArgumentParser(description="ScatterMoE LoRA kernel benchmark")
+    parser.add_argument(
+        "--models",
+        "-m",
+        nargs="+",
+        help="Model names or HF IDs (default: all builtins)",
+    )
+    parser.add_argument("--ranks", "-r", nargs="+", type=int, default=[16, 32, 64])
+    parser.add_argument("--seq-len", "-T", type=int, default=2048)
+    args = parser.parse_args()
+
+    T = args.seq_len
+    print(f"GPU: {torch.cuda.get_device_name()}")
+    print(f"T={T}, ranks={args.ranks}\n")
+
+    if args.models:
+        configs = [_resolve_config(m) for m in args.models]
+    else:
+        configs = list(BUILTIN_CONFIGS.items())
+
+    for model_name, (num_experts, hidden, inter, top_k) in configs:
+        print(f"{'=' * 70}")
+        print(f"  {model_name}: E={num_experts}, H={hidden}, I={inter}, k={top_k}")
+        print(f"{'=' * 70}")
+
+        for R in args.ranks:
+            for proj, K, N in [("gate_up", hidden, 2 * inter), ("down", inter, hidden)]:
+                _clean()
+                x, W, lA, lB, sei, ssi, eo, gx, dy = _setup(
+                    num_experts, K, N, T, top_k, R
+                )
+
+                # Forward with LoRA (auto-dispatched: fused or split)
+                dispatch = (
+                    "split"
+                    if (
+                        num_experts <= lora_ops._SPLIT_LORA_FWD_MAX_EXPERTS
+                        and K * N >= lora_ops._SPLIT_LORA_FWD_THRESHOLD
+                    )
+                    else "fused"
+                )
+                t_fwd = _bench(partial(_call_fwd, x, W, sei, ssi, top_k, lA, lB))
+                t_base = _bench(partial(_call_base, x, W, sei, ssi, top_k))
+                t_dx = _bench(partial(_call_dx, dy, W, sei, ssi, lA, lB))
+                t_bwd = _bench(partial(_call_bwd, dy, gx, lA, lB, eo, num_experts))
+
+                total = t_fwd + t_dx + t_bwd
+                overhead = t_fwd / t_base - 1 if t_base > 0 else 0
+
+                print(
+                    f"  R={R:>2} {proj:<8}  "
+                    f"fwd={t_fwd:>6.2f}ms [{dispatch}]  "
+                    f"base={t_base:>6.2f}ms "
+                    f"(+{overhead * 100:.0f}%)  "
+                    f"dx={t_dx:>6.2f}ms  bwd={t_bwd:>6.2f}ms  "
+                    f"total={total:>6.2f}ms"
+                )
+
+                # Full autograd fwd+bwd with memory measurement
+                x_ag = x.clone().requires_grad_(True)
+                lA_ag = lA.clone().requires_grad_(True)
+                lB_ag = lB.clone().requires_grad_(True)
+
+                def _run_autograd(
+                    _x=x_ag,
+                    _W=W,
+                    _k=top_k,
+                    _sei=sei,
+                    _ssi=ssi,
+                    _eo=eo,
+                    _lA=lA_ag,
+                    _lB=lB_ag,
+                ):
+                    out = ScatterMoELoRA.apply(
+                        _x,
+                        _W,
+                        _k,
+                        _sei,
+                        _ssi,
+                        _eo,
+                        _lA,
+                        _lB,
+                        2.0,
+                        None,
+                        None,
+                        False,
+                        False,
+                        True,
+                        False,
+                    )
+                    out.sum().backward()
+                    _x.grad = None
+                    _lA.grad = None
+                    _lB.grad = None
+
+                t_full = _bench(_run_autograd)
+
+                _clean()
+                torch.cuda.reset_peak_memory_stats()
+                mem_before = torch.cuda.memory_allocated()
+                _run_autograd()
+                torch.cuda.synchronize()
+                mem_peak = torch.cuda.max_memory_allocated() - mem_before
+
+                print(
+                    f"         full_fwd_bwd={t_full:>6.2f}ms  "
+                    f"peak_delta={mem_peak / 1e6:>6.1f}MB"
+                )
+
+        print()
+
+
+if __name__ == "__main__":
+    main()

benchmarks/bench_selective_logsoftmax.py

@@ -0,0 +1,191 @@
+"""Benchmark for selective_log_softmax Triton kernel vs original implementation.
+
+Usage: CUDA_VISIBLE_DEVICES=0 python benchmarks/bench_selective_logsoftmax.py
+"""
+
+import gc
+import statistics
+
+import torch
+
+from axolotl.monkeypatch.trainer.utils import (
+    selective_log_softmax,
+    selective_log_softmax_original,
+)
+
+V = 151936  # Qwen vocab
+WARMUP = 5
+BENCH_ITERS = 20
+MEM_ITERS = 10
+
+
+def _clean_gpu():
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_peak_memory_stats()
+    torch.cuda.reset_accumulated_memory_stats()
+    torch.cuda.synchronize()
+
+
+def profile_time(fn, args, n_iters=BENCH_ITERS):
+    for _ in range(WARMUP):
+        fn(*args)
+    torch.cuda.synchronize()
+
+    times = []
+    for _ in range(n_iters):
+        s = torch.cuda.Event(enable_timing=True)
+        e = torch.cuda.Event(enable_timing=True)
+        s.record()
+        fn(*args)
+        e.record()
+        torch.cuda.synchronize()
+        times.append(s.elapsed_time(e))
+    return times
+
+
+def profile_memory(fn, args, n_iters=MEM_ITERS):
+    for _ in range(WARMUP):
+        out = fn(*args)
+        del out
+    torch.cuda.synchronize()
+
+    peaks = []
+    for _ in range(n_iters):
+        _clean_gpu()
+        base = torch.cuda.max_memory_allocated()
+        out = fn(*args)
+        torch.cuda.synchronize()
+        peaks.append(torch.cuda.max_memory_allocated() - base)
+        del out
+    return [p / 1e6 for p in peaks]
+
+
+def fmt(values, unit=""):
+    mean = statistics.mean(values)
+    std = statistics.stdev(values) if len(values) > 1 else 0.0
+    return f"{mean:8.2f} ± {std:5.2f} {unit}  [min={min(values):.2f}, max={max(values):.2f}]"
+
+
+def benchmark_forward():
+    print("=" * 60)
+    print(f"FORWARD BENCHMARK  (warmup={WARMUP}, time={BENCH_ITERS}, mem={MEM_ITERS})")
+    print("=" * 60)
+
+    configs = [
+        (1, 2048),
+        (1, 8192),
+        (4, 4096),
+        (8, 2048),
+        (16, 2048),
+        (16, 4096),
+    ]
+
+    for B, L in configs:
+        mem_gb = B * L * V * 2 / 1e9
+        if mem_gb > 28:
+            print(f"\n  skip B={B}, L={L} ({mem_gb:.1f} GB)")
+            continue
+
+        N = B * L
+        print(f"\n{'─' * 60}")
+        print(f"B={B:2d}, L={L:5d}  ({N:6d} rows, logits {mem_gb:.2f} GB)")
+        print(f"{'─' * 60}")
+
+        torch.manual_seed(42)
+        logits = torch.randn(B, L, V, device="cuda", dtype=torch.bfloat16)
+        index = torch.randint(0, V, (B, L), device="cuda")
+
+        t_orig = profile_time(selective_log_softmax_original, (logits, index))
+        t_triton = profile_time(selective_log_softmax, (logits, index))
+        orig_mean = statistics.mean(t_orig)
+        triton_mean = statistics.mean(t_triton)
+
+        print("  TIME (ms):")
+        print(f"    original: {fmt(t_orig, 'ms')}")
+        print(f"    triton:   {fmt(t_triton, 'ms')}")
+        print(f"    speedup:  {orig_mean / triton_mean:.2f}x")
+
+        m_orig = profile_memory(selective_log_softmax_original, (logits, index))
+        m_triton = profile_memory(selective_log_softmax, (logits, index))
+        orig_peak = statistics.mean(m_orig)
+        triton_peak = statistics.mean(m_triton)
+
+        print("  MEMORY (peak overhead):")
+        print(f"    original: {fmt(m_orig, 'MB')}")
+        print(f"    triton:   {fmt(m_triton, 'MB')}")
+        print(f"    saved:    {orig_peak - triton_peak:.1f} MB")
+
+        del logits, index
+        _clean_gpu()
+
+
+def benchmark_backward():
+    print("\n" + "=" * 60)
+    print(f"FWD+BWD BENCHMARK  (warmup={WARMUP}, time={BENCH_ITERS}, mem={MEM_ITERS})")
+    print("=" * 60)
+
+    configs = [
+        (1, 2048),
+        (1, 8192),
+        (4, 4096),
+        (8, 2048),
+        (16, 2048),
+        (16, 4096),
+    ]
+
+    def fwd_bwd_original(logits, index):
+        logits.grad = None
+        out = selective_log_softmax_original(logits, index)
+        out.sum().backward()
+
+    def fwd_bwd_triton(logits, index):
+        logits.grad = None
+        out = selective_log_softmax(logits, index)
+        out.sum().backward()
+
+    for B, L in configs:
+        mem_gb = B * L * V * 2 / 1e9
+        if mem_gb > 20:
+            print(f"\n  skip B={B}, L={L} ({mem_gb:.1f} GB, need room for grads)")
+            continue
+
+        N = B * L
+        print(f"\n{'─' * 60}")
+        print(f"B={B:2d}, L={L:5d}  ({N:6d} rows, logits {mem_gb:.2f} GB)")
+        print(f"{'─' * 60}")
+
+        torch.manual_seed(42)
+        logits_orig = torch.randn(
+            B, L, V, device="cuda", dtype=torch.bfloat16, requires_grad=True
+        )
+        logits_tri = logits_orig.detach().clone().requires_grad_(True)
+        index = torch.randint(0, V, (B, L), device="cuda")
+
+        t_orig = profile_time(fwd_bwd_original, (logits_orig, index))
+        t_triton = profile_time(fwd_bwd_triton, (logits_tri, index))
+        orig_mean = statistics.mean(t_orig)
+        triton_mean = statistics.mean(t_triton)
+
+        print("  FWD+BWD TIME (ms):")
+        print(f"    original: {fmt(t_orig, 'ms')}")
+        print(f"    triton:   {fmt(t_triton, 'ms')}")
+        print(f"    speedup:  {orig_mean / triton_mean:.2f}x")
+
+        m_orig = profile_memory(fwd_bwd_original, (logits_orig, index))
+        m_triton = profile_memory(fwd_bwd_triton, (logits_tri, index))
+        orig_peak = statistics.mean(m_orig)
+        triton_peak = statistics.mean(m_triton)
+
+        print("  FWD+BWD MEMORY (peak overhead):")
+        print(f"    original: {fmt(m_orig, 'MB')}")
+        print(f"    triton:   {fmt(m_triton, 'MB')}")
+        print(f"    saved:    {orig_peak - triton_peak:.1f} MB")
+
+        del logits_orig, logits_tri, index
+        _clean_gpu()
+
+
+if __name__ == "__main__":
+    benchmark_forward()
+    benchmark_backward()

cicd/Dockerfile-uv.jinja

@@ -31,8 +31,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN uv pip install packaging==26.0 setuptools==75.8.0
+RUN uv pip install packaging==26.0 setuptools==78.1.1
 RUN uv pip install torchvision
+RUN uv pip uninstall causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/Dockerfile.jinja

@@ -32,7 +32,8 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN pip install packaging==26.0 setuptools==75.8.0 psutil
+RUN pip install packaging==26.0 setuptools==78.1.1 psutil
+RUN pip uninstall -y causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/cicd.sh

@@ -3,6 +3,12 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
+# curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/"  --use-compress-program unzstd --strip-components=1
+hf download "NousResearch/Meta-Llama-3-8B"
+hf download "NousResearch/Meta-Llama-3-8B-Instruct"
+hf download "microsoft/Phi-4-reasoning"
+hf download "microsoft/Phi-3.5-mini-instruct"
+
 # Run unit tests with initial coverage report
 pytest -v --durations=10 -n8 \
   --ignore=tests/e2e/ \

docker/Dockerfile

@@ -22,6 +22,7 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
+RUN pip uninstall -y causal_conv1d
 RUN if [ "$TARGETARCH" = "arm64" ]; then \
         BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
     else \

docker/Dockerfile-base

@@ -59,34 +59,18 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN case "$PYTORCH_VERSION" in \
-        2.9.[0-9]*) \
-            if [ "$CUDA" = "128" ]; then \
-                if [ "$TARGETARCH" = "amd64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl"; \
-                    WHL_VERSION="v0.5.4"; \
-                elif [ "$TARGETARCH" = "arm64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl"; \
-                    WHL_VERSION="v0.6.4"; \
-                else \
-                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-                fi; \
-                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
-                pip3 install --no-cache-dir ${WHL_FILE}; \
-                rm ${WHL_FILE}; \
-            elif [ "$CUDA" = "130" ]; then \
-                if [ "$TARGETARCH" = "amd64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl"; \
-                    WHL_VERSION="v0.5.4"; \
-                elif [ "$TARGETARCH" = "arm64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl"; \
-                    WHL_VERSION="v0.6.4"; \
-                else \
-                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-                fi; \
-                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
-                pip3 install --no-cache-dir ${WHL_FILE}; \
-                rm ${WHL_FILE}; \
-            fi \
-            ;; \
-    esac
+# Map Python version (e.g., 3.12 -> cp312)
+RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
+    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
+    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
+    # Map architecture
+    case "$TARGETARCH" in \
+        amd64) ARCH_TAG="x86_64" ;; \
+        arm64) ARCH_TAG="aarch64" ;; \
+        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
+    esac && \
+    WHL_VERSION="v0.7.16" && \
+    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
+    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
+    pip3 install --no-cache-dir "${WHL_FILE}" && \
+    rm "${WHL_FILE}"

docker/Dockerfile-cloud-uv

@@ -0,0 +1,30 @@
+ARG BASE_TAG=main
+FROM axolotlai/axolotl-uv:$BASE_TAG
+
+ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
+ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV HF_HOME="/workspace/data/huggingface-cache/hub"
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
+
+EXPOSE 8888
+EXPOSE 22
+
+COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
+COPY scripts/motd /etc/motd
+
+RUN uv pip install jupyterlab notebook ipywidgets && \
+    jupyter lab clean
+RUN apt update && \
+    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
+    rm -rf /var/cache/apt/archives && \
+    rm -rf /var/lib/apt/lists/* && \
+    mkdir -p ~/.ssh && \
+    chmod 700 ~/.ssh && \
+    printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
+    printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
+    chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
+    chmod +x /root/cloud-entrypoint.sh && \
+    echo 'set-option -g history-limit 5000' >> ~/.tmux.conf
+
+ENTRYPOINT ["/root/cloud-entrypoint.sh"]
+CMD ["sleep", "infinity"]

docker/Dockerfile-uv

@@ -0,0 +1,48 @@
+ARG BASE_TAG=main-base
+FROM axolotlai/axolotl-base-uv:$BASE_TAG
+
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ARG AXOLOTL_EXTRAS=""
+ARG AXOLOTL_ARGS=""
+ARG CUDA="118"
+ARG PYTORCH_VERSION="2.1.2"
+ARG TARGETARCH
+
+ENV PYTORCH_VERSION=$PYTORCH_VERSION
+
+RUN apt-get update && \
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs && \
+    rm -rf /var/cache/apt/archives && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /workspace
+
+RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
+
+WORKDIR /workspace/axolotl
+
+# If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
+RUN uv pip uninstall causal_conv1d
+RUN if [ "$TARGETARCH" = "arm64" ]; then \
+        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+    else \
+        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+    fi && \
+    if [ "$AXOLOTL_EXTRAS" != "" ]; then \
+        uv pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+    else \
+        uv pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
+    fi && \
+    python scripts/unsloth_install.py --uv | sh && \
+    python scripts/cutcrossentropy_install.py --uv | sh && \
+    uv pip install pytest && \
+    uv cache clean
+
+# fix so that git fetch/pull from remote works with shallow clone
+RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+    git config --get remote.origin.fetch && \
+    git config --global credential.helper store
+
+COPY .axolotl-complete.bash /root/.axolotl-complete.bash
+RUN chmod +x /root/.axolotl-complete.bash && \
+    echo 'source /root/.axolotl-complete.bash' >> ~/.bashrc

docker/Dockerfile-uv-base

@@ -6,6 +6,7 @@ ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
+ARG TARGETARCH
 ARG PYTHON_VERSION="3.11"
 ARG PYTORCH_VERSION="2.6.0"
 ARG CUDA="126"
@@ -39,28 +40,18 @@ RUN if [ "$TARGETARCH" = "amd64" ]; then \
         uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
     fi
 
-RUN case "$PYTORCH_VERSION" in \
-        2.9.[0-9]*) \
-            if [ "$TARGETARCH" = "amd64" ]; then \
-                if [ "$CUDA" = "128" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                elif [ "$CUDA" = "130" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                fi \
-            elif [ "$TARGETARCH" = "arm64" ]; then \
-                if [ "$CUDA" = "128" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                elif [ "$CUDA" = "130" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                fi \
-            fi \
-            ;; \
-    esac
+# Map Python version (e.g., 3.12 -> cp312)
+RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
+    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
+    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
+    # Map architecture
+    case "$TARGETARCH" in \
+        amd64) ARCH_TAG="x86_64" ;; \
+        arm64) ARCH_TAG="aarch64" ;; \
+        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
+    esac && \
+    WHL_VERSION="v0.7.16" && \
+    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
+    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
+    uv pip install --no-cache-dir "${WHL_FILE}" && \
+    rm "${WHL_FILE}"

docs/attention.qmd

@@ -0,0 +1,178 @@
+---
+title: Attention
+description: Supported attention modules in Axolotl
+---
+
+## SDP Attention
+
+This is the default built-in attention in PyTorch.
+
+```yaml
+sdp_attention: true
+```
+
+For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
+
+## Flash Attention
+
+Axolotl supports Flash Attention 2, 3, and 4. The best available version is used automatically
+based on your installed packages and GPU.
+
+```yaml
+flash_attention: true
+```
+
+For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
+
+### Flash Attention 2
+
+Requirements: Ampere, Ada, or Hopper GPUs (Turing or lower not supported)
+
+```bash
+pip install flash-attn --no-build-isolation
+```
+
+::: {.callout-tip}
+
+If you get `undefined symbol` while training, ensure you installed PyTorch prior to Axolotl.
+Alternatively, try reinstall or downgrade a version.
+
+:::
+
+### Flash Attention 3
+
+Requirements: Hopper only and CUDA 12.8 (recommended)
+
+```bash
+git clone https://github.com/Dao-AILab/flash-attention.git
+cd flash-attention/hopper
+
+python setup.py install
+```
+
+### Flash Attention 4
+
+Requirements: Hopper or Blackwell GPUs
+
+```bash
+pip install flash-attn-4
+```
+
+Or from source:
+
+```bash
+git clone https://github.com/Dao-AILab/flash-attention.git
+cd flash-attention/flash_attn/cute
+
+pip install -e .
+
+# FA2's flash_attn package includes a cute/ stub that shadows FA4.
+# Remove it so Python can find the real FA4 module:
+rm -r $(python -c "import flash_attn; print(flash_attn.__path__[0])")/cute
+```
+
+::: {.callout-note}
+
+**Hopper (SM90) users**: The backward kernel is not yet included in the pip package. To use FA4
+for training on Hopper, install from source using the instructions above.
+
+:::
+
+::: {.callout-warning}
+
+FA4 only supports head dimensions up to 128 (`d ≤ 128`). The DeepSeek shape `(192, 128)` is
+also supported but only on Blackwell. Axolotl automatically detects incompatible head dimensions
+and falls back to FA2/3.
+
+:::
+
+For more details: [flash-attention/flash_attn/cute](https://github.com/Dao-AILab/flash-attention/tree/main/flash_attn/cute)
+
+### AMD
+
+Requirements: ROCm 6.0 and above.
+
+See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
+
+## Flex Attention
+
+A flexible PyTorch API for attention used in combination with `torch.compile`.
+
+```yaml
+flex_attention: true
+
+# recommended
+torch_compile: true
+```
+
+::: {.callout-note}
+
+We recommend using latest stable version of PyTorch for best performance.
+
+:::
+
+For more details: [PyTorch docs](https://pytorch.org/blog/flexattention/)
+
+## SageAttention
+
+Attention kernels with QK Int8 and PV FP16 accumulator.
+
+```yaml
+sage_attention: true
+```
+
+Requirements: Ampere, Ada, or Hopper GPUs
+
+```bash
+pip install sageattention==2.2.0 --no-build-isolation
+```
+
+::: {.callout-warning}
+
+Only LoRA/QLoRA recommended at the moment. We found loss drop to 0 for full finetuning. See [GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
+
+:::
+
+For more details: [Sage Attention](https://github.com/thu-ml/SageAttention)
+
+::: {.callout-note}
+
+We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
+
+:::
+
+
+## xFormers
+
+```yaml
+xformers_attention: true
+```
+
+::: {.callout-tip}
+
+We recommend using with Turing GPUs or below (such as on Colab).
+
+:::
+
+For more details: [xFormers](https://github.com/facebookresearch/xformers)
+
+## Shifted Sparse Attention
+
+::: {.callout-warning}
+
+We plan to deprecate this! If you use this feature, we recommend switching to methods above.
+
+:::
+
+Requirements: LLaMA model architecture
+
+```yaml
+flash_attention: true
+s2_attention: true
+```
+
+::: {.callout-tip}
+
+No sample packing support!
+
+:::

docs/cli.qmd

@@ -210,6 +210,8 @@ axolotl lm-eval config.yml
 Configuration options:
 
 ```yaml
+lm_eval_model: # model to evaluate (local or hf path)
+
 # List of tasks to evaluate
 lm_eval_tasks:
   - arc_challenge
@@ -218,7 +220,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
+See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
 
 ### delinearize-llama4
 

docs/expert_quantization.qmd

@@ -0,0 +1,67 @@
+---
+title: "MoE Expert Quantization"
+description: "Reduce VRAM usage when training MoE model adapters by quantizing expert weights on load"
+---
+
+Transformers v5 changed MoE expert layers from `nn.Linear` to fused `nn.Parameter` (3D+ tensors).
+This means `bitsandbytes` can no longer quantize them during model loading, resulting in all expert
+weights being loaded in full bf16 precision and causing massive VRAM usage.
+
+`quantize_moe_experts` solves this by quantizing expert weights during model loading.
+It intercepts the weight loading process, quantizes each expert tensor on the fly, and
+immediately frees the original bf16 tensor from VRAM. This dramatically reduces peak memory.
+For example, GLM-4.7-Flash QLoRA drops from ~127GiB to ~23GiB reserved memory.
+
+## Usage
+
+Enable expert quantization in your Axolotl config:
+
+```yaml
+quantize_moe_experts: true
+```
+
+This works with both 4-bit (QLoRA) and 8-bit (LoRA) quantization.
+
+### Expert LoRA targeting
+
+You can optionally apply LoRA adapters directly to expert weights using `lora_target_parameters`:
+
+```yaml
+lora_target_parameters:
+  - mlp.experts.gate_up_proj
+  - mlp.experts.down_proj
+  # - mlp.gate.weight  # router
+```
+
+::: {.callout-note}
+`lora_dropout` must be `0` when using `lora_target_parameters`.
+:::
+
+## Requirements
+
+- Requires (`adapter: lora` and `load_in_8bit: true`) or (`adapter: qlora` and `load_in_4bit: true`)
+- CUDA GPUs only (not tested with ROCm or other backends)
+- FSDP2 compatible for distributed training
+
+## Limitations
+
+- `lora_target_linear` is not compatible with `quantize_moe_experts`. See [Expert LoRA targeting](#expert-lora-targeting) instead.
+- `cpu_ram_efficient_loading` hangs / takes long time with FSDP2 + QLoRA.
+- Total model parameter count may display incorrectly (trainable param count is correct).
+- FSDP LoRA (8-bit) may have a large initial VRAM spike at the first 1-2 steps, which then drops. QLoRA does not exhibit this.
+- FSDP2 may use more VRAM per GPU than single GPU training due to not all layers being properly sharded across ranks.
+- Model loading takes longer due to on-demand quantization, even on consecutive runs.
+- DeepSpeed has not been tested.
+
+## Implementation details
+
+The quantization is applied by patching transformers to intercept weight loading.
+When a 3D+ CUDA tensor with "expert" in its name is detected:
+
+- **4-bit mode:** Uses bitsandbytes NF4 parametrization (configurable via `bnb_4bit_quant_type`).
+- **8-bit mode:** Uses a custom row-wise int8 parametrization with bitsandbytes dequantization.
+
+The original bf16 tensor is freed immediately after quantization. Multiple sub-patches are applied to
+transformers, PEFT and accelerate FSDP2 to support these parametrized expert modules.
+
+For full implementation details, see [PR #3439](https://github.com/axolotl-ai-cloud/axolotl/pull/3439).

docs/lora_optims.qmd

@@ -89,6 +89,10 @@ lora_o_kernel: true
 Currently, LoRA kernels are not supported for RLHF training, only SFT.
 :::
 
+::: {.callout-warning}
+LoRA kernels do not support remote modeling code.
+:::
+
 ## Requirements
 
 - One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)

docs/multimodal.qmd

@@ -13,12 +13,14 @@ format:
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
+- [Mistral-Small-4](#sec-mistral-small-4)
 - [Magistral-Small-2509](#sec-magistral-small-2509)
 - [Voxtral](#sec-voxtral)
 - [Gemma-3](#sec-gemma-3)
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
+- [GLM-4.6V](#sec-glm-4-6v)
 - [SmolVLM2](#sec-smolvlm2)
 - [LFM2-VL](#sec-lfm2-vl)
 - [Intern-VL](#sec-intern-vl)
@@ -107,6 +109,12 @@ Please make sure to install vision lib via `pip install 'mistral-common[opencv]=
 base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 ```
 
+### Mistral-Small-4 {#sec-mistral-small-4}
+
+```yaml
+base_model: mistralai/Mistral-Small-4-119B-2603
+```
+
 ### Magistral-Small-2509 {#sec-magistral-small-2509}
 
 ::: {.callout-tip}
@@ -183,6 +191,18 @@ base_model: Qwen/Qwen3-VL-4B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### GLM-4.6V {#sec-glm-4-6v}
+
+Both GLM-4.6V (106B MoE) and GLM-4.6V-Flash (9B) are supported.
+
+```yaml
+# GLM-4.6V (106B MoE version)
+base_model: zai-org/GLM-4.6V
+
+# OR GLM-4.6V-Flash (9B version)
+base_model: zai-org/GLM-4.6V-Flash
+```
+
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}

docs/optimizations.qmd

@@ -66,6 +66,15 @@ Provides efficient Triton kernels to improve training speed and reduce memory us
 
 - **Learn more:** [Custom Integrations - Liger Kernels](custom_integrations.qmd#liger-kernels)
 
+### Expert Kernels
+
+Optimized kernel implementations for Mixture of Experts (MoE) model training.
+
+- **ScatterMoE**: Triton-based MoE kernels with fused LoRA support.
+- **SonicMoE**: CUTLASS-based MoE kernels for NVIDIA Hopper and Blackwell GPUs.
+
+- **Learn more:** [Custom Integrations - Kernels Integration](custom_integrations.qmd#kernels-integration)
+
 ## Long Context Models
 
 Techniques to train models on sequences longer than their original context window.
@@ -131,3 +140,10 @@ Simulates quantization effects during training, helping the model adapt and pote
 Allows you to finetune LoRA adapters on top of a model that has already been quantized using the GPTQ method.
 
 - **Example:** [GPTQ LoRA Example](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-2/gptq-lora.yml)
+
+### MoE Expert Quantization
+
+Quantizes MoE expert weights on load to reduce VRAM when training MoE models with adapters. Required for Transformers v5+ MoE models where experts use fused `nn.Parameter` tensors.
+
+- **Config:** `quantize_moe_experts: true`
+- **Learn more:** [MoE Expert Quantization](expert_quantization.qmd)

docs/rlhf.qmd

@@ -721,6 +721,213 @@ trl:
 
 For more information, see [GRPO docs](https://huggingface.co/docs/trl/v0.17.0/en/grpo_trainer#loss-types).
 
+#### Async GRPO
+
+Async GRPO overlaps vLLM generation with training by producing rollouts in a background thread. While the model trains on the current batch, the next batch is already being generated. This can significantly reduce wall-clock time per step.
+
+```yaml
+trl:
+  use_data_producer: true     # Enable data producer protocol
+  use_vllm: true
+  async_prefetch: true         # Generate rollouts in background thread
+  prefetch_depth: 1            # Number of rollouts to prefetch
+  vllm_sync_interval: 2        # Sync weights to vLLM every N steps
+```
+
+::: {.callout-note}
+Because the background thread generates completions with slightly stale model weights, async GRPO uses importance sampling correction to account for the distribution shift. This is controlled by `vllm_importance_sampling_correction: true` (default when async is enabled).
+:::
+
+##### vLLM LoRA Sync
+
+By default, weight sync to vLLM merges the LoRA adapter into the base model and broadcasts all parameters via NCCL. LoRA sync is a faster alternative that saves only the adapter weights to the filesystem and has vLLM load them natively using Punica kernels.
+
+```yaml
+adapter: lora
+lora_r: 32
+lora_alpha: 64
+lora_target_linear: true
+
+trl:
+  vllm_lora_sync: true         # Enable native LoRA sync
+```
+
+When `vllm_lora_sync: true` is set, axolotl automatically selects the LoRA-aware vLLM serve module. Start vLLM as usual:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml
+```
+
+Then start training on a separate GPU:
+
+```bash
+CUDA_VISIBLE_DEVICES=1 axolotl train config.yaml
+```
+
+::: {.callout-tip}
+LoRA sync is especially beneficial with multi-GPU training (FSDP/DeepSpeed), where NCCL merge-sync can cause GPU contention with vLLM generation.
+:::
+
+##### Streaming Partial Batch
+
+Instead of scoring the entire batch at once, streaming mode scores one prompt group at a time. This enables finer-grained zero-advantage skipping and reduces peak memory usage during scoring.
+
+```yaml
+trl:
+  streaming_partial_batch: true
+```
+
+##### Importance Sampling Correction
+
+When using async prefetch, completions are generated from a slightly older version of the model. Importance sampling (IS) correction adjusts the policy gradient to account for this distribution shift.
+
+```yaml
+trl:
+  vllm_importance_sampling_correction: true   # Enable IS correction
+  importance_sampling_level: token             # 'token' or 'sequence'
+  off_policy_mask_threshold: 0.5              # Mask sequences with IS ratio below this
+```
+
+- `importance_sampling_level: token` applies per-token IS ratios (recommended with Liger kernel)
+- `importance_sampling_level: sequence` applies per-sequence IS ratios
+- `off_policy_mask_threshold` masks out sequences where the IS ratio indicates they are too far off-policy
+
+##### Replay Buffer
+
+The replay buffer caches rollout groups that had learning signal (non-zero reward variance) and uses them to replace zero-signal groups in later batches.
+
+```yaml
+trl:
+  replay_buffer_size: 100       # Max cached groups (0 = disabled)
+  replay_recompute_logps: true  # Recompute log-probs for replayed data (recommended)
+```
+
+::: {.callout-note}
+When `replay_recompute_logps: true` (default), old log-probabilities are recomputed using the current model weights. This fixes the IS mismatch that would otherwise occur when replaying stale data.
+:::
+
+##### Deferred Re-rolling
+
+Failed prompts (where the model produces zero reward for all generations) are buffered and re-injected into later batches when the model may be better equipped to solve them.
+
+```yaml
+trl:
+  reroll_start_fraction: 0.5    # Start re-rolling after 50% of training
+  reroll_max_groups: 1          # Max groups to replace per batch
+```
+
+##### Zero-Advantage Batch Skipping
+
+When all advantages in a micro-batch are zero (no learning signal), the forward/backward pass is skipped entirely. This is enabled by default and logged as `skipped_zero_adv_batches=1`.
+
+```yaml
+trl:
+  skip_zero_advantage_batches: true   # default
+```
+
+##### Parallel Reward Workers
+
+Reward functions that use `signal.alarm()` (e.g., `math_verify`) must run in the main thread. Parallel reward workers use subprocesses to work around this limitation while enabling concurrent reward computation.
+
+```yaml
+trl:
+  reward_num_workers: 4         # Number of subprocess workers (1 = no parallelism)
+```
+
+##### Full Async GRPO Example
+
+```yaml
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+
+vllm:
+    host: 0.0.0.0
+    port: 8000
+    gpu_memory_utilization: 0.35
+    dtype: auto
+
+adapter: lora
+lora_r: 32
+lora_alpha: 64
+lora_target_linear: true
+
+rl: grpo
+trl:
+  use_data_producer: true
+  use_vllm: true
+  async_prefetch: true
+  prefetch_depth: 1
+  vllm_sync_interval: 2
+  vllm_lora_sync: true
+  streaming_partial_batch: true
+  vllm_importance_sampling_correction: true
+  off_policy_mask_threshold: 0.5
+  importance_sampling_level: token
+  num_generations: 8
+  max_completion_length: 512
+  reward_funcs:
+    - rewards.accuracy_reward
+  reroll_start_fraction: 0.5
+  replay_buffer_size: 100
+  reward_num_workers: 4
+  skip_zero_advantage_batches: true
+
+datasets:
+  - path: AI-MO/NuminaMath-TIR
+    type: rewards.prompt_transform
+    split: train
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+max_steps: 500
+learning_rate: 1e-5
+bf16: true
+gradient_checkpointing: true
+```
+
+```bash
+# Terminal 1: Start vLLM on GPU 0
+CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml
+
+# Terminal 2: Train on GPU 1
+CUDA_VISIBLE_DEVICES=1 axolotl train config.yaml
+```
+
+##### Multi-GPU Async GRPO
+
+Async GRPO supports FSDP and DeepSpeed ZeRO-3 for multi-GPU training. vLLM runs on one GPU while training is distributed across the remaining GPUs.
+
+**FSDP:**
+
+```yaml
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+```
+
+**DeepSpeed ZeRO-3:**
+
+```yaml
+deepspeed: deepspeed_configs/zero3_bf16.json
+gradient_checkpointing_kwargs:
+  use_reentrant: true   # Required for ZeRO-3
+```
+
+```bash
+# Terminal 1: Start vLLM on GPU 0
+CUDA_VISIBLE_DEVICES=0 axolotl vllm-serve config.yaml
+
+# Terminal 2: Train on GPUs 0,1
+CUDA_VISIBLE_DEVICES=0,1 accelerate launch --num_processes 2 -m axolotl.cli.train config.yaml
+```
+
+::: {.callout-important}
+With multi-GPU async prefetch, only rank 0 generates completions in the background thread. Results are broadcast to all ranks on the main thread. This avoids FSDP/DeepSpeed collective deadlocks from unsynchronized background threads.
+:::
+
 ### GDPO
 
 GDPO (Group Reward-Decoupled Policy Optimization) extends GRPO for multi-reward training. It addresses the **reward advantage collapse** problem by normalizing each reward function independently before combining them.

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
     "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f4b5712\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe\""
    ]
   },
   {

examples/glm45/README.md

@@ -0,0 +1,72 @@
+# Finetune Z.ai's GLM-4.5-Air with Axolotl
+
+[GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air) is a MoE model by Z.ai.
+
+This guide shows how to fine-tune it with Axolotl.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. Run the finetuning example:
+
+```bash
+# QLoRA (1x80GB @ ~63.4GiB/GPU)
+axolotl train examples/glm45/glm-45-air-qlora.yaml
+```
+
+### Dataset
+
+In addition to the standard OpenAI Messages format, GLM-4.5 supports an extra parameter for thinking in the assistant section.
+
+```json
+{
+    "role": "assistant",
+    "reasoning_content": "...",  // or have </think>...</think> in `content`
+    "content": "..."
+}
+```
+
+Make sure you set the below extra attributes if needed:
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    message_property_mappings:
+      role: role
+      content: content
+
+    #   tool_calls: tool_calls  # uncomment if using tools
+    #   reasoning_content: reasoning_content  # uncomment if have reasoning
+
+# Uncomment if training on tool role (you would rarely if ever need this)
+# eot_tokens:
+#   - <|observation|>
+```
+
+### Tips
+
+- The role name for tools in this template is `tool`.
+- You will see this Axolotl WARNING — this is expected as the template does not use EOS:
+  ```
+  EOS token '<|endoftext|>' not found in chat_template. Please check if your template/EOS token is correct.
+  ```
+- You can run a full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config.
+- **LoRA kernels**: Incompatible with this model. Must be explicitly disabled (`lora_*_kernel: false`).
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [GLM-4.5-Air on HuggingFace](https://huggingface.co/zai-org/GLM-4.5-Air)
+- [GLM-4.5 Blog](https://z.ai/blog/glm-4.5)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/glm45/glm-45-air-qlora.yaml

@@ -0,0 +1,64 @@
+base_model: zai-org/GLM-4.5-Air
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+quantize_moe_experts: true # important
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 16
+lora_alpha: 8
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/glm46v/README.md

@@ -0,0 +1,44 @@
+# Finetune GLM-4.6V with Axolotl
+
+GLM-4.6V is a family of vision-language models from ZhipuAI found on [HuggingFace](https://huggingface.co/zai-org/GLM-4.6V). This guide shows how to fine-tune it with Axolotl for vision-language tasks.
+
+
+
+## Getting started
+
+1. Install Axolotl from source following the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+
+3. Run the fine-tuning:
+
+    glm-4-6v-flash(9B)
+    ```bash
+    axolotl train examples/glm46v/glm-4-6v-flash-qlora.yaml
+    ```
+
+Let us know how it goes. Happy finetuning! 🚀
+
+## Tips
+
+- Vision datasets should follow the format described in the [multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format)
+- You can run a **full finetuning** by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
+- Read more on how to load your own dataset in the [dataset loading docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Supported Models
+
+- **GLM-4.6V**: Full vision-language model (`zai-org/GLM-4.6V`)
+- **GLM-4.6V-Flash**: Faster variant (`zai-org/GLM-4.6V-Flash`)
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [ZhipuAI GLM-4.6V](https://huggingface.co/zai-org/GLM-4.6V)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/glm46v/glm-4-6v-flash-ddp.yaml

@@ -0,0 +1,53 @@
+base_model: zai-org/GLM-4.6V-Flash
+trust_remote_code: true
+
+processor_type: AutoProcessor
+load_in_4bit: true
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+ddp_find_unused_parameters: true
+
+output_dir: ./outputs/glm-4-6v-flash-qlora
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.05
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+sequence_len: 2048
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+logging_steps: 1
+sdp_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 0
+saves_per_epoch: 1
+weight_decay: 0.0

examples/glm46v/glm-4-6v-flash-qlora.yaml

@@ -0,0 +1,50 @@
+base_model: zai-org/GLM-4.6V-Flash
+trust_remote_code: true
+
+processor_type: AutoProcessor
+load_in_4bit: true
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+output_dir: ./outputs/glm-4-6v-flash-qlora
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.05
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+sequence_len: 2048
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+logging_steps: 1
+sdp_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 0
+saves_per_epoch: 1
+weight_decay: 0.0

examples/glm47-flash/README.md

@@ -0,0 +1,65 @@
+# Finetune Z.ai's GLM-4.7-Flash with Axolotl
+
+[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model by Z.ai.
+
+This guide shows how to fine-tune it with Axolotl.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. Run the finetuning example:
+
+```bash
+# QLoRA
+# - no target experts (1x48GB @ ~24GiB/GPU)
+# - target experts (1x48GB @ ~34GiB/GPU)
+axolotl train examples/glm47-flash/qlora.yaml
+
+# QLoRA FSDP2 no target experts (2x48GB @ ~29GiB/GPU)
+axolotl train examples/glm47-flash/qlora_fsdp.yaml
+```
+
+```bash
+# LoRA
+# - no target experts (1x48GB @ ~35GiB/GPU)
+# - target experts (1x48GB @ OOM. Projected ~45-50GiB/GPU)
+axolotl train examples/glm47-flash/lora.yaml
+
+# LoRA FSDP2 no target experts (2x48GB @ ~43GiB/GPU)
+axolotl train examples/glm47-flash/lora_fsdp.yaml
+```
+
+### MoE Expert Quantization & Expert LoRA
+
+This model quantize expert weights on load. To learn about expert quantization, expert LoRA targeting, and related limitations, see the [MoE Expert Quantization](https://docs.axolotl.ai/docs/expert_quantization.html) docs.
+
+## Limitations
+
+- **lora_target_linear**: Incompatible for this model.
+- **LoRA kernels**: Incompatible with this model due to non-standard attention projections (DSA). Must be explicitly disabled (`lora_*_kernel: false`).
+
+
+### TIPS
+
+- For inference, the official Z.ai team recommends these default settings (most tasks):
+  - `temperature: 1.0`
+  - `top_p: 0.95`
+  - `max_new_tokens: 131072`
+- You can run a full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config. This is heavy, so we have not tested this.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
+- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/glm47-flash/lora.yaml

@@ -0,0 +1,65 @@
+base_model: zai-org/GLM-4.7-Flash
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: true
+quantize_moe_experts: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/glm4.7-flash-lora-8bit-out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+# Uncomment to also target MoE expert weights:
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+# LoRA kernels incompatible with DSA attention
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1

examples/glm47-flash/lora_fsdp.yaml

@@ -0,0 +1,75 @@
+base_model: zai-org/GLM-4.7-Flash
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: true
+quantize_moe_experts: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/glm4.7-flash-lora-8bit-fsdp-out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+# Uncomment to also target MoE expert weights:
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+# LoRA kernels incompatible with DSA attention
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+fsdp_config:
+  fsdp_version: 2
+  offload_params: false
+  cpu_ram_efficient_loading: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Glm4MoeLiteDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/glm47-flash/qlora.yaml

@@ -0,0 +1,65 @@
+base_model: zai-org/GLM-4.7-Flash
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_4bit: true
+quantize_moe_experts: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/glm4.7-flash-qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+# Uncomment to also target MoE expert weights:
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+# LoRA kernels incompatible with DSA attention
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1

examples/glm47-flash/qlora_fsdp.yaml

@@ -0,0 +1,75 @@
+base_model: zai-org/GLM-4.7-Flash
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_4bit: true
+quantize_moe_experts: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/glm4.7-flash-qlora-fsdp-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+# Uncomment to also target MoE expert weights:
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+# LoRA kernels incompatible with DSA attention
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+fsdp_config:
+  fsdp_version: 2
+  offload_params: false
+  cpu_ram_efficient_loading: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Glm4MoeLiteDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/llama-3/3b-qat-mxfp4.yaml

@@ -0,0 +1,65 @@
+base_model: meta-llama/Llama-3.2-3B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+
+datasets:
+  - path: yahma/alpaca-cleaned
+    type: alpaca
+    split: train[:95%]
+
+output_dir: ./outputs/qat_out/
+dataset_prepared_path: ./outputs/dataset_prepared
+
+sequence_len: 2048
+flash_attention: true
+
+qat:
+  activation_dtype: mxfp4
+  weight_dtype: mxfp4
+  group_size: 32
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_checkpointing: true
+activation_offloading: true
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_8bit
+
+cosine_constant_lr_ratio: 0
+cosine_min_lr_ratio: 1.0
+learning_rate: 2e-5
+save_only_model: true
+bf16: true
+
+resume_from_checkpoint:
+logging_steps: 1
+
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+warmup_ratio: 0.1
+weight_decay: 0.0
+
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/mistral4/README.md

@@ -0,0 +1,85 @@
+# Finetune Mistral Small 4 with Axolotl
+
+Mistral Small 4 is a 119B parameter (6.5B active) multimodal MoE model from MistralAI that unifies instruct, reasoning, and coding capabilities into a single model. It is available on HuggingFace at [Mistral-Small-4-119B-2603](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603).
+
+Thanks to the team at MistralAI for giving us early access to prepare for this release.
+
+## Getting started
+
+Note: Training this model requires weights in BF16 which we will link to later.
+Users interested in training can convert / descale the existing FP8 weights.
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
+
+3. Install transformers from main
+
+  ```bash
+  pip install git+https://github.com/huggingface/transformers.git
+  ```
+
+4. Run one of the example configs:
+
+  ```bash
+  # text-only
+  axolotl train examples/mistral4/qlora-text.yml  # no experts ~69 GiB, experts ~93 GiB
+  axolotl train examples/mistral4/fft-text.yml
+
+  # text + vision
+  # run: wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+  axolotl train examples/mistral4/qlora-vision.yml  # no experts ~68 GiB
+  axolotl train examples/mistral4/fft-vision.yml
+  ```
+
+Note: FFT configs provided as reference. Please adjust hyperparameters as needed.
+
+## Reasoning Effort
+
+The chat template supports a `reasoning_effort` variable to control the model's reasoning depth:
+
+- `"none"` — instruct mode (default)
+- `"high"` — reasoning mode with explicit thinking steps
+
+Pass it via `chat_template_kwargs` under your dataset config:
+
+```yaml
+datasets:
+  - path: your/dataset
+    type: chat_template
+    chat_template_kwargs:
+      reasoning_effort: high
+```
+
+## Thinking Support
+
+The chat template supports a `thinking` content type in assistant messages for training on reasoning traces (rendered as `[THINK]...[/THINK]` blocks).
+
+To use thinking datasets, add the `thinking` mapping via `message_property_mappings`:
+
+```yaml
+datasets:
+  - path: your/thinking-dataset
+    type: chat_template
+    message_property_mappings:
+      role: role
+      content: content
+      thinking: thinking
+    chat_template_kwargs:
+      reasoning_effort: high
+```
+
+See the [Magistral thinking guide](../magistral/think/README.md) for dataset format details.
+
+## Tips
+
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+- The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+
+## Related Resources
+
+- [MistralAI Mistral Small 4 Blog](https://mistral.ai/news/mistral-small-4)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/mistral4/fft-text.yml

@@ -0,0 +1,58 @@
+base_model: mistralai/Mistral-Small-4-119B-2603
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_sonicmoe: true
+
+# only train language model layers, freeze vision tower
+unfrozen_parameters:
+  - model.language_model.*
+  - lm_head
+  - embed_tokens
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+sequence_len: 2048
+sample_packing: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: false
+  state_dict_type: FULL_STATE_DICT
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Mistral4DecoderLayer
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/mistral4/fft-vision.yml

@@ -0,0 +1,57 @@
+base_model: mistralai/Mistral-Small-4-119B-2603
+processor_type: AutoProcessor
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_sonicmoe: true
+
+# vision requirements
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: Nanobit/text-vision-2k-test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+sequence_len: 2048
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: false
+  state_dict_type: FULL_STATE_DICT
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Mistral4DecoderLayer
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/mistral4/qlora-text.yml

@@ -0,0 +1,58 @@
+base_model: mistralai/Mistral-Small-4-119B-2603
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_4bit: true
+quantize_moe_experts: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+# uncomment to train on expert layers
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+# lora_mlp_kernel: false
+# lora_qkv_kernel: false
+# lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0

examples/mistral4/qlora-vision.yml

@@ -0,0 +1,63 @@
+base_model: mistralai/Mistral-Small-4-119B-2603
+processor_type: AutoProcessor
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_4bit: true
+quantize_moe_experts: true
+
+# vision chat template requirements
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: Nanobit/text-vision-2k-test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+
+sequence_len: 2048
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+# uncomment to train on expert layers
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+# lora_mlp_kernel: false
+# lora_qkv_kernel: false
+# lora_o_kernel: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0

examples/qwen3-next/README.md

@@ -6,30 +6,13 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ## Getting started
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Qwen3-Next is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
 
-    Here is an example of how to install from main for pip:
-
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn]'
-
-# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
-python scripts/cutcrossentropy_install.py | sh
-```
-
-2. Install Qwen3-Next transformers commit
-```bash
-pip3 uninstall -y transformers && pip3 install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
-```
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
 
 3. Install FLA for improved performance
 ```bash
-pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
+pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
 ```
 
 4. Run the finetuning example:
@@ -38,7 +21,7 @@ pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
 axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
 ```
 
-This config uses about 45.62 GiB VRAM.
+This config uses about ~47 GiB (no target experts) and ~71GiB (target experts) VRAM.
 
 Let us know how it goes. Happy finetuning! 🚀
 

examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml

@@ -9,6 +9,8 @@ plugins:
 load_in_8bit: false
 load_in_4bit: true
 
+quantize_moe_experts: true
+
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
@@ -25,7 +27,7 @@ sample_packing: true
 
 lora_r: 16
 lora_alpha: 8
-lora_dropout: 0.05
+lora_dropout: 0
 lora_target_modules:
   - linear_attn.in_proj_ba
   - linear_attn.in_proj_qkvz
@@ -34,12 +36,19 @@ lora_target_modules:
   - shared_expert.down_proj
   - shared_expert.gate_proj
   - shared_expert_gate
-  - mlp.gate
   - q_proj
   - v_proj
   - k_proj
   - o_proj
 
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
 wandb_project:
 wandb_entity:
 wandb_watch:

examples/qwen3.5/122b-a10b-moe-qlora.yaml

@@ -0,0 +1,71 @@
+base_model: Qwen/Qwen3.5-122B-A10B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+quantize_moe_experts: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+
+#lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/qwen3.5/27b-qlora.yaml

@@ -0,0 +1,72 @@
+base_model: Qwen/Qwen3.5-27B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+# Note: Qwen3.5 is an early-fusion VLM (image+text). This config fine-tunes
+# the text-only path. For multimodal (image+text) fine-tuning, add image
+# columns to your dataset following axolotl's multimodal dataset format.
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - down_proj
+  - up_proj
+  # Uncomment below to also target the linear attention projections.
+  # These use separate in_proj_qkv / in_proj_z / out_proj (Qwen3.5-specific).
+  # - linear_attn.in_proj_qkv
+  # - linear_attn.in_proj_z
+  # - linear_attn.out_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/qwen3.5/35b-a3b-moe-qlora.yaml

@@ -0,0 +1,70 @@
+base_model: Qwen/Qwen3.5-35B-A3B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+quantize_moe_experts: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+
+#lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/qwen3.5/7b-lora-vision.yaml

@@ -0,0 +1,72 @@
+base_model: Qwen/Qwen3.5-7B
+processor_type: AutoProcessor
+
+# Qwen3.5-7B and above are early-fusion VLMs (Qwen3_5ForConditionalGeneration).
+# Vision and text tokens are processed together by the same transformer layers.
+# Note: Qwen3.5-2B is a text-only model — the smallest VLM is Qwen3.5-7B.
+
+# These 3 lines are required for vision/multimodal training
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: qwen3_5
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+# Targets the language model attention and MLP layers.
+# Qwen3.5 is early-fusion: all layers (including those seeing vision tokens) share
+# the same transformer stack, so standard attention targets work for both modalities.
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - down_proj
+  - up_proj
+  # Uncomment to also target the linear attention (GatedDeltaNet) projections:
+  # - linear_attn.in_proj_qkv
+  # - linear_attn.in_proj_z
+  # - linear_attn.out_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0

examples/qwen3.5/README.md

@@ -0,0 +1,61 @@
+# Finetune Qwen3.5 with Axolotl
+
+[Qwen3.5](https://huggingface.co/collections/Qwen/qwen35-68452f3bc6e4b7cfb4e1c803) is a hybrid architecture model series combining Gated DeltaNet linear attention with standard Transformer attention. Models from 7B onwards are early-fusion vision-language models (`Qwen3_5ForConditionalGeneration`), meaning vision and text tokens are processed through the same transformer stack. The 2B variant is text-only.
+
+Available configs:
+
+| Config | Model | Type |
+|---|---|---|
+| `27b-qlora.yaml` | Qwen3.5-27B | Dense VLM, text-only path |
+| `35b-a3b-moe-qlora.yaml` | Qwen3.5-35B-A3B | MoE, text-only path |
+| `122b-a10b-moe-qlora.yaml` | Qwen3.5-122B-A10B | MoE, text-only path |
+| `7b-lora-vision.yaml` | Qwen3.5-7B | Vision+text (multimodal) |
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. Install FLA for sample packing support with the Gated DeltaNet linear attention layers:
+```bash
+pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
+```
+> FLA is required when `sample_packing: true`. Without it, training raises a `RuntimeError` on packed sequences. Vision configs use `sample_packing: false` so FLA is optional there.
+
+4. Run a finetuning example:
+
+```bash
+# Dense 27B text-only (QLoRA, ~47 GiB VRAM with sample packing)
+axolotl train examples/qwen3.5/27b-qlora.yaml
+
+# MoE 35B-A3B text-only (QLoRA)
+axolotl train examples/qwen3.5/35b-a3b-moe-qlora.yaml
+
+# MoE 122B-A10B text-only (QLoRA)
+axolotl train examples/qwen3.5/122b-a10b-moe-qlora.yaml
+
+# 7B vision+text (LoRA, multimodal dataset)
+axolotl train examples/qwen3.5/7b-lora-vision.yaml
+```
+
+### TIPS
+
+- For inference, you can experiment with `temperature: 0.7`, `top_p: 0.8`, `top_k: 20`, and `min_p: 0`.
+- You can run a full finetuning by removing `adapter: qlora` and `load_in_4bit: true`. See [Multi-GPU](#optimization-guides) below.
+- Read more on loading your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+- For **multimodal** finetuning, set `processor_type: AutoProcessor`, `skip_prepare_dataset: true`, and `remove_unused_columns: false` as shown in `7b-lora-vision.yaml`.
+- The Gated DeltaNet linear attention layers (`linear_attn.*`) can optionally be added to `lora_target_modules` — they are commented out by default.
+
+## Optimization Guides
+
+- [Optimizations Guide](https://docs.axolotl.ai/docs/optimizations.html)
+
+## Related Resources
+
+- [Qwen3.5 Blog](https://qwenlm.github.io/blog/qwen3.5/)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/trinity/README.md

@@ -8,13 +8,15 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
 
-2. Run the finetuning example:
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. Run the finetuning example:
 
     ```bash
     axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
     ```
 
-This config uses about 24.9 GiB VRAM.
+This config uses about 24.9 GiB VRAM (w/o CCE).
 
 Let us know how it goes. Happy finetuning! 🚀
 
@@ -29,10 +31,6 @@ Let us know how it goes. Happy finetuning! 🚀
 
 Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
-## Limitations
-
-**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
-
 ## Related Resources
 
 - [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)

examples/trinity/trinity-nano-preview-qlora.yaml

@@ -1,5 +1,4 @@
 base_model: arcee-ai/Trinity-Nano-Preview
-trust_remote_code: true
 revision_of_model: 2ee94b0
 
 # Automatically upload checkpoint and final model to HF

requirements.txt

@@ -2,25 +2,28 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.49.1
-triton>=3.0.0
+triton>=3.4.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.6.4
+liger-kernel==0.7.0
 # END section
 
 packaging==26.0
 huggingface_hub>=1.1.7
 peft>=0.18.1
 tokenizers>=0.22.1
-transformers==5.0.0
-accelerate==1.12.0
+transformers==5.3.0
+accelerate==1.13.0
 datasets==4.5.0
-deepspeed>=0.18.3
-trl==0.27.1
-hf_xet==1.2.0
-kernels==0.11.5
+deepspeed>=0.18.6,<0.19.0
+trl==0.29.0
+hf_xet==1.3.2
+kernels==0.12.2
 
-trackio>=0.13.0
+fla-core==0.4.1
+flash-linear-attention==0.4.1
+
+trackio>=0.16.1
 typing-extensions>=4.15.0
 
 optimum==1.16.2
@@ -63,7 +66,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.13.0
+torchao==0.16.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 
@@ -72,4 +75,4 @@ axolotl-contribs-mit==0.0.6
 # telemetry
 posthog==6.7.11
 
-mistral-common==1.8.8
+mistral-common==1.10.0

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f4b5712"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"'
 )

setup.py

@@ -26,6 +26,18 @@ def parse_requirements(extras_require_map):
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         install_xformers = platform.machine() != "aarch64"
+        if platform.machine() == "aarch64":
+            # skip on ARM64
+            skip_packages = [
+                "torchao",
+                "fla-core",
+                "flash-linear-attention",
+            ]
+            _install_requires = [
+                req
+                for req in _install_requires
+                if re.split(r"[>=<]", req)[0].strip() not in skip_packages
+            ]
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [

src/axolotl/cli/__init__.py

@@ -6,5 +6,6 @@ from axolotl.logging_config import configure_logging
 
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+os.environ.setdefault("TRL_EXPERIMENTAL_SILENCE", "1")
 
 configure_logging()

src/axolotl/cli/cloud/modal_.py

@@ -90,9 +90,8 @@ class ModalCloud(Cloud):
         # grab the sha256 hash from docker hub for this image+tag
         # this ensures that we always get the latest image for this tag, even if it's already cached
         try:
-            manifest = subprocess.check_output(  # nosec B602
-                f"docker manifest inspect {docker_image}",
-                shell=True,
+            manifest = subprocess.check_output(  # nosec
+                ["docker", "manifest", "inspect", docker_image],
             ).decode("utf-8")
             sha256_hash = json.loads(manifest)["manifests"][0]["digest"]
         except subprocess.CalledProcessError:

src/axolotl/cli/config.py

@@ -5,13 +5,13 @@ import os
 import tempfile
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import Union
+from typing import Any, Optional, Union
 from urllib.parse import urlparse
 
 import requests
 import torch
 import yaml
-from transformers.utils import is_torch_bf16_gpu_available
+from transformers.utils import is_torch_bf16_gpu_available, is_torch_tf32_available
 
 from axolotl.integrations.base import PluginManager
 from axolotl.telemetry.errors import send_errors
@@ -32,6 +32,63 @@ from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__)
 
+
+def _coerce_value(value: Any, existing: Optional[Any] = None) -> Any:
+    """Coerce a string CLI value to its most likely Python type.
+
+    If an existing value is present in the config, its type is used to guide
+    casting.  Otherwise, YAML-style inference is applied: booleans, ints,
+    floats, and None literals are recognised automatically.
+
+    Args:
+        value: The raw value (typically a string from the CLI).
+        existing: An optional existing config value whose type guides coercion.
+
+    Returns:
+        The value cast to the inferred or expected type.
+    """
+    if not isinstance(value, str):
+        return value
+
+    # If the config already has a typed value, cast to match
+    if existing is not None:
+        if isinstance(existing, bool):
+            return value.lower() in ("true", "1", "yes")
+        if isinstance(existing, int):
+            try:
+                return int(value)
+            except (ValueError, TypeError):
+                return value
+        if isinstance(existing, float):
+            try:
+                return float(value)
+            except (ValueError, TypeError):
+                return value
+        # For other types (str, list, dict, etc.), return as-is
+        return value
+
+    # No existing value -- use YAML-style inference
+    lower = value.lower()
+    if lower in ("true", "yes"):
+        return True
+    if lower in ("false", "no"):
+        return False
+    if lower in ("null", "none", "~"):
+        return None
+
+    # Try int then float
+    try:
+        return int(value)
+    except ValueError:
+        pass
+    try:
+        return float(value)
+    except ValueError:
+        pass
+
+    return value
+
+
 API_KEY_FIELDS = {"comet_api_key"}
 
 TELEMETRY_MANAGER = TelemetryManager.get_instance()
@@ -208,18 +265,42 @@ def load_cfg(
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
     cfg_keys = cfg.keys()
+
+    # Separate nested (dot-notation) kwargs from flat kwargs
+    nested_kwargs: dict[str, dict[str, Any]] = {}
+    flat_kwargs: dict[str, Any] = {}
     for key, value in kwargs.items():
+        if "__" in key:
+            parent, child = key.split("__", 1)
+            nested_kwargs.setdefault(parent, {})[child] = value
+        else:
+            flat_kwargs[key] = value
+
+    # Apply flat kwargs
+    for key, value in flat_kwargs.items():
         # If not strict, allow writing to cfg even if it's not in the yml already
         if key in cfg_keys or not cfg.strict:
-            if isinstance(cfg[key], bool):
-                cfg[key] = bool(value)
-            else:
-                cfg[key] = value
+            cfg[key] = _coerce_value(value, cfg.get(key))
+
+    # Apply nested kwargs (e.g., trl__beta -> cfg.trl.beta)
+    for parent, children in nested_kwargs.items():
+        if parent not in cfg_keys and cfg.strict:
+            continue
+        if cfg[parent] is None:
+            cfg[parent] = {}
+        if not isinstance(cfg[parent], dict):
+            LOG.warning(
+                "Overwriting non-dict value for '%s' with nested CLI overrides", parent
+            )
+            cfg[parent] = {}
+        for child_key, child_value in children.items():
+            existing_child = cfg[parent].get(child_key)
+            cfg[parent][child_key] = _coerce_value(child_value, existing_child)
 
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
-    except:
+    except (RuntimeError, AssertionError):
         gpu_version = None
 
     prepare_plugins(cfg)
@@ -229,6 +310,7 @@ def load_cfg(
         capabilities={
             "bf16": is_torch_bf16_gpu_available(),
             "fp8": compute_supports_fp8(),
+            "tf32": is_torch_tf32_available(),
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },

src/axolotl/cli/merge_lora.py

@@ -71,6 +71,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         merge_lora=True,
         load_in_8bit=False,
         load_in_4bit=False,
+        quantize_moe_experts=False,
         flash_attention=False,
         context_parallel_size=None,
         deepspeed=None,

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -196,12 +196,10 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     state.wait_for_everyone()
     LOG.info(
         f"FSDP SHARDED_STATE_DICT weights successfully merged to: {output_path}",
-        main_process_only=True,
     )
     LOG.info(
         "Merged weights are only the safetensors and doesn't include the model configuration "
         f"or tokenizer which may be found in {parsed_cfg.output_dir}.",
-        main_process_only=True,
     )
 
 

src/axolotl/cli/utils/args.py

@@ -2,7 +2,7 @@
 
 import dataclasses
 from functools import wraps
-from types import NoneType
+from types import NoneType, UnionType
 from typing import Any, Callable, Type, Union, get_args, get_origin
 
 import click
@@ -20,7 +20,8 @@ def _strip_optional_type(field_type: type | str | None):
         If the input type is `Union[T, None]` or `Optional[T]`, returns `T`. Otherwise
             returns the input type unchanged.
     """
-    if get_origin(field_type) is Union and type(None) in get_args(field_type):
+    is_union = get_origin(field_type) is Union or isinstance(field_type, UnionType)
+    if is_union and type(None) in get_args(field_type):
         field_type = next(
             t for t in get_args(field_type) if not isinstance(t, NoneType)
         )
@@ -87,10 +88,70 @@ def add_options_from_dataclass(config_class: Type[Any]) -> Callable:
     return decorator
 
 
+def _is_pydantic_model(field_type: type) -> bool:
+    """Check if a type is a Pydantic BaseModel subclass."""
+    try:
+        return isinstance(field_type, type) and issubclass(field_type, BaseModel)
+    except TypeError:
+        return False
+
+
+def _get_field_description(field) -> str | None:
+    """Get description from a Pydantic field, checking both .description and json_schema_extra."""
+    if field.description:
+        return field.description
+    if field.json_schema_extra and isinstance(field.json_schema_extra, dict):
+        return field.json_schema_extra.get("description")
+    return None
+
+
+def _add_nested_model_options(
+    function: Callable, parent_name: str, model_class: Type[BaseModel]
+) -> Callable:
+    """
+    Add Click options for all fields of a nested Pydantic model using dot-notation.
+
+    Note: Only single-level nesting is supported (e.g., ``--trl.beta``).
+    Deeper nesting (e.g., ``--trl.scheduler.warmup``) is not handled.
+
+    Args:
+        function: Click command function to add options to.
+        parent_name: Parent field name (e.g., "trl").
+        model_class: Nested Pydantic model class.
+
+    Returns:
+        Function with added Click options.
+    """
+    for sub_name, sub_field in reversed(model_class.model_fields.items()):
+        sub_type = _strip_optional_type(sub_field.annotation)
+        # Use dot notation: --parent.sub_field
+        cli_name = f"{parent_name}.{sub_name}".replace("_", "-")
+        # The kwarg name uses double-underscore as separator
+        param_name = f"{parent_name}__{sub_name}"
+        description = _get_field_description(sub_field)
+
+        if sub_type is bool:
+            option_name = f"--{cli_name}/--no-{cli_name}"
+            function = click.option(
+                option_name, param_name, default=None, help=description
+            )(function)
+        else:
+            option_name = f"--{cli_name}"
+            click_type = {str: str, int: int, float: float}.get(sub_type)
+            function = click.option(
+                option_name, param_name, default=None, type=click_type, help=description
+            )(function)
+
+    return function
+
+
 def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
     """
     Create Click options from the fields of a Pydantic model.
 
+    For fields whose type is itself a Pydantic BaseModel, dot-notation CLI options are
+    generated for each sub-field (e.g., ``--trl.beta=0.1``).
+
     Args:
         config_class: PyDantic model with fields to parse from the CLI
 
@@ -103,6 +164,11 @@ def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
         for name, field in reversed(config_class.model_fields.items()):
             field_type = _strip_optional_type(field.annotation)
 
+            # Handle nested Pydantic models with dot-notation options
+            if _is_pydantic_model(field_type):
+                function = _add_nested_model_options(function, name, field_type)
+                continue
+
             if field_type is bool:
                 field_name = name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"

src/axolotl/cli/vllm_serve.py

@@ -38,7 +38,18 @@ def do_vllm_serve(
     cfg = load_cfg(config)
     model = cfg.base_model
 
-    serve_module = cli_args.get("serve_module", "trl.scripts.vllm_serve")
+    # Determine serve module: explicit CLI/config > auto-select from vllm_lora_sync > default
+    serve_module = cli_args.get("serve_module") or getattr(
+        cfg.vllm, "serve_module", None
+    )
+    if (
+        serve_module is None
+        and getattr(cfg, "trl", None)
+        and getattr(cfg.trl, "vllm_lora_sync", False)
+    ):
+        serve_module = "axolotl.scripts.vllm_serve_lora"
+    if serve_module is None:
+        serve_module = "trl.scripts.vllm_serve"
     vllm_serve_main = __import__(serve_module, fromlist=["main"]).main
     tensor_parallel_size = 1
     data_parallel_size = 1
@@ -68,7 +79,7 @@ def do_vllm_serve(
         cli_args.get("enable_reasoning") or cfg.vllm.enable_reasoning or False
     )
 
-    vllm_script_args = AxolotlScriptArguments(
+    base_kwargs = dict(
         model=model,
         tensor_parallel_size=tensor_parallel_size,
         data_parallel_size=data_parallel_size,
@@ -78,7 +89,21 @@ def do_vllm_serve(
         dtype=dtype,
         max_model_len=max_model_len,
         enable_prefix_caching=enable_prefix_caching,
-        reasoning_parser=reasoning_parser,
-        enable_reasoning=enable_reasoning,
     )
+
+    # Use LoRAScriptArguments when serving with native LoRA support
+    if serve_module == "axolotl.scripts.vllm_serve_lora":
+        from axolotl.scripts.vllm_serve_lora import LoRAScriptArguments
+
+        lora_kwargs = {}
+        if hasattr(cfg, "lora_r") and cfg.lora_r:
+            lora_kwargs["max_lora_rank"] = cfg.lora_r
+        vllm_script_args = LoRAScriptArguments(**base_kwargs, **lora_kwargs)
+    else:
+        vllm_script_args = AxolotlScriptArguments(
+            **base_kwargs,
+            reasoning_parser=reasoning_parser,
+            enable_reasoning=enable_reasoning,
+        )
+
     vllm_serve_main(vllm_script_args)

src/axolotl/common/architectures.py

@@ -12,10 +12,15 @@ MOE_ARCH_BLOCK = {
     "mixtral": "MixtralSparseMoeBlock",
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
+    "qwen3_5_moe": "Qwen3_5MoeSparseMoeBlock",
     "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
     "deepseek_v3": "DeepseekV3MoE",
+    "mistral4": "Mistral4MoE",
     "gpt_oss": "GptOssDecoderLayer",
     "lfm2_moe": "Lfm2MoeSparseMoeBlock",
     "afmoe": "AfmoeMoE",
+    "glm4_moe": "Glm4MoeDecoderLayer",
+    "glm4_moe_lite": "Glm4MoeLiteDecoderLayer",
+    "glm_moe_dsa": "GlmMoeDsaDecoderLayer",
 }

src/axolotl/convert.py

@@ -67,7 +67,7 @@ class JsonToJsonlConverter:
         self.json_parser = json_parser
         self.jsonl_serializer = jsonl_serializer
 
-    def convert(self, input_file_path, output_file_path):
+    def convert(self, input_file_path):
         content = self.file_reader.read(input_file_path)
         data = self.json_parser.parse(content)
         # data = [r for r in data if r["conversations"]]  # vicuna cleaned has rows with empty conversations

src/axolotl/core/builders/base.py

@@ -250,7 +250,7 @@ class TrainerBuilderBase(abc.ABC):
 
     def _configure_precision_settings(self, training_args_kwargs: dict):
         training_args_kwargs["fp16"] = (self.cfg.fp16 and not self.cfg.bf16) or False
-        training_args_kwargs["tf32"] = self.cfg.tf32
+        training_args_kwargs["tf32"] = True if self.cfg.tf32 is True else False
         if self.cfg.bf16 == "full":
             training_args_kwargs["bf16_full_eval"] = True
         else:
@@ -409,6 +409,9 @@ class TrainerBuilderBase(abc.ABC):
             if self.cfg.hub_strategy:
                 training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
 
+            if self.cfg.hub_revision:
+                training_args_kwargs["hub_revision"] = self.cfg.hub_revision
+
     def _configure_save_and_eval_strategy(self, training_args_kwargs: dict):
         # save_strategy and save_steps
         if self.cfg.save_steps:

src/axolotl/core/builders/causal.py

@@ -122,6 +122,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ColabCallback = colab_inference_post_train_callback(trainer)
             callbacks.append(ColabCallback(self.cfg))
 
+        if getattr(self.cfg, "generate_samples", False):
+            from axolotl.utils.callbacks.generation import SFTGenerationCallback
+
+            callbacks.append(SFTGenerationCallback(trainer))
+            LOG.info("SFT sample generation enabled")
+
         callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
         return callbacks
 
@@ -246,7 +252,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ddp_find_unused_parameters
         )
 
-        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
+        if self.cfg.group_by_length:
+            training_arguments_kwargs["train_sampling_strategy"] = "group_by_length"
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)

src/axolotl/core/builders/rl.py

@@ -11,7 +11,6 @@ from axolotl.core.trainers import (
 )
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
-from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
 from axolotl.utils.callbacks.qat import QATCallback
@@ -53,8 +52,18 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls_args = [self.model]
 
         if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
+            from axolotl.core.trainers.grpo import GRPOStrategy
+
+            async_grpo = bool(
+                self.cfg.trl
+                and (
+                    getattr(self.cfg.trl, "async_prefetch", False)
+                    or getattr(self.cfg.trl, "use_data_producer", False)
+                )
+            )
             trainer_cls = GRPOStrategy.get_trainer_class(
-                sequence_parallel=self.cfg.context_parallel_size > 1
+                sequence_parallel=self.cfg.context_parallel_size > 1,
+                async_grpo=async_grpo,
             )
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
             trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
@@ -119,11 +128,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.use_wandb:
             training_args_kwargs["run_name"] = self.cfg.wandb_name
 
-        if self.cfg.max_prompt_len:
-            training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
-        else:
-            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
-
         training_args_cls = None
         blocklist_args_kwargs = []
         if self.cfg.rl is RLType.SIMPO:
@@ -133,21 +137,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-            # Handle when max_prompt_length == max_length from defaults
-            # CPOTrainer requires strictly less than
-            if (
-                training_args_kwargs["max_prompt_length"]
-                == training_args_kwargs["max_length"]
-            ):
-                training_args_kwargs["max_prompt_length"] -= 1
+            blocklist_args_kwargs.append("max_prompt_length")
 
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
 
+            blocklist_args_kwargs.append("max_prompt_length")
+
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
             # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs = ["max_prompt_length"]
+            blocklist_args_kwargs.append("max_prompt_length")
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -157,7 +157,18 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             )
 
         elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            training_args_cls = GRPOStrategy.get_training_args_class()
+            from axolotl.core.trainers.grpo import GRPOStrategy
+
+            async_grpo = bool(
+                self.cfg.trl
+                and (
+                    getattr(self.cfg.trl, "async_prefetch", False)
+                    or getattr(self.cfg.trl, "use_data_producer", False)
+                )
+            )
+            training_args_cls = GRPOStrategy.get_training_args_class(
+                async_grpo=async_grpo
+            )
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
             if self.cfg.rl is RLType.GDPO:
@@ -223,13 +234,36 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             trainer_kwargs, trainer_cls
         )
 
-        trainer = trainer_cls(
-            *trainer_cls_args,
-            args=training_args,
-            train_dataset=self.train_dataset,
-            callbacks=self.get_callbacks(),
-            **trainer_kwargs,
-        )
+        # Allow FP8-quantized models to be fine-tuned with LoRA adapters.
+        # transformers' validate_quantization_for_training blocks FP8 because
+        # hf_quantizer.is_trainable is False, but LoRA only trains the adapters
+        # (base weights stay frozen in FP8).
+        _orig_validate_quant = None
+        if (
+            self.cfg.adapter
+            and hasattr(self.model, "is_quantized")
+            and self.model.is_quantized
+        ):
+            import transformers.trainer as _trainer_module
+
+            _orig_validate_quant = _trainer_module.validate_quantization_for_training
+            _trainer_module.validate_quantization_for_training = lambda model: None
+
+        try:
+            trainer = trainer_cls(
+                *trainer_cls_args,
+                args=training_args,
+                train_dataset=self.train_dataset,
+                callbacks=self.get_callbacks(),
+                **trainer_kwargs,
+            )
+        finally:
+            if _orig_validate_quant is not None:
+                import transformers.trainer as _trainer_module
+
+                _trainer_module.validate_quantization_for_training = (
+                    _orig_validate_quant
+                )
         if self.cfg.fsdp_config or self.cfg.fsdp:
             ensure_dtype(trainer.model, dtype=self.cfg.torch_dtype)
             if self.cfg.rl in [RLType.DPO, RLType.IPO] and trainer.ref_model:

src/axolotl/core/trainers/base.py

@@ -26,7 +26,7 @@ from transformers import PreTrainedModel, Trainer
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
 from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
-from trl.trainer.utils import pad_to_length
+from trl.experimental.utils import pad_to_length
 from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (
@@ -719,6 +719,20 @@ class AxolotlTrainer(
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         LOG.info(f"Saving model checkpoint to {output_dir}")
+
+        # fix for Context Parallel save: CP eval invalidates tensor storage
+        # pointers, so clone to CPU to get fresh valid storage for safetensors
+        if (
+            state_dict is not None
+            and self.axolotl_cfg
+            and self.axolotl_cfg.context_parallel_size
+            and self.axolotl_cfg.context_parallel_size > 1
+        ):
+            state_dict = {
+                k: v.detach().cpu() if isinstance(v, torch.Tensor) else v
+                for k, v in state_dict.items()
+            }
+
         supported_classes = (
             (PreTrainedModel,)
             if not is_peft_available()
@@ -729,6 +743,7 @@ class AxolotlTrainer(
         if not isinstance(self.model, supported_classes):
             if state_dict is None:
                 state_dict = self.model.state_dict()
+
             if isinstance(
                 self.accelerator.unwrap_model(self.model, keep_torch_compile=False),
                 supported_classes,
@@ -738,6 +753,7 @@ class AxolotlTrainer(
                 ).save_pretrained(
                     output_dir,
                     state_dict=state_dict,
+                    is_main_process=self.accelerator.is_main_process,
                 )
             else:
                 LOG.info(
@@ -765,11 +781,7 @@ class AxolotlTrainer(
             LOG.info(
                 "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
             )
-            save_jinja_files = True
-            if self.axolotl_cfg:
-                save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
-            self.data_collator.tokenizer.save_pretrained(
-                output_dir, save_jinja_files=save_jinja_files
-            )
+            self.data_collator.tokenizer.save_pretrained(output_dir)
+
         # Good practice: save your training arguments together with the trained model
         torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

src/axolotl/core/trainers/dpo/__init__.py

@@ -25,17 +25,13 @@ class DPOStrategy:
         # Label smoothing is not compatible with IPO
         if cfg.rl is RLType.DPO and cfg.dpo_label_smoothing:
             training_args_kwargs["label_smoothing"] = cfg.dpo_label_smoothing
-        training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
-        training_args_kwargs["generate_during_eval"] = cfg.dpo_generate_during_eval
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
         if cfg.dpo_padding_free is not None:
             training_args_kwargs["padding_free"] = cfg.dpo_padding_free
         if cfg.dpo_norm_loss is not None:
             training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
-        if cfg.dpo_use_logits_to_keep is not None:
-            training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
         if cfg.dpo_use_liger_kernel is not None:
             training_args_kwargs["use_liger_kernel"] = cfg.dpo_use_liger_kernel
         return training_args_kwargs

src/axolotl/core/trainers/dpo/trainer.py

@@ -57,16 +57,18 @@ class AxolotlDPOTrainer(
     def tokenize_row(
         features,
         processing_class,
-        max_prompt_length,
-        max_completion_length,
-        add_special_tokens,
+        max_prompt_length: int | None = None,
+        max_completion_length: int | None = None,
+        add_special_tokens: bool = True,
+        is_chat: bool = False,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length,
-            max_completion_length,
-            add_special_tokens,
+            max_prompt_length=max_prompt_length,
+            max_completion_length=max_completion_length,
+            add_special_tokens=add_special_tokens,
+            is_chat=is_chat,
         )
         # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
         if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
@@ -101,10 +103,10 @@ class AxolotlDPOTrainer(
     ) -> dict[str, torch.Tensor]:
         if self.args.dpo_norm_loss:
             # fmt: off
-            loss_type: str = self.loss_type  # type: ignore[has-type]
+            loss_type: list[str] = self.loss_type  # type: ignore[has-type]
             # fmt: on
             # concatenated_forward handles avg token logprob for ipo case already
-            self.loss_type = "ipo"
+            self.loss_type = ["ipo"]
             res = super().concatenated_forward(model, batch, is_ref_model=is_ref_model)
             self.loss_type = loss_type
             return res

src/axolotl/core/trainers/grpo/__init__.py

@@ -9,8 +9,9 @@ from huggingface_hub import snapshot_download
 from requests import HTTPError
 from trl.trainer.grpo_trainer import RewardFunc
 
-from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
+from axolotl.core.trainers.grpo.args import AxolotlAsyncGRPOConfig, AxolotlGRPOConfig
 from axolotl.core.trainers.grpo.trainer import (
+    AxolotlAsyncGRPOTrainer,
     AxolotlGRPOSequenceParallelTrainer,
     AxolotlGRPOTrainer,
 )
@@ -27,14 +28,31 @@ class GRPOStrategy:
 
     @classmethod
     def get_trainer_class(
-        cls, sequence_parallel: bool
-    ) -> type[AxolotlGRPOTrainer] | type[AxolotlGRPOSequenceParallelTrainer]:
+        cls,
+        sequence_parallel: bool,
+        async_grpo: bool = False,
+    ) -> (
+        type[AxolotlGRPOTrainer]
+        | type[AxolotlGRPOSequenceParallelTrainer]
+        | type[AxolotlAsyncGRPOTrainer]
+    ):
+        if sequence_parallel and async_grpo:
+            raise ValueError(
+                "sequence_parallel and async_grpo cannot both be enabled. "
+                "Disable one of context_parallel_size > 1 or async_prefetch/use_data_producer."
+            )
         if sequence_parallel:
             return AxolotlGRPOSequenceParallelTrainer
+        if async_grpo:
+            return AxolotlAsyncGRPOTrainer
         return AxolotlGRPOTrainer
 
     @classmethod
-    def get_training_args_class(cls) -> type[AxolotlGRPOConfig]:
+    def get_training_args_class(
+        cls, async_grpo: bool = False
+    ) -> type[AxolotlGRPOConfig] | type[AxolotlAsyncGRPOConfig]:
+        if async_grpo:
+            return AxolotlAsyncGRPOConfig
         return AxolotlGRPOConfig
 
     @classmethod
@@ -124,16 +142,63 @@ class GRPOStrategy:
             grpo_args_kwargs["epsilon_high"] = trl.epsilon_high
 
         if trl.use_liger_loss is not None:
-            grpo_args_kwargs["use_liger_loss"] = trl.use_liger_loss
-
-        if trl.rollout_func:
-            grpo_args_kwargs["rollout_func"] = cls.get_rollout_func(trl.rollout_func)
+            grpo_args_kwargs["use_liger_kernel"] = trl.use_liger_loss
 
         if trl.multi_objective_aggregation is not None:
             grpo_args_kwargs["multi_objective_aggregation"] = (
                 trl.multi_objective_aggregation
             )
 
+        # Async GRPO fields
+        if getattr(trl, "use_data_producer", None) is not None:
+            grpo_args_kwargs["use_data_producer"] = trl.use_data_producer
+        if getattr(trl, "async_prefetch", None) is not None:
+            grpo_args_kwargs["async_prefetch"] = trl.async_prefetch
+        if getattr(trl, "prefetch_depth", None) is not None:
+            grpo_args_kwargs["prefetch_depth"] = trl.prefetch_depth
+        if getattr(trl, "vllm_sync_interval", None) is not None:
+            grpo_args_kwargs["vllm_sync_interval"] = trl.vllm_sync_interval
+        if getattr(trl, "streaming_partial_batch", None) is not None:
+            grpo_args_kwargs["streaming_partial_batch"] = trl.streaming_partial_batch
+        if getattr(trl, "streaming_min_groups", None) is not None:
+            grpo_args_kwargs["streaming_min_groups"] = trl.streaming_min_groups
+        if getattr(trl, "vllm_importance_sampling_correction", None) is not None:
+            grpo_args_kwargs["vllm_importance_sampling_correction"] = (
+                trl.vllm_importance_sampling_correction
+            )
+        if getattr(trl, "vllm_importance_sampling_mode", None) is not None:
+            grpo_args_kwargs["vllm_importance_sampling_mode"] = (
+                trl.vllm_importance_sampling_mode
+            )
+        if getattr(trl, "vllm_importance_sampling_cap", None) is not None:
+            grpo_args_kwargs["vllm_importance_sampling_cap"] = (
+                trl.vllm_importance_sampling_cap
+            )
+        if getattr(trl, "off_policy_mask_threshold", None) is not None:
+            grpo_args_kwargs["off_policy_mask_threshold"] = (
+                trl.off_policy_mask_threshold
+            )
+        if getattr(trl, "use_bias_correction_kl", None) is not None:
+            grpo_args_kwargs["use_bias_correction_kl"] = trl.use_bias_correction_kl
+
+        # Fast Async GRPO fields
+        if getattr(trl, "reward_num_workers", None) is not None:
+            grpo_args_kwargs["reward_num_workers"] = trl.reward_num_workers
+        if getattr(trl, "replay_buffer_size", None) is not None:
+            grpo_args_kwargs["replay_buffer_size"] = trl.replay_buffer_size
+        if getattr(trl, "replay_recompute_logps", None) is not None:
+            grpo_args_kwargs["replay_recompute_logps"] = trl.replay_recompute_logps
+        if getattr(trl, "reroll_start_fraction", None) is not None:
+            grpo_args_kwargs["reroll_start_fraction"] = trl.reroll_start_fraction
+        if getattr(trl, "reroll_max_groups", None) is not None:
+            grpo_args_kwargs["reroll_max_groups"] = trl.reroll_max_groups
+        if getattr(trl, "skip_zero_advantage_batches", None) is not None:
+            grpo_args_kwargs["skip_zero_advantage_batches"] = (
+                trl.skip_zero_advantage_batches
+            )
+        if getattr(trl, "vllm_lora_sync", None) is not None:
+            grpo_args_kwargs["vllm_lora_sync"] = trl.vllm_lora_sync
+
         return grpo_args_kwargs
 
     @classmethod
@@ -154,6 +219,8 @@ class GRPOStrategy:
             trainer_kwargs["reward_processing_classes"] = (
                 cfg.trl.reward_processing_classes
             )
+        if cfg.trl and cfg.trl.rollout_func:
+            trainer_kwargs["rollout_func"] = cls.get_rollout_func(cfg.trl.rollout_func)
 
         return trainer_kwargs
 
@@ -164,7 +231,12 @@ class GRPOStrategy:
 
     @classmethod
     def get_blocklist_args_kwargs(cls) -> list[str]:
-        return ["dataset_num_proc", "max_length", "include_tokens_per_second"]
+        return [
+            "dataset_num_proc",
+            "max_length",
+            "include_tokens_per_second",
+            "max_prompt_length",
+        ]
 
     @classmethod
     def get_reward_func(cls, reward_func_fqn: str) -> RewardFunc:

src/axolotl/core/trainers/grpo/args.py

@@ -6,6 +6,7 @@ from dataclasses import dataclass
 
 from trl import GRPOConfig
 
+from axolotl.core.trainers.grpo.fast_async_trainer import FastAsyncGRPOConfig
 from axolotl.core.training_args import AxolotlTrainingMixins
 
 
@@ -14,3 +15,10 @@ class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
     """Axolotl GRPO Config for GRPO training"""
 
     context_parallel_size: int | None = None
+
+
+@dataclass
+class AxolotlAsyncGRPOConfig(AxolotlTrainingMixins, FastAsyncGRPOConfig):
+    """Axolotl Async GRPO Config — adds async prefetch, streaming scoring, and IS correction."""
+
+    context_parallel_size: int | None = None

src/axolotl/core/trainers/grpo/fast_async_trainer.py

@@ -0,0 +1,768 @@
+# Copyright 2020-2026 Axolotl AI. 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.
+
+"""
+Experimental GRPO extensions: parallel reward workers, replay buffer,
+deferred re-roll, and zero-advantage skipping.
+
+These features are built as subclasses of GRPOTrainer and GRPODataProducer,
+using the hook system (_compute_rewards_for_batch, _post_advantage_hook,
+_pre_produce_hook) defined in the base classes.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import threading
+from dataclasses import dataclass, field
+
+import torch
+from torch import nn
+from trl import GRPOTrainer
+
+from axolotl.core.trainers.grpo.async_trainer import (
+    AsyncGRPOConfig,
+    AsyncGRPOTrainer,
+    GRPODataProducer,
+)
+from axolotl.core.trainers.grpo.replay_buffer import ReplayBuffer
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Extended config
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FastAsyncGRPOConfig(AsyncGRPOConfig):
+    """GRPOConfig with additional experimental parameters."""
+
+    reward_num_workers: int = field(
+        default=1,
+        metadata={
+            "help": "Number of persistent subprocess workers for parallel reward computation. Each worker has its "
+            "own main thread so signal.alarm() (used by math_verify) works correctly. Work is sharded across "
+            "workers by prompt groups. Only used with use_data_producer=True and non-nn.Module reward functions."
+        },
+    )
+    replay_buffer_size: int = field(
+        default=0,
+        metadata={
+            "help": "[Experimental, disabled by default] Size of the replay buffer for storing high-signal rollout "
+            "groups. When > 0, groups with reward variance are cached and used to replace zero-signal groups "
+            "(where all rewards are identical). Set to 0 to disable. Only used with use_data_producer=True."
+        },
+    )
+    replay_recompute_logps: bool = field(
+        default=True,
+        metadata={
+            "help": "When True (default), recompute old_per_token_logps for replayed groups using the current "
+            "training model. This fixes the importance sampling mismatch that occurs when replaying stale data. "
+            "Only relevant when replay_buffer_size > 0."
+        },
+    )
+    reroll_start_fraction: float = field(
+        default=0.5,
+        metadata={
+            "help": "Fraction of total training steps after which deferred re-rolling begins. Zero-signal prompts "
+            "(where all rewards in a group are identical) are buffered and re-injected into later batches when the "
+            "model is more likely to solve them. Set to 1.0 to disable. Only used with use_data_producer=True."
+        },
+    )
+    reroll_max_groups: int = field(
+        default=1,
+        metadata={
+            "help": "Maximum number of prompt groups to replace with re-roll candidates per batch. Higher values "
+            "increase data utilization but reduce prompt diversity. Only used with use_data_producer=True."
+        },
+    )
+    skip_zero_advantage_batches: bool = field(
+        default=True,
+        metadata={
+            "help": "When True, skip gradient computation for micro-batches where all advantages are zero (no learning "
+            "signal). This avoids the forward/backward pass entirely when no learning signal is present. The step is "
+            "logged with skipped_zero_adv_batches=1 for monitoring."
+        },
+    )
+    vllm_lora_sync: bool = field(
+        default=False,
+        metadata={
+            "help": "When True, sync LoRA adapter weights to vLLM via filesystem instead of merging into base model "
+            "and NCCL-broadcasting all parameters. vLLM loads the adapter natively using Punica kernels. "
+            "Requires vllm_serve_lora serve module (auto-selected when this is True). "
+            "Syncs only LoRA adapter weights (much smaller) vs full merged model. Legacy merge behavior is used when False."
+        },
+    )
+
+
+# ---------------------------------------------------------------------------
+# Extended data producer with re-roll injection
+# ---------------------------------------------------------------------------
+
+
+class RerollDataProducer(GRPODataProducer):
+    """GRPODataProducer that injects re-roll candidates into prompt batches.
+
+    Reads from the trainer's ``_reroll_buffer`` (populated by
+    ``GRPOExperimentalTrainer._post_advantage_hook``) and replaces the
+    last N prompt groups with previously-failed prompts.
+    """
+
+    def _pre_produce_hook(self, inputs: list, global_step: int) -> list:
+        trainer = self._trainer
+        reroll_buf = getattr(trainer, "_reroll_buffer", None)
+        reroll_lock = getattr(trainer, "_reroll_lock", None)
+        if reroll_buf is None or reroll_lock is None:
+            return inputs
+
+        max_steps = getattr(trainer.args, "max_steps", -1)
+        start_frac = getattr(trainer.args, "reroll_start_fraction", 1.0)
+        max_groups = getattr(trainer.args, "reroll_max_groups", 1)
+        reroll_start_step = (
+            max(1, int(max_steps * start_frac)) if max_steps > 0 else float("inf")
+        )
+
+        if global_step < reroll_start_step:
+            return inputs
+
+        with reroll_lock:
+            n_to_take = min(max_groups, len(reroll_buf))
+            reroll_prompts = [reroll_buf.pop(0) for _ in range(n_to_take)]
+
+        if reroll_prompts:
+            num_gen = self._num_generations
+            n_groups = len(inputs) // num_gen
+            for i, reroll_prompt in enumerate(reroll_prompts):
+                group_idx = n_groups - 1 - i
+                if group_idx < 0:
+                    break
+                start = group_idx * num_gen
+                for j in range(num_gen):
+                    inputs[start + j] = reroll_prompt
+            logger.info(
+                f"[REROLL] Step {global_step}: replaced {len(reroll_prompts)}/{n_groups} prompt groups "
+                f"with deferred re-roll candidates ({len(reroll_buf)} remaining)"
+            )
+
+        return inputs
+
+
+# ---------------------------------------------------------------------------
+# Persistent reward subprocess pool
+# ---------------------------------------------------------------------------
+
+
+def _persistent_reward_worker(conn):
+    """Long-lived reward worker. Receives work items, returns results."""
+    while True:
+        try:
+            msg = conn.recv()
+        except EOFError:
+            break
+        if msg is None:  # Shutdown signal
+            break
+        (
+            reward_funcs,
+            prompts,
+            completions,
+            completion_ids_list,
+            inputs,
+            reward_func_names,
+        ) = msg
+        try:
+            keys = [
+                key
+                for key in inputs[0]
+                if key not in ["prompt", "completion", "completion_ids"]
+            ]
+            reward_kwargs = {key: [example[key] for example in inputs] for key in keys}
+            results = []
+            for reward_func, _reward_func_name in zip(
+                reward_funcs, reward_func_names, strict=True
+            ):
+                output = reward_func(
+                    prompts=prompts,
+                    completions=completions,
+                    completion_ids=completion_ids_list,
+                    **reward_kwargs,
+                )
+                results.append(
+                    [float(r) if r is not None else float("nan") for r in output]
+                )
+            conn.send(results)
+        except Exception:
+            conn.send(None)
+
+
+# ---------------------------------------------------------------------------
+# Extended trainer
+# ---------------------------------------------------------------------------
+
+
+class FastAsyncGRPOTrainer(AsyncGRPOTrainer):
+    """GRPOTrainer with experimental extensions.
+
+    Adds:
+    - Parallel reward subprocess workers (``reward_num_workers``)
+    - Replay buffer for high-signal group reuse (``replay_buffer_size``)
+    - Deferred re-roll of failed prompts (``reroll_start_fraction``)
+    - Zero-advantage micro-batch skipping
+    """
+
+    def __init__(self, *args, **kwargs):
+        # These must be initialized before super().__init__() because
+        # _create_data_producer (called during super().__init__) needs them.
+        self._reroll_buffer: list = []
+        self._reroll_lock = threading.Lock()
+
+        # Temporarily suppress the base class's Liger + OPSM validation check,
+        # since this subclass supports it via a custom compute_liger_loss override.
+        grpo_args = kwargs.get("args")
+        if grpo_args is None:
+            for a in args:
+                if hasattr(a, "off_policy_mask_threshold"):
+                    grpo_args = a
+                    break
+        saved_threshold = None
+        if grpo_args is not None and getattr(grpo_args, "use_liger_kernel", False):
+            saved_threshold = grpo_args.off_policy_mask_threshold
+            grpo_args.off_policy_mask_threshold = None
+
+        super().__init__(*args, **kwargs)
+
+        if saved_threshold is not None:
+            grpo_args.off_policy_mask_threshold = saved_threshold
+            self.off_policy_mask_threshold = saved_threshold
+
+        # Replay buffer
+        if getattr(self.args, "replay_buffer_size", 0) > 0:
+            self._replay_buffer = ReplayBuffer(max_size=self.args.replay_buffer_size)
+        else:
+            self._replay_buffer = None
+        self._replay_recompute_logps = getattr(
+            self.args, "replay_recompute_logps", True
+        )
+
+        # Reward worker pool (lazy-initialized)
+        self._reward_workers = None
+
+    # -- Factory override: use RerollDataProducer ----------------------------
+
+    def _create_data_producer(self, args, train_dataset):
+        """Override to use RerollDataProducer for re-roll prompt injection."""
+        from axolotl.core.trainers.grpo.async_trainer import (
+            AsyncDataProducer,
+            ProducerConfig,
+        )
+
+        producer_config = ProducerConfig(
+            mini_epochs=args.num_iterations,
+            max_rollouts=None,
+            eval_during_produce=False,
+            empty_cache_before_produce=True,
+            empty_cache_after_produce=True,
+            async_prefetch=args.async_prefetch,
+            prefetch_depth=args.prefetch_depth,
+        )
+        data_producer = RerollDataProducer(
+            config=producer_config,
+            prompt_dataset=train_dataset,
+            num_generations=self.num_generations,
+            generation_batch_size=args.generation_batch_size,
+            train_batch_size=args.per_device_train_batch_size,
+            steps_per_generation=args.steps_per_generation,
+            shuffle_dataset=self.shuffle_dataset,
+            seed=args.seed,
+        )
+        data_producer.set_trainer(self)
+        if args.async_prefetch:
+            data_producer = AsyncDataProducer(
+                data_producer,
+                background_produce_kwargs={"skip_policy_logps": True},
+            )
+        return data_producer
+
+    # -- Reward worker pool --------------------------------------------------
+
+    def _get_reward_workers(self):
+        """Return a list of persistent reward worker subprocesses (lazy-initialized)."""
+        import multiprocessing as _mp
+
+        num_workers = getattr(self.args, "reward_num_workers", 1)
+        if num_workers < 1:
+            num_workers = 1
+
+        if self._reward_workers is not None:
+            alive = all(proc.is_alive() for conn, proc in self._reward_workers)
+            if alive and len(self._reward_workers) == num_workers:
+                return self._reward_workers
+            self._shutdown_reward_workers()
+
+        workers = []
+        for _ in range(num_workers):
+            parent_conn, child_conn = _mp.Pipe()
+            proc = _mp.Process(
+                target=_persistent_reward_worker, args=(child_conn,), daemon=True
+            )
+            proc.start()
+            child_conn.close()
+            workers.append((parent_conn, proc))
+
+        self._reward_workers = workers
+        return workers
+
+    def _shutdown_reward_workers(self):
+        """Shut down all persistent reward workers."""
+        if self._reward_workers is None:
+            return
+        for conn, proc in self._reward_workers:
+            try:
+                conn.send(None)
+                proc.join(timeout=5)
+            except Exception:
+                pass
+            try:
+                conn.close()
+            except Exception:
+                pass
+        self._reward_workers = None
+
+    # -- Hook overrides ------------------------------------------------------
+
+    def _compute_rewards_for_batch(
+        self, inputs, prompts, completions, completion_ids_list
+    ):
+        """Dispatch rewards to parallel subprocess workers (synchronous wrapper)."""
+        self._launch_reward_workers(inputs, prompts, completions, completion_ids_list)
+        return self._collect_reward_workers(
+            inputs, prompts, completions, completion_ids_list
+        )
+
+    def _launch_reward_workers(self, inputs, prompts, completions, completion_ids_list):
+        """Send reward work to subprocess workers (non-blocking).
+
+        Results are collected later by _collect_reward_workers, allowing GPU
+        logprob computation to overlap with CPU reward computation.
+        """
+        reward_can_bg = all(
+            callable(rf)
+            and not isinstance(rf, nn.Module)
+            and not asyncio.iscoroutinefunction(rf)
+            for rf in self.reward_funcs
+        )
+        num_workers = getattr(self.args, "reward_num_workers", 1)
+
+        if not reward_can_bg or num_workers <= 1:
+            # Can't parallelize — store args for sync fallback in collect
+            self._reward_workers_used = None
+            self._pending_reward_args = (
+                inputs,
+                prompts,
+                completions,
+                completion_ids_list,
+            )
+            return
+
+        workers = self._get_reward_workers()
+        num_generations = self.num_generations
+        num_prompts = len(prompts)
+        num_groups = num_prompts // num_generations
+
+        # Shard by prompt groups across workers
+        groups_per_worker = max(1, (num_groups + len(workers) - 1) // len(workers))
+        workers_used = []
+        for w_idx, (conn, _proc) in enumerate(workers):
+            g_start = w_idx * groups_per_worker
+            g_end = min((w_idx + 1) * groups_per_worker, num_groups)
+            if g_start >= num_groups:
+                break
+            s_start = g_start * num_generations
+            s_end = g_end * num_generations
+            conn.send(
+                (
+                    self.reward_funcs,
+                    prompts[s_start:s_end],
+                    completions[s_start:s_end],
+                    completion_ids_list[s_start:s_end],
+                    inputs[s_start:s_end],
+                    self.reward_func_names,
+                )
+            )
+            workers_used.append(conn)
+
+        self._reward_workers_used = workers_used
+        self._pending_reward_args = (inputs, prompts, completions, completion_ids_list)
+
+    def _collect_reward_workers(
+        self, inputs, prompts, completions, completion_ids_list
+    ):
+        """Collect reward results from subprocess workers (blocks until done)."""
+        from accelerate.utils import gather
+
+        workers_used = getattr(self, "_reward_workers_used", None)
+        args = getattr(self, "_pending_reward_args", None)
+        self._reward_workers_used = None
+        self._pending_reward_args = None
+
+        if workers_used is None:
+            # Sync fallback — compute on main thread
+            if args is not None:
+                return self._calculate_rewards(*args)
+            return self._calculate_rewards(
+                inputs, prompts, completions, completion_ids_list
+            )
+
+        device = self.accelerator.device
+        num_prompts = len(args[1]) if args else len(prompts)
+
+        # Collect results from workers
+        all_worker_results = []
+        any_failed = False
+        for conn in workers_used:
+            result = conn.recv()
+            if result is None:
+                any_failed = True
+                # Drain remaining workers to prevent stale results in pipes
+                for remaining_conn in workers_used:
+                    if remaining_conn is not conn:
+                        try:
+                            remaining_conn.recv()
+                        except Exception:
+                            pass
+                break
+            all_worker_results.append(result)
+
+        if not any_failed:
+            rewards_per_func = torch.zeros(
+                num_prompts, len(self.reward_funcs), device=device
+            )
+            offset = 0
+            for worker_result in all_worker_results:
+                chunk_size = len(worker_result[0])
+                for i, result in enumerate(worker_result):
+                    rewards_per_func[offset : offset + chunk_size, i] = torch.tensor(
+                        result, dtype=torch.float32, device=device
+                    )
+                offset += chunk_size
+            return gather(rewards_per_func)
+
+        # Fallback to main thread on failure
+        if args is not None:
+            return self._calculate_rewards(*args)
+        return self._calculate_rewards(
+            inputs, prompts, completions, completion_ids_list
+        )
+
+    def _post_advantage_hook(
+        self,
+        data: dict,
+        rewards_per_func,
+        advantages,
+        inputs: list,
+        num_generations: int,
+        mode: str,
+        s_start: int | None = None,
+        s_end: int | None = None,
+        is_last_chunk: bool = True,
+    ) -> None:
+        """Replay buffer store/replace + re-roll buffering."""
+        from trl.models.utils import disable_gradient_checkpointing
+
+        # -- Replay buffer: store high-signal groups --
+        if self._replay_buffer is not None:
+            local_grouped = rewards_per_func.view(
+                -1, num_generations, len(self.reward_funcs)
+            )
+            per_group_std = local_grouped.std(dim=1)
+            has_signal = (per_group_std > 0).any(dim=1)
+            offset = s_start or 0
+
+            if has_signal.any():
+                grouped_adv = advantages.view(-1, num_generations)
+                replay_scores = grouped_adv.abs().sum(dim=1) * per_group_std.sum(dim=1)
+                for group_idx in has_signal.nonzero(as_tuple=True)[0]:
+                    gi = group_idx.item()
+                    start = offset + gi * num_generations
+                    end = start + num_generations
+                    group_data = {}
+                    for key in data:
+                        val = data[key]
+                        if (
+                            isinstance(val, torch.Tensor)
+                            and val.dim() > 0
+                            and val.size(0) >= end
+                        ):
+                            group_data[key] = val[start:end].clone()
+                    self._replay_buffer.add(replay_scores[gi].item(), group_data)
+
+            # Replace zero-signal groups with high-signal replay buffer entries
+            # Only in non-streaming path (s_start is None) — streaming scores
+            # groups incrementally, so replacement + logprob recompute would be
+            # too expensive per chunk.
+            n_replaced = 0
+            if s_start is None:
+                no_signal = ~has_signal
+                replaced_ranges = []
+                if no_signal.any() and len(self._replay_buffer) > 0:
+                    for group_idx in no_signal.nonzero(as_tuple=True)[0]:
+                        sampled = self._replay_buffer.sample(1)
+                        if sampled is None:
+                            break
+                        sampled_group = sampled[0]
+                        gi = group_idx.item()
+                        start = offset + gi * num_generations
+                        end = start + num_generations
+                        for key, val in sampled_group.items():
+                            if key in data and isinstance(data[key], torch.Tensor):
+                                src = val.to(data[key].device)
+                                tgt_seq_len = (
+                                    data[key].size(1) if data[key].dim() > 1 else None
+                                )
+                                if start >= data[key].size(0) or end > data[key].size(
+                                    0
+                                ):
+                                    continue
+                                if tgt_seq_len is not None:
+                                    if src.size(1) <= tgt_seq_len:
+                                        data[key][start:end] = 0
+                                        data[key][start:end, : src.size(1)] = src
+                                    else:
+                                        data[key][start:end] = src[:, :tgt_seq_len]
+                                else:
+                                    data[key][start:end] = src
+                        replaced_ranges.append((start, end))
+                        n_replaced += 1
+
+                # Recompute old_per_token_logps for replayed groups
+                if (
+                    n_replaced > 0
+                    and self._replay_recompute_logps
+                    and "old_per_token_logps" in data
+                ):
+                    with (
+                        torch.no_grad(),
+                        disable_gradient_checkpointing(
+                            self.model, self.args.gradient_checkpointing_kwargs
+                        ),
+                    ):
+                        for r_start, r_end in replaced_ranges:
+                            r_ids = torch.cat(
+                                [
+                                    data["prompt_ids"][r_start:r_end],
+                                    data["completion_ids"][r_start:r_end],
+                                ],
+                                dim=1,
+                            )
+                            r_mask = torch.cat(
+                                [
+                                    data["prompt_mask"][r_start:r_end],
+                                    data["completion_mask"][r_start:r_end],
+                                ],
+                                dim=1,
+                            )
+                            r_logits_to_keep = data["completion_ids"].size(1)
+                            r_fwd_kwargs = {}
+                            for fk in (
+                                "pixel_values",
+                                "image_grid_thw",
+                                "pixel_attention_mask",
+                                "image_sizes",
+                                "token_type_ids",
+                                "mm_token_type_ids",
+                            ):
+                                if fk in data:
+                                    r_fwd_kwargs[fk] = data[fk]
+                            r_logps, _ = self._get_per_token_logps_and_entropies(
+                                self.model,
+                                r_ids,
+                                r_mask,
+                                r_logits_to_keep,
+                                r_end - r_start,
+                                **r_fwd_kwargs,
+                            )
+                            data["old_per_token_logps"][r_start:r_end] = r_logps
+
+                if n_replaced > 0:
+                    self._metrics[mode]["replay_buffer_replacements"].append(
+                        float(n_replaced)
+                    )
+
+            if is_last_chunk:
+                self._metrics[mode]["replay_buffer_size"].append(
+                    float(len(self._replay_buffer))
+                )
+
+        # -- Re-roll buffer: store failed prompts --
+        if getattr(self.args, "reroll_start_fraction", 1.0) < 1.0:
+            grouped_rewards = rewards_per_func.view(
+                -1, num_generations, len(self.reward_funcs)
+            )
+            per_group_std = grouped_rewards.std(dim=1)
+            per_group_mean = grouped_rewards.mean(dim=1)
+            zero_signal = (per_group_std == 0).all(dim=1)
+            all_failed = (per_group_mean.abs() < 1e-6).all(dim=1)
+            should_reroll = zero_signal & all_failed
+            _n_buffered = 0
+            with self._reroll_lock:
+                for group_idx in should_reroll.nonzero(as_tuple=True)[0]:
+                    idx = group_idx.item() * num_generations
+                    if idx >= len(inputs):
+                        continue
+                    prompt_input = inputs[idx]
+                    self._reroll_buffer.append(prompt_input)
+                    _n_buffered += 1
+            if _n_buffered > 0:
+                self._metrics[mode]["reroll_buffered"].append(float(_n_buffered))
+            if is_last_chunk:
+                self._metrics[mode]["reroll_buffer_size"].append(
+                    float(len(self._reroll_buffer))
+                )
+
+    # -- Zero-advantage skipping + Liger OPSM ---------------------------------
+
+    def compute_liger_loss(self, unwrapped_model, inputs):
+        """Liger loss with zero-adv skipping and off-policy sequence masking (OPSM).
+
+        The base class Liger path doesn't support OPSM because the fused kernel
+        doesn't expose per-token logprobs needed for the KL computation. This
+        override computes them via chunked lm_head matmul (no grad, low memory)
+        and applies the OPSM to the loss mask before calling the kernel.
+        """
+        if self.args.skip_zero_advantage_batches and torch.all(
+            inputs["advantages"] == 0
+        ):
+            mode = "train" if self.model.training else "eval"
+            self._metrics[mode]["skipped_zero_adv_batches"].append(1.0)
+            return torch.tensor(
+                0.0, device=inputs["advantages"].device, requires_grad=True
+            )
+
+        if self.off_policy_mask_threshold is None:
+            return super().compute_liger_loss(unwrapped_model, inputs)
+
+        # OPSM path: need per_token_logps for KL, which Liger kernel doesn't provide
+        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
+        completion_ids, completion_mask = (
+            inputs["completion_ids"],
+            inputs["completion_mask"],
+        )
+        input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
+        logits_to_keep = completion_ids.size(1)
+
+        last_hidden_state = self._get_last_hidden_state(
+            unwrapped_model,
+            input_ids,
+            attention_mask,
+            logits_to_keep,
+            inputs.get("pixel_values"),
+            inputs.get("image_grid_thw"),
+            inputs.get("pixel_attention_mask"),
+            inputs.get("image_sizes"),
+        )
+
+        loss_mask = (
+            completion_mask
+            if "tool_mask" not in inputs
+            else completion_mask * inputs["tool_mask"]
+        )
+
+        # Compute per_token_logps via chunked lm_head matmul (no grad, low memory)
+        lm_weight = unwrapped_model.lm_head.weight
+        lm_bias = unwrapped_model.lm_head.bias
+        with torch.no_grad():
+            per_token_logps_chunks = []
+            for i in range(last_hidden_state.size(0)):
+                chunk_logits = torch.matmul(last_hidden_state[i : i + 1], lm_weight.t())
+                if lm_bias is not None:
+                    chunk_logits = chunk_logits + lm_bias
+                chunk_lps = (
+                    chunk_logits.float()
+                    .log_softmax(-1)
+                    .gather(-1, completion_ids[i : i + 1].unsqueeze(-1))
+                    .squeeze(-1)
+                )
+                per_token_logps_chunks.append(chunk_lps)
+                del chunk_logits
+            per_token_logps = torch.cat(per_token_logps_chunks, dim=0)
+
+        advantages = inputs["advantages"]
+        if advantages.dim() == 1:
+            advantages_2d = advantages.unsqueeze(1)
+        else:
+            advantages_2d = advantages
+
+        sampling_per_token_logps = inputs.get("sampling_per_token_logps")
+        if sampling_per_token_logps is None:
+            sampling_per_token_logps = inputs.get("old_per_token_logps")
+        if sampling_per_token_logps is None:
+            sampling_per_token_logps = per_token_logps
+
+        off_policy_mask = GRPOTrainer.get_off_policy_mask(
+            advantages=advantages_2d,
+            per_token_logps=per_token_logps,
+            sampling_per_token_logps=sampling_per_token_logps,
+            mask=loss_mask,
+            off_policy_threshold=self.off_policy_mask_threshold,
+        )
+        loss_mask = loss_mask * off_policy_mask
+
+        # Call the Liger fused kernel with OPSM-modified mask
+        loss, metrics = self.liger_grpo_loss(
+            _input=last_hidden_state,
+            lin_weight=unwrapped_model.lm_head.weight,
+            selected_token_ids=completion_ids,
+            attention_mask=loss_mask,
+            advantages=inputs["advantages"],
+            bias=unwrapped_model.lm_head.bias,
+            old_per_token_logps=inputs.get("old_per_token_logps"),
+            ref_per_token_logps=inputs.get("ref_per_token_logps"),
+            vllm_is_ratio=inputs.get("importance_sampling_ratio"),
+        )
+
+        mean_kl = metrics[0] if self.beta != 0.0 else None
+        clip_ratio = metrics[-1]
+
+        mode = "train" if self.model.training else "eval"
+        if self.beta != 0.0:
+            self._metrics[mode]["kl"].append(
+                self.accelerator.gather(mean_kl).mean().item()
+            )
+        self._metrics[mode]["clip_ratio"].append(
+            self.accelerator.gather(clip_ratio).mean().item()
+        )
+        normalizer = (
+            self.current_gradient_accumulation_steps if mode == "train" else 1.0
+        )
+        return loss / normalizer
+
+    def _compute_loss(self, model, inputs):
+        if self.args.skip_zero_advantage_batches and torch.all(
+            inputs["advantages"] == 0
+        ):
+            mode = "train" if self.model.training else "eval"
+            self._metrics[mode]["skipped_zero_adv_batches"].append(1.0)
+            # Create zero loss with grad_fn. DeepSpeed requires grad_fn != None.
+            # With ZeRO-3, parameters are partitioned (shape=[0], requires_grad=False)
+            # so we can't just do `(p * 0).sum()`. Instead, do a tiny forward pass
+            # with a single token to create a proper computation graph.
+            prompt_ids = inputs["prompt_ids"][:1, :1]  # (1, 1)
+            attn = torch.ones_like(prompt_ids)
+            with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
+                out = model(input_ids=prompt_ids, attention_mask=attn)
+            return out.logits.sum() * 0
+        return super()._compute_loss(model, inputs)

src/axolotl/core/trainers/grpo/replay_buffer.py

@@ -0,0 +1,44 @@
+"""Simple replay buffer for storing and sampling high-signal rollout groups."""
+
+import heapq
+
+import torch
+
+
+class ReplayBuffer:
+    """Min-heap replay buffer that keeps the highest-scoring rollout groups.
+    Groups are scored by signal quality (advantage magnitude * reward variance).
+    When sampling, groups are drawn proportional to their scores.
+    """
+
+    def __init__(self, max_size: int):
+        self.max_size = max_size
+        self._heap: list[tuple[float, int, dict]] = []  # min-heap of (score, id, data)
+        self._counter = 0  # unique tiebreaker for heap
+
+    def __len__(self):
+        return len(self._heap)
+
+    def add(self, score: float, data: dict):
+        """Add a group to the buffer. If full, replaces lowest-scoring entry."""
+        if self.max_size <= 0:
+            return
+        self._counter += 1
+        if len(self._heap) < self.max_size:
+            heapq.heappush(self._heap, (score, self._counter, data))
+        elif score > self._heap[0][0]:
+            heapq.heapreplace(self._heap, (score, self._counter, data))
+
+    def sample(self, num_samples: int) -> list[dict] | None:
+        """Sample groups weighted by their scores. Returns None if buffer is empty."""
+        if self.max_size <= 0 or not self._heap:
+            return None
+
+        scores = torch.tensor([item[0] for item in self._heap], dtype=torch.float32)
+        scores = scores.clamp(min=1e-8)  # avoid zero probabilities
+        probs = scores / scores.sum()
+        replacement = num_samples > len(self._heap)
+        indices = torch.multinomial(
+            probs, num_samples, replacement=replacement
+        ).tolist()
+        return [self._heap[i][2] for i in indices]

src/axolotl/core/trainers/grpo/trainer.py

@@ -40,6 +40,7 @@ from trl.trainer.grpo_config import GRPOConfig
 from trl.trainer.grpo_trainer import RewardFunc, nanstd
 from trl.trainer.utils import pad
 
+from axolotl.core.trainers.grpo.fast_async_trainer import FastAsyncGRPOTrainer
 from axolotl.core.trainers.grpo.sampler import SequenceParallelRepeatRandomSampler
 from axolotl.core.trainers.mixins import (
     DistributedParallelMixin,
@@ -66,6 +67,19 @@ class AxolotlGRPOTrainer(
     _tag_names = ["trl", "grpo", "axolotl"]
 
 
+class AxolotlAsyncGRPOTrainer(
+    RngLoaderMixin,
+    SchedulerMixin,
+    OptimizerMixin,
+    OptimizerInitMixin,
+    DistributedParallelMixin,
+    FastAsyncGRPOTrainer,
+):
+    """Extend AsyncGRPOTrainer with axolotl helpers"""
+
+    _tag_names = ["trl", "grpo", "async", "axolotl"]
+
+
 class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     """Extend the base GRPOTrainer for sequence parallelism handling"""
 

src/axolotl/core/trainers/mixins/checkpoints.py

@@ -19,5 +19,4 @@ class CheckpointSaveMixin(Trainer):
                 f"Trainer does not support saving optimizer and scheduler:  {exc}\n"
                 "Optimizer and scheduler states were not saved - resuming from checkpoints "
                 "for this training run will not be possible.",
-                main_process_only=True,
             )

src/axolotl/core/trainers/mixins/optimizer.py

@@ -104,7 +104,7 @@ class OptimizerMixin(Trainer):
 
         return optimizer_grouped_parameters
 
-    def create_optimizer(self):
+    def create_optimizer(self, model=None):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.embedding_lr_scale is None
@@ -112,9 +112,9 @@ class OptimizerMixin(Trainer):
             and self.args.lr_groups is None
             and self.optimizer_cls_and_kwargs is None
         ):
-            return super().create_optimizer()
+            return super().create_optimizer(model=model)
 
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        opt_model = self.model if model is None else model
 
         if (
             not self.optimizer

src/axolotl/core/trainers/mixins/scheduler.py

@@ -25,7 +25,7 @@ class SchedulerMixin(Trainer):
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
 
     def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+        self, num_training_steps: int, optimizer: None | torch.optim.Optimizer = None
     ) -> LRScheduler:
         """
         Set up the scheduler. The optimizer of the trainer must have been set up either before this method is called or
@@ -45,6 +45,13 @@ class SchedulerMixin(Trainer):
             and self.args.cosine_min_lr_ratio is not None
         )
 
+        if optimizer is None:
+            if self.optimizer is None:
+                raise ValueError(
+                    "Optimizer must be set before calling create_scheduler or passed as an argument."
+                )
+            optimizer = self.optimizer
+
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore
             # fmt: on

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f4b5712"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"
 ```
 
 ## Usage
@@ -31,6 +31,7 @@ plugins:
 
 ## Supported Models
 
+- afmoe
 - apertus
 - arcee
 - cohere
@@ -51,11 +52,12 @@ plugins:
 - glm4v
 - glm4v_moe
 - glm_image
+- glm_moe_dsa
 - gpt_oss
 - granite
 - granitemoe
-- granitemoeshared
 - granitemoehybrid
+- granitemoeshared
 - hunyuan_v1_dense
 - hunyuan_v1_moe
 - internvl
@@ -71,25 +73,33 @@ plugins:
 - ministral3
 - mistral
 - mistral3
+- mistral4
 - mixtral
 - mllama
+- nemotron_h
 - olmo
 - olmo2
 - olmo3
+- olmoe
 - phi
 - phi3
 - phi4_multimodal
 - qwen2
-- qwen2_vl
-- qwen2_moe
 - qwen2_5_vl
+- qwen2_moe
+- qwen2_vl
 - qwen3
+- qwen3_5
+- qwen3_5_text
+- qwen3_5_moe
+- qwen3_5_moe_text
 - qwen3_moe
+- qwen3_next
 - qwen3_vl
 - qwen3_vl_moe
-- qwen3_next
-- smollm3
 - seed_oss
+- smollm3
+- step3p5
 - voxtral
 
 ## Citation

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f4b5712"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"`'
 )
 
 
@@ -104,7 +104,7 @@ class CutCrossEntropyPlugin(BasePlugin):
 
     def patch_llama_like(
         self,
-        model_type: str,
+        model_type_to_patch: str,
     ) -> None:
         """
         Generic patch for model architectures with causal lm similar to llama
@@ -112,7 +112,10 @@ class CutCrossEntropyPlugin(BasePlugin):
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
         def patch_generic(
-            maybe_model, patch_options, model_type: str, remote_model_id: str | None
+            maybe_model,
+            patch_options,
+            remote_model_id: str | None,
+            model_type: str,
         ):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
@@ -136,11 +139,13 @@ class CutCrossEntropyPlugin(BasePlugin):
                     f"Error: {str(e)}"
                 ) from e
 
-        if model_type not in PATCH_FNS:
+        if model_type_to_patch not in PATCH_FNS:
             LOG.warning_once(
-                "Setting up generic cce patch for model type: %s", model_type
+                "Setting up generic cce patch for model type: %s", model_type_to_patch
             )
             LOG.warning_once(
-                f"Generic Cut Cross Entropy + {model_type} support is experimental and may not work as expected."
+                f"Generic Cut Cross Entropy + {model_type_to_patch} support is experimental and may not work as expected."
+            )
+            PATCH_FNS[model_type_to_patch] = partial(
+                patch_generic, model_type=model_type_to_patch
             )
-            PATCH_FNS[model_type] = partial(patch_generic, model_type=model_type)

src/axolotl/integrations/kernels/README.md

@@ -0,0 +1,78 @@
+# Kernels Integration
+
+MoE (Mixture of Experts) kernels speed up training for MoE layers and reduce VRAM costs. In transformers v5, `batched_mm` and `grouped_mm` were integrated as built-in options via the `experts_implementation` config kwarg:
+
+```python
+class ExpertsInterface(GeneralInterface):
+    _global_mapping = {
+        "batched_mm": batched_mm_experts_forward,
+        "grouped_mm": grouped_mm_experts_forward,
+    }
+```
+
+In our custom integration, we add support for **ScatterMoE** and **SonicMoE**, which are more efficient and faster than `grouped_mm`.
+
+## Usage
+
+Add the following to your axolotl YAML config:
+
+```yaml
+plugins:
+  - axolotl.integrations.kernels.KernelsPlugin
+
+use_kernels: true
+
+# Choose one (mutually exclusive):
+use_scattermoe: true
+# OR
+use_sonicmoe: true
+```
+
+**Important:** Setting `experts_implementation` is incompatible with custom kernel options.
+
+### SonicMoE installation
+
+**Prerequisites:**
+- NVIDIA Hopper (H100, H200) or Blackwell (B200, GB200) GPU
+- CUDA 12.9+ (13.0+ for B300)
+- PyTorch 2.7+ (2.9.1 recommended)
+- For B300: Triton 3.6.0
+
+```bash
+pip install --ignore-requires-python --no-deps "sonic-moe @ git+https://github.com/Dao-AILab/sonic-moe.git@116e2df0a41874f77fa0ad269ce7df3f0cfcb956" && pip install nvidia-cutlass-dsl==4.4.0 quack-kernels==0.2.5
+```
+
+See the [SonicMoE installation guide](https://github.com/Dao-AILab/sonic-moe?tab=readme-ov-file#-installation) for the latest prerequisite details.
+
+**Note:** Blackwell support is in upstream beta. On Blackwell GPUs, Axolotl automatically sets `USE_QUACK_GEMM=1` to enable the Blackwell kernels.
+
+## How It Works
+
+The `KernelsPlugin` runs before model loading and:
+
+### ScatterMoE
+1. Registers the ScatterMoE kernel from the local `libs/scattermoe_lora` package (includes fused LoRA support via Triton kernels).
+2. Patches the model's `SparseMoeBlock` forward method with the optimized ScatterMoE implementation.
+
+### SonicMoE
+1. Resolves the model's MoE block class(es) from `constants.py`.
+2. Patches the forward method with SonicMoE's optimized kernels and registers a weight converter for the interleaved gate/up projection format.
+3. Supports both softmax->topk and sigmoid->topk routing strategies.
+
+Both paths use the shared `resolve_moe_block_classes` utility in `constants.py` for model-type-to-class resolution.
+
+#### Supported Models
+
+See `constants.py` for the full list of supported model types (Qwen2-MoE, Qwen3-MoE, OLMoE, Mixtral, DeepSeek-V3, GLM-MoE, MiniMax, etc.).
+
+## Limitations
+
+ScatterMoE uses a softmax -> topk routing, so results may be different for some model architectures as baseline (GPT-OSS, etc). Incompatible with `GLM_MOE_DSA` (GLM 5) and `GLM4_MOE_LITE` (GLM 4.7 Flash) at the moment.
+
+SonicMoE supports both softmax->topk and sigmoid->topk routing, covering a wider range of architectures.
+
+ScatterMoE does not work for GLM4.7 Flash (glm4_moe_lite) atm.
+
+## Note on MegaBlocks
+
+We tested [MegaBlocks](https://huggingface.co/kernels-community/megablocks) but were unable to ensure numerical accuracy, so we did not integrate it. It was also incompatible with many newer model architectures in transformers.

src/axolotl/integrations/kernels/args.py

@@ -6,7 +6,18 @@ LOG = get_logger(__name__)
 
 
 class KernelsArgs(BaseModel):
-    use_scattermoe: bool | None = True
+    use_scattermoe: bool | None = None
+    use_sonicmoe: bool | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_mutually_exclusive(cls, data):
+        if data.get("use_scattermoe") and data.get("use_sonicmoe"):
+            raise ValueError(
+                "Cannot use both ScatterMoE and SonicMoE simultaneously. "
+                "Please set only one of `use_scattermoe` or `use_sonicmoe` to true."
+            )
+        return data
 
     @model_validator(mode="before")
     @classmethod
@@ -33,3 +44,16 @@ class KernelsArgs(BaseModel):
             data["experts_implementation"] = "eager"
 
         return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def disable_mlp_kernel(cls, data):
+        if data.get("use_scattermoe") is True or data.get("use_sonicmoe") is True:
+            if data.get("lora_mlp_kernel") is True:
+                LOG.warning(
+                    "Disabling lora_mlp_kernel when using custom MoE kernels due to compatibility issues."
+                )
+                data["lora_mlp_kernel"] = False
+            data["mlp_kernel"] = False
+
+        return data

src/axolotl/integrations/kernels/autotune_callback.py

@@ -0,0 +1,120 @@
+"""Trainer callback for reporting Triton autotune results from scattermoe-lora kernels."""
+
+import logging
+
+import torch
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+LOG = logging.getLogger(__name__)
+
+# Give up looking for autotune data after this many training steps.
+_MAX_POLL_STEP = 5
+
+
+def _get_gpu_info() -> dict:
+    """Return basic GPU identification for the current device."""
+    if not torch.cuda.is_available():
+        return {}
+    try:
+        idx = torch.cuda.current_device()
+        props = torch.cuda.get_device_properties(idx)
+        return {
+            "gpu_name": props.name,
+            "gpu_compute_capability": f"{props.major}.{props.minor}",
+            "gpu_memory_bytes": props.total_memory,
+        }
+    except Exception:  # pylint: disable=broad-exception-caught
+        return {}
+
+
+def _get_smem_capacity() -> dict:
+    """Return shared memory capacity from the runtime lora_ops module."""
+    try:
+        from axolotl.integrations.kernels.autotune_collector import (
+            _find_lora_ops_module,
+        )
+
+        lora_ops = _find_lora_ops_module()
+        if lora_ops is None:
+            return {}
+        fn = getattr(lora_ops, "_get_smem_capacity", None)
+        if fn is None:
+            return {}
+        return {"smem_capacity_bytes": fn()}
+    except Exception:  # pylint: disable=broad-exception-caught
+        return {}
+
+
+class AutotuneReportCallback(TrainerCallback):
+    """Reports Triton kernel autotune selections via telemetry.
+
+    Fires **once** after the first training step completes (step 1), at
+    which point the forward and backward passes have both run and the
+    autotuned kernels have populated their caches.  If for some reason
+    the caches are still empty (e.g. the kernel was never invoked), the
+    callback retries on subsequent steps up to ``_MAX_POLL_STEP`` and
+    then stops polling.
+
+    After reporting (or giving up) every subsequent ``on_step_end``
+    call short-circuits on the ``_reported`` flag — zero hot-path cost.
+    """
+
+    def __init__(self):
+        self._reported = False
+
+    # pylint: disable=unused-argument
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        if self._reported:
+            return
+
+        # Lazy import — Triton / scattermoe kernels may not be installed.
+        from axolotl.integrations.kernels.autotune_collector import (
+            collect_autotune_configs,
+        )
+
+        configs = collect_autotune_configs()
+
+        if not configs:
+            if state.global_step >= _MAX_POLL_STEP:
+                LOG.debug(
+                    "No autotune data found after %d steps; giving up.",
+                    state.global_step,
+                )
+                self._reported = True
+            return
+
+        self._reported = True
+
+        from axolotl.telemetry.manager import TelemetryManager
+
+        telemetry_manager = TelemetryManager.get_instance()
+        if not telemetry_manager.enabled:
+            return
+
+        properties = {
+            "kernel_count": len(configs),
+            "kernels": configs,
+        }
+        properties.update(_get_gpu_info())
+        properties.update(_get_smem_capacity())
+
+        telemetry_manager.send_event(
+            event_type="scattermoe-autotune",
+            properties=properties,
+        )
+
+        LOG.info(
+            "Reported %d scattermoe kernel autotune config(s) to telemetry.",
+            len(configs),
+        )

src/axolotl/integrations/kernels/autotune_collector.py

@@ -0,0 +1,114 @@
+"""Collect Triton autotune results from scattermoe-lora kernels.
+
+This module reads the ``.cache`` attribute from Triton ``@triton.autotune``
+decorated kernel objects and returns structured dicts describing the selected
+configurations.  It has **no** telemetry dependency — callers decide what to
+do with the data.
+"""
+
+import logging
+import sys
+from types import ModuleType
+from typing import Any
+
+LOG = logging.getLogger(__name__)
+
+# (human-readable name, attribute on the lora_ops module)
+_KERNEL_REGISTRY: list[tuple[str, str]] = [
+    ("scatter2scatter_lora_fwd", "_scatter2scatter_lora"),
+    ("scatter2scatter_lora_dX", "_scatter2scatter_lora_dX"),
+    ("group_bwd_lora", "_group_bwd_lora"),
+    ("group_bwd_lora_fused", "_group_bwd_lora_fused"),
+]
+
+# The autotune key declared on every kernel: key=["M", "N", "K"]
+_KEY_NAMES: list[str] = ["M", "N", "K"]
+
+
+def _parse_key_tuple(key_tuple: tuple) -> dict[str, Any]:
+    """Turn the autotune cache key tuple into a labelled dict.
+
+    Triton builds the cache key from the values of the declared ``key``
+    args (``M``, ``N``, ``K``) followed by dtype signature elements.
+    We label the first three and store the rest under ``_extra``.
+    """
+    result: dict[str, Any] = {}
+    for i, name in enumerate(_KEY_NAMES):
+        if i < len(key_tuple):
+            result[name] = key_tuple[i]
+    if len(key_tuple) > len(_KEY_NAMES):
+        result["_extra"] = [str(v) for v in key_tuple[len(_KEY_NAMES) :]]
+    return result
+
+
+def _find_lora_ops_module() -> ModuleType | None:
+    """Locate the *runtime* ``lora_ops`` module in ``sys.modules``.
+
+    The HF ``kernels`` package loads ``scattermoe_lora`` via
+    ``import_from_path`` which registers it in ``sys.modules`` under a
+    hash-suffixed name (e.g. ``scattermoe_lora_a1b2c3d4``).  A normal
+    import (``from axolotl.integrations.kernels...``) would create a
+    *separate* module instance whose kernel objects have empty
+    ``.cache`` dicts because autotuning ran on the runtime copy.
+
+    We search ``sys.modules`` for any module whose name contains
+    ``lora_ops`` and that has the ``_scatter2scatter_lora`` kernel
+    attribute — that is the runtime copy with populated caches.
+    """
+    for name, module in list(sys.modules.items()):
+        if (
+            module is not None
+            and "lora_ops" in name
+            and hasattr(module, "_scatter2scatter_lora")
+        ):
+            return module
+    return None
+
+
+def collect_autotune_configs() -> list[dict[str, Any]]:
+    """Read autotune caches from the four scattermoe-lora kernels.
+
+    Returns a (possibly empty) list of dicts, each containing:
+
+    * ``kernel`` – human-readable kernel name
+    * ``key``    – dict with the ``M``/``N``/``K`` problem dimensions
+    * ``config`` – dict with the selected tile sizes, ``num_warps``,
+      and ``num_stages``
+
+    Returns ``[]`` if the kernel module cannot be found or if no
+    autotune cache entries exist yet.
+    """
+    lora_ops = _find_lora_ops_module()
+    if lora_ops is None:
+        LOG.debug(
+            "lora_ops module not found in sys.modules; skipping autotune collection"
+        )
+        return []
+
+    results: list[dict[str, Any]] = []
+
+    for friendly_name, attr_name in _KERNEL_REGISTRY:
+        kernel_fn = getattr(lora_ops, attr_name, None)
+        if kernel_fn is None:
+            continue
+
+        cache = getattr(kernel_fn, "cache", None)
+        if not cache:
+            continue
+
+        for key_tuple, config in cache.items():
+            config_dict = dict(config.kwargs)
+            config_dict["num_warps"] = config.num_warps
+            config_dict["num_stages"] = config.num_stages
+            if getattr(config, "num_ctas", None) is not None:
+                config_dict["num_ctas"] = config.num_ctas
+
+            results.append(
+                {
+                    "kernel": friendly_name,
+                    "key": _parse_key_tuple(key_tuple),
+                    "config": config_dict,
+                }
+            )
+
+    return results

src/axolotl/integrations/kernels/constants.py

@@ -0,0 +1,70 @@
+"""
+Supported MoE block mappings for kernel integrations.
+
+Maps model_type to the SparseMoeBlock class name(s) in transformers.
+Used by both ScatterMoE and SonicMoE kernel paths.
+
+Values can be a single class name (str) or a list of class names for models
+with multiple MoE block types (e.g. qwen3_omni_moe has Thinker + Talker).
+"""
+
+import importlib
+
+SPARSE_MOE_BLOCK = {
+    # softmax -> topk routing
+    "qwen2_moe": "Qwen2MoeSparseMoeBlock",
+    "qwen3_moe": "Qwen3MoeSparseMoeBlock",
+    "qwen3_5_moe": "Qwen3_5MoeSparseMoeBlock",
+    "qwen3_next": "Qwen3NextSparseMoeBlock",
+    "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
+    # qwen3_omni_moe: Thinker (standard) + Talker (shared experts + shared_expert_gate)
+    "qwen3_omni_moe": [
+        "Qwen3OmniMoeThinkerTextSparseMoeBlock",
+        "Qwen3OmniMoeTalkerTextSparseMoeBlock",
+    ],
+    "olmoe": "OlmoeSparseMoeBlock",
+    "mixtral": "MixtralSparseMoeBlock",
+    "minimax": "MiniMaxSparseMoeBlock",
+    # softmax -> topk routing (with group-based expert selection)
+    "mistral4": "Mistral4MoE",
+    # sigmoid -> topk routing (with group-based expert selection)
+    "glm_moe_dsa": "GlmMoeDsaMoE",
+    "deepseek_v3": "DeepseekV3MoE",
+    "glm4_moe": "Glm4MoeMoE",
+    "glm4_moe_lite": "Glm4MoeLiteMoE",
+    "glm4v_moe": "Glm4vMoeTextMoE",
+    # sigmoid -> topk routing (no group selection)
+    "minimax_m2": "MiniMaxM2SparseMoeBlock",
+    # Models below need custom routing (not yet implemented):
+    # "ernie4_5_moe": "Ernie4_5_MoeSparseMoeBlock",  # softmax->topk, e_score_correction_bias between softmax and topk
+    # "deepseek_v2": "DeepseekV2Moe",  # softmax->topk, group_limited_greedy, different attr names (num_group)
+    # "hunyuan_v1_moe": "HunYuanMoEV1Moe",  # softmax->topk, gate.wg (not gate.weight), scatter routing
+    # "gpt_oss": "GptOssMLP",  # topk->softmax, transposed layout [E,H,2*I], custom GLU, expert biases
+}
+
+
+def resolve_moe_block_classes(model_type: str):
+    """Resolve all MoE block classes from transformers for the given model type.
+
+    Returns a list of classes (one for most models, multiple for models with
+    distinct MoE block types like qwen3_omni_moe).
+    """
+    entry = SPARSE_MOE_BLOCK.get(model_type)
+    if entry is None:
+        raise ValueError(
+            f"Unsupported MoE model type '{model_type}'. "
+            f"Supported types: {list(SPARSE_MOE_BLOCK.keys())}"
+        )
+
+    cls_names = entry if isinstance(entry, list) else [entry]
+    module_path = f"transformers.models.{model_type}.modeling_{model_type}"
+    module = importlib.import_module(module_path)
+
+    classes = []
+    for cls_name in cls_names:
+        moe_cls = getattr(module, cls_name, None)
+        if moe_cls is None:
+            raise ValueError(f"Could not find class '{cls_name}' in '{module_path}'")
+        classes.append(moe_cls)
+
+    return classes

src/axolotl/integrations/kernels/libs/scattermoe_lora/__init__.py

@@ -0,0 +1,18 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) Axolotl AI
+# Licensed under the Apache License, Version 2.0
+
+from . import layers
+from .lora_ops import ParallelExperts
+from .parallel_experts import flatten_sort_count, parallel_linear
+from .parallel_linear_lora import ScatterMoELoRA, parallel_linear_lora
+
+__all__ = [
+    "layers",
+    "ParallelExperts",
+    "flatten_sort_count",
+    "parallel_linear",
+    "ScatterMoELoRA",
+    "parallel_linear_lora",
+    "lora_ops",
+]

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/__init__.py

@@ -0,0 +1,12 @@
+# SPDX-License-Identifier: Apache-2.0
+#
+# Original work Copyright (c) Shawn Tan and ScatterMoE Contributors
+# Adapted from https://github.com/shawntan/scattermoe
+# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
+#
+# Modifications and LoRA adaptation Copyright (c) Axolotl AI
+# Licensed under the Apache License, Version 2.0
+
+from . import lora_ops, ops
+
+__all__ = ["ops", "lora_ops"]

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/ops.py

@@ -0,0 +1,645 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from https://github.com/shawntan/scattermoe
+# Copyright (c) Shawn Tan and ScatterMoE Contributors
+# Licensed under the Apache License, Version 2.0
+# See https://github.com/shawntan/scattermoe/blob/main/LICENSE
+
+from typing import Optional
+
+import torch
+import triton
+import triton.language as tl
+
+BLOCK_M = 128
+ALLOW_TF32 = True
+
+
+@triton.jit
+def _compute_expert_block(
+    E_idx,
+    E_mask,
+    M_in_idx,
+    N_block,
+    N_mask,
+    X_ptr,
+    stride_xm,
+    stride_xk,
+    W_ptr,
+    stride_we,
+    stride_wk,
+    stride_wn,
+    K,
+    acc,
+    no_k_mask,
+    BLOCK_K,
+    allow_tf32=True,
+):
+    K_block = tl.arange(0, BLOCK_K)
+    X_blk_ptrs = X_ptr + M_in_idx[:, None] * stride_xm + K_block[None, :] * stride_xk
+    W_blk_ptrs = (
+        W_ptr
+        + K_block[:, None] * stride_wk
+        + N_block[None, :] * stride_wn
+        + E_idx * stride_we
+    )
+    iters = tl.cdiv(K, BLOCK_K)
+
+    for K_block_id in range(iters):
+        if no_k_mask:
+            x = tl.load(X_blk_ptrs, mask=E_mask[:, None])
+            w = tl.load(W_blk_ptrs, mask=N_mask[None, :])
+        else:
+            K_mask = (K_block_id * BLOCK_K + K_block) < K
+            x = tl.load(X_blk_ptrs, mask=E_mask[:, None] & K_mask[None, :])
+            w = tl.load(W_blk_ptrs, mask=K_mask[:, None] & N_mask[None, :])
+
+        X_blk_ptrs += BLOCK_K * stride_xk
+        W_blk_ptrs += BLOCK_K * stride_wk
+        acc = tl.dot(x, w, acc, allow_tf32=allow_tf32)
+    return acc
+
+
+def _scatter2scatter_configs():
+    return [
+        triton.Config({"BLOCK_N": 128, "BLOCK_K": 32}, num_stages=4, num_warps=4),
+    ]
+
+
+@triton.autotune(
+    configs=_scatter2scatter_configs(),
+    key=["M", "N", "K"],
+)
+@triton.heuristics(
+    {
+        "NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0,
+        "NO_N_MASK": lambda args: (args["N"] % args["BLOCK_N"]) == 0,
+    }
+)
+@triton.jit
+def _scatter2scatter(
+    X_ptr,
+    stride_xm: tl.constexpr,
+    stride_xk: tl.constexpr,
+    W_ptr,
+    stride_we,
+    stride_wk: tl.constexpr,
+    stride_wn: tl.constexpr,
+    Y_ptr,
+    stride_ym: tl.constexpr,
+    stride_yn: tl.constexpr,
+    B_ptr,
+    stride_be: tl.constexpr,
+    stride_bn: tl.constexpr,
+    grouped_idx_ptr,
+    expert_idxs_ptr,
+    # block_start_idx_ptr,
+    FAN_OUT: tl.constexpr,
+    M,
+    K: tl.constexpr,
+    N: tl.constexpr,
+    E: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    ACC_TYPE: tl.constexpr,
+    # OUT_M,
+    allow_tf32: tl.constexpr,
+    x_grouped: tl.constexpr,
+    y_grouped: tl.constexpr,
+    NO_K_MASK: tl.constexpr,
+    NO_N_MASK: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+
+    N_BLOCK_COUNT = tl.cdiv(N, BLOCK_N)
+    M_block_id = pid // N_BLOCK_COUNT
+    N_block_id = pid % N_BLOCK_COUNT
+
+    M_block = M_block_id * BLOCK_M + tl.arange(0, BLOCK_M)
+    N_block = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
+    N_mask = N_block < N
+    M_boundary_mask = M_block < (FAN_OUT * M)
+    E_idxs = tl.load(expert_idxs_ptr + M_block, mask=M_boundary_mask, other=E)
+
+    no_k_mask = K % BLOCK_K == 0
+
+    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
+    E_first_idx = tl.min(E_idxs)
+    E_last_idx = tl.minimum(tl.max(E_idxs), E - 1)
+    M_idx = tl.load(grouped_idx_ptr + M_block, mask=M_boundary_mask).to(tl.int32)
+    for E_idx in range(E_first_idx, E_last_idx + 1):
+        E_mask = E_idxs == E_idx
+        E_M_idx = M_idx
+        if x_grouped:
+            M_in_idx = M_block
+        else:
+            M_in_idx = E_M_idx // FAN_OUT
+        acc = _compute_expert_block(
+            E_idx,
+            E_mask,
+            M_in_idx,
+            N_block,
+            N_mask,
+            X_ptr,
+            stride_xm,
+            stride_xk,
+            W_ptr,
+            stride_we,
+            stride_wk,
+            stride_wn,
+            K,
+            acc,
+            no_k_mask,
+            BLOCK_K,
+            allow_tf32=allow_tf32,
+        )
+
+    if B_ptr is not None:
+        B_blk_ptrs = B_ptr + E_idxs[:, None] * stride_be + N_block[None, :] * stride_bn
+        acc += tl.load(B_blk_ptrs, mask=M_boundary_mask[:, None] & N_mask[None, :])
+
+    if y_grouped:
+        M_out_idx = M_block
+    else:
+        M_out_idx = M_idx
+    Y_blk_ptrs = Y_ptr + (M_out_idx[:, None] * stride_ym + N_block[None, :] * stride_yn)
+    tl.store(Y_blk_ptrs, acc, mask=M_boundary_mask[:, None] & N_mask[None, :])
+
+
+def scatter2scatter(
+    X,
+    W,
+    sorted_expert_idxs,
+    sorted_scattered_idxs,
+    k,
+    b=None,
+    x_grouped=False,
+    y_grouped=False,
+    out=None,
+):
+    assert sorted_scattered_idxs.size(0) == sorted_expert_idxs.size(0)
+    assert sorted_scattered_idxs.size(0) == X.size(0) * k
+    # Pre-kernel setup
+    y_dim = W.size(-1)
+    L_scattered = sorted_expert_idxs.size(0)
+    if out is None:
+        output = torch.empty((L_scattered, y_dim), device=X.device, dtype=X.dtype)
+    else:
+        assert out.size(0) == L_scattered and out.size(1) == y_dim
+        output = out
+
+    scatter2scatter_compileable(
+        output,
+        W,
+        X,
+        k,
+        sorted_expert_idxs,
+        sorted_scattered_idxs,
+        b,
+        x_grouped,
+        y_grouped,
+    )
+    return output
+
+
+@torch.library.custom_op("scattermoe::scatter2scatter", mutates_args={"output"})
+def scatter2scatter_compileable(
+    output: torch.Tensor,
+    W: torch.Tensor,
+    X: torch.Tensor,
+    k: int,
+    sorted_expert_idxs: torch.Tensor,
+    sorted_scattered_idxs: torch.Tensor,
+    b: Optional[torch.Tensor],
+    x_grouped: bool,
+    y_grouped: bool,
+) -> None:
+    def grid(META):
+        grid_num = (
+            triton.cdiv(sorted_expert_idxs.size(0), META["BLOCK_M"])
+            * triton.cdiv(META["N"], META["BLOCK_N"]),
+        )
+        return grid_num
+
+    if b is None:
+        b = None
+        stride_be = stride_bn = 0
+    else:
+        stride_be, stride_bn = b.stride()
+
+    _scatter2scatter[grid](
+        # X_ptr, stride_xm, stride_xk,
+        X,
+        X.stride(0),
+        X.stride(1),
+        # W_ptr, stride_we, stride_wk, stride_wn,
+        W,
+        W.stride(0),
+        W.stride(1),
+        W.stride(2),
+        # Y_ptr, stride_ym, stride_yn,
+        output,
+        output.stride(0),
+        output.stride(1),
+        # B_ptr, stride_be, stride_bn
+        b,
+        stride_be,
+        stride_bn,
+        grouped_idx_ptr=sorted_scattered_idxs,
+        expert_idxs_ptr=sorted_expert_idxs,
+        # block_start_idx_ptr=padded_block_idxs,
+        FAN_OUT=k,
+        M=X.size(0),
+        K=X.size(1),
+        N=output.size(1),
+        E=W.size(0),
+        BLOCK_M=BLOCK_M,
+        ACC_TYPE=tl.float32,
+        allow_tf32=ALLOW_TF32,
+        x_grouped=x_grouped,
+        y_grouped=y_grouped,
+    )
+
+
+def _config_XtY():
+    return [
+        triton.Config(
+            {"BLOCK_N": 128, "BLOCK_K": 128, "BLOCK_M": 32}, num_stages=4, num_warps=4
+        ),
+    ]
+
+
+def group_bwd_W(DY, X, expert_offsets, E, has_bias=False):
+    DWt = torch.zeros((E, DY.size(-1), X.size(-1)), device=DY.device, dtype=DY.dtype)
+    DW = DWt.permute(0, 2, 1)
+    if has_bias:
+        Db = torch.zeros((E, DY.size(-1)), device=DY.device, dtype=DY.dtype)
+    else:
+        Db = None
+    groupXtY_compileable(E, DW, Db, DY, X, expert_offsets)
+    return DW, Db
+
+
+@torch.library.custom_op("scattermoe::groupXtY", mutates_args={"DW", "Db"})
+def groupXtY_compileable(
+    E: int,
+    DW: torch.Tensor,
+    Db: Optional[torch.Tensor],
+    DY: torch.Tensor,
+    X: torch.Tensor,
+    expert_offsets: torch.Tensor,
+) -> None:
+    def grid(META):
+        grid = (
+            E * triton.cdiv(META["K"], META["BLOCK_K"]),
+            triton.cdiv(META["N"], META["BLOCK_N"]),
+        )
+        return grid
+
+    if Db is None:
+        stride_dbe = 0
+        stride_dbn = 0
+    else:
+        stride_dbe, stride_dbn = Db.stride()
+
+    _groupXtY[grid](
+        # DY_ptr, stride_dym, stride_dyk,
+        DY,
+        DY.stride(0),
+        DY.stride(1),
+        # X_ptr, stride_xm, stride_xn,
+        X,
+        X.stride(0),
+        X.stride(1),
+        # DW_ptr, stride_dwe, stride_dwk, stride_dwn,
+        DW,
+        DW.stride(0),
+        DW.stride(1),
+        DW.stride(2),
+        # Db_ptr, stride_dwe, stride_dbn,
+        Db,
+        stride_dbe,
+        stride_dbn,
+        # expert_offsets_ptr,
+        expert_offsets,
+        # K: tl.constexpr, N: tl.constexpr,
+        M=DY.size(0),
+        N=DY.size(-1),
+        K=X.size(-1),
+        # ACC_TYPE: tl.constexpr,
+        ACC_TYPE=tl.float32,
+        allow_tf32=ALLOW_TF32,
+    )
+
+
+@triton.autotune(
+    configs=_config_XtY(),
+    key=["M", "N", "K"],
+)
+@triton.heuristics(
+    {
+        "NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0,
+        "NO_N_MASK": lambda args: (args["N"] % args["BLOCK_N"]) == 0,
+    }
+)
+@triton.jit
+def _groupXtY(
+    DY_ptr,
+    stride_dym,
+    stride_dyk,
+    X_ptr,
+    stride_xm,
+    stride_xn,
+    DW_ptr,
+    stride_dwe,
+    stride_dwk,
+    stride_dwn,
+    Db_ptr,
+    stride_dbe,
+    stride_dbn,
+    expert_offsets_ptr,
+    M,
+    K: tl.constexpr,
+    N: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    ACC_TYPE: tl.constexpr,
+    allow_tf32: tl.constexpr,
+    NO_K_MASK: tl.constexpr,
+    NO_N_MASK: tl.constexpr,
+):
+    pid0 = tl.program_id(axis=0)
+    pid1 = tl.program_id(axis=1)
+    num0 = tl.num_programs(0)
+    num1 = tl.num_programs(1)
+    # pid1, pid0 = tl.swizzle2d(pid1, pid0, num1, num0, 128)
+    pid0, pid1 = tl.swizzle2d(pid0, pid1, num0, num1, 4)
+
+    K_BLOCK_COUNT = tl.cdiv(K, BLOCK_K)
+    E_idx = pid0 // K_BLOCK_COUNT
+    K_block_id = pid0 % K_BLOCK_COUNT
+    N_block_id = pid1
+
+    if E_idx == 0:
+        start_idx = 0
+    else:
+        start_idx = tl.load(expert_offsets_ptr + E_idx - 1).to(tl.int32)
+    end_idx = tl.load(expert_offsets_ptr + E_idx).to(tl.int32)
+
+    if end_idx > start_idx:
+        M_block = tl.max_contiguous(start_idx + tl.arange(0, BLOCK_M), BLOCK_M)
+
+        K_block = K_block_id * BLOCK_K + tl.arange(0, BLOCK_K)
+        K_mask = K_block < K
+        K_block = tl.max_contiguous(tl.multiple_of(K_block % K, BLOCK_K), BLOCK_K)
+
+        N_block = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
+        N_mask = N_block < N
+        N_block = tl.max_contiguous(tl.multiple_of(N_block % N, BLOCK_N), BLOCK_N)
+
+        M_idxs = M_block
+        xt_blk_ptrs = X_ptr + K_block[:, None] * stride_xn + M_idxs[None, :] * stride_xm
+        dy_blk_ptrs = (
+            DY_ptr + M_idxs[:, None] * stride_dym + N_block[None, :] * stride_dyk
+        )
+        if (Db_ptr is not None) and (K_block_id == 0):
+            _xty_and_bias(
+                E_idx,
+                start_idx,
+                end_idx,
+                M_block,
+                K_block,
+                K_mask,
+                N_block,
+                N_mask,
+                dy_blk_ptrs,
+                stride_dym,
+                xt_blk_ptrs,
+                stride_xm,
+                DW_ptr,
+                stride_dwe,
+                stride_dwk,
+                stride_dwn,
+                Db_ptr,
+                stride_dbe,
+                stride_dbn,
+                BLOCK_M,
+                BLOCK_N,
+                BLOCK_K,
+                ACC_TYPE,
+                allow_tf32,
+                NO_K_MASK,
+                NO_N_MASK,
+                compute_bias=True,
+            )
+        else:
+            _xty_and_bias(
+                E_idx,
+                start_idx,
+                end_idx,
+                M_block,
+                K_block,
+                K_mask,
+                N_block,
+                N_mask,
+                dy_blk_ptrs,
+                stride_dym,
+                xt_blk_ptrs,
+                stride_xm,
+                DW_ptr,
+                stride_dwe,
+                stride_dwk,
+                stride_dwn,
+                Db_ptr,
+                stride_dbe,
+                stride_dbn,
+                BLOCK_M,
+                BLOCK_N,
+                BLOCK_K,
+                ACC_TYPE,
+                allow_tf32,
+                NO_K_MASK,
+                NO_N_MASK,
+                compute_bias=False,
+            )
+
+
+@triton.jit
+def _xty_and_bias(
+    E_idx,
+    start_idx,
+    end_idx,
+    M_block,
+    K_block,
+    K_mask,
+    N_block,
+    N_mask,
+    dy_blk_ptrs,
+    stride_dym,
+    xt_blk_ptrs,
+    stride_xm,
+    DW_ptr,
+    stride_dwe,
+    stride_dwk,
+    stride_dwn,
+    Db_ptr,
+    stride_dbe,
+    stride_dbn,
+    BLOCK_M,
+    BLOCK_N,
+    BLOCK_K,
+    ACC_TYPE,
+    allow_tf32,
+    NO_K_MASK,
+    NO_N_MASK,
+    compute_bias: tl.constexpr,
+):
+    if compute_bias:
+        db_acc = tl.zeros((BLOCK_N,), dtype=ACC_TYPE)
+    else:
+        db_acc = None
+
+    acc = tl.zeros((BLOCK_K, BLOCK_N), dtype=ACC_TYPE)
+    iters = tl.cdiv(end_idx - start_idx, BLOCK_M)
+    for i in range(0, iters):
+        M_mask = (i * BLOCK_M + M_block) < end_idx
+        if NO_K_MASK:
+            xt = tl.load(xt_blk_ptrs, mask=M_mask[None, :])
+        else:
+            xt = tl.load(xt_blk_ptrs, mask=K_mask[:, None] & M_mask[None, :])
+        if NO_N_MASK:
+            dy = tl.load(dy_blk_ptrs, mask=M_mask[:, None])
+        else:
+            dy = tl.load(dy_blk_ptrs, mask=M_mask[:, None] & N_mask[None, :])
+
+        acc += tl.dot(xt, dy, out_dtype=ACC_TYPE, allow_tf32=allow_tf32)
+
+        xt_blk_ptrs += BLOCK_M * stride_xm
+        dy_blk_ptrs += BLOCK_M * stride_dym
+
+        if compute_bias:
+            db_acc += tl.sum(dy, axis=0)
+
+    DW_blk_ptrs = (
+        DW_ptr
+        + E_idx * stride_dwe
+        + K_block[:, None] * stride_dwk
+        + N_block[None, :] * stride_dwn
+    )
+    acc = acc.to(DW_blk_ptrs.dtype.element_ty)
+    tl.store(DW_blk_ptrs, acc, mask=K_mask[:, None] & N_mask[None, :])
+    if compute_bias:
+        Db_blk_ptrs = Db_ptr + E_idx * stride_dbe + N_block * stride_dbn
+        tl.store(Db_blk_ptrs, db_acc, mask=N_mask)
+
+
+def _config_grouping():
+    return [
+        triton.Config({"BLOCK_N": 256, "BLOCK_K": 128}, num_stages=4, num_warps=4),
+        # triton.Config({'BLOCK_N': 128, 'BLOCK_K': 64}, num_stages=4, num_warps=4),
+        # triton.Config({'BLOCK_N': 64, 'BLOCK_K': 32}, num_stages=4, num_warps=4),
+    ]
+
+
+def group(A, sorted_expert_idxs, coeff=None, fan_out=1, out=None):
+    N = sorted_expert_idxs.size(0)
+    K = A.size(1)
+    assert A.size(0) * fan_out == N
+    if out is not None:
+        Y = out
+    else:
+        Y = torch.empty((N, K), dtype=A.dtype, device=A.device)
+    group_compileable(A, K, N, Y, coeff, coeff is not None, fan_out, sorted_expert_idxs)
+    return Y
+
+
+@torch.library.custom_op("scattermoe::group", mutates_args={"Y"})
+def group_compileable(
+    A: torch.Tensor,
+    K: int,
+    N: int,
+    Y: torch.Tensor,
+    coeff: Optional[torch.Tensor],
+    has_coeff: bool,
+    fan_out: int,
+    sorted_expert_idxs: torch.Tensor,
+) -> None:
+    def grid(META):
+        grid_num = (triton.cdiv(META["N"], META["BLOCK_N"]),)
+        return grid_num
+
+    _group[grid](
+        # A_ptr, stride_an, stride_ai,
+        A,
+        A.stride(0),
+        A.stride(1),
+        has_coeff,
+        coeff,
+        fan_out,
+        # Y_ptr, stride_yn, stride_yk,
+        Y,
+        Y.stride(0),
+        Y.stride(1),
+        # grouped_idx_ptr,
+        sorted_expert_idxs,
+        # N: tl.constexpr, K: tl.constexpr,
+        N,
+        K,
+    )
+
+
+@triton.autotune(configs=_config_grouping(), key=["K"])
+@triton.heuristics({"NO_K_MASK": lambda args: (args["K"] % args["BLOCK_K"]) == 0})
+@triton.jit
+def _group(
+    src_ptr,
+    stride_sn,
+    stride_sk,
+    has_coeff: tl.constexpr,
+    coeff_ptr,
+    FAN_OUT: tl.constexpr,
+    tgt_ptr,
+    stride_tn,
+    stride_ti,
+    grouped_idx_ptr,
+    N,
+    K: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    NO_K_MASK: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+
+    N_block_id = pid
+    N_blk = N_block_id * BLOCK_N + tl.arange(0, BLOCK_N)
+    N_mask = N_blk < N
+    N_blk = tl.max_contiguous(tl.multiple_of(N_blk % N, BLOCK_N), BLOCK_N)
+    N_idx = tl.load(grouped_idx_ptr + N_blk, mask=N_mask, other=0)
+
+    K_blk = tl.arange(0, BLOCK_K)
+    src_blk_ptrs = (
+        src_ptr + (N_idx // FAN_OUT)[:, None] * stride_sn + K_blk[None, :] * stride_sk
+    )
+    tgt_blk_ptrs = tgt_ptr + N_blk[:, None] * stride_tn + K_blk[None, :] * stride_ti
+
+    if has_coeff:
+        c = tl.load(coeff_ptr + N_idx, mask=N_mask)[:, None]
+
+    iters = tl.cdiv(K, BLOCK_K)
+    for i in range(0, iters):
+        if NO_K_MASK or i < iters - 1:
+            block = tl.load(src_blk_ptrs, mask=N_mask[:, None])
+            if has_coeff:
+                block *= c
+            tl.store(tgt_blk_ptrs, block, mask=N_mask[:, None])
+
+        else:
+            K_mask = (i * BLOCK_K + K_blk) < K
+            mask = N_mask[:, None] & K_mask[None, :]
+            block = tl.load(src_blk_ptrs, mask=mask)
+            if has_coeff:
+                block *= c
+            tl.store(tgt_blk_ptrs, block, mask=mask)
+        src_blk_ptrs += BLOCK_K * stride_sk
+        tgt_blk_ptrs += BLOCK_K * stride_ti