release tag (#2799)

* kd fixes

* fix collator setup

* fix input args

* better handling to drop string fields for kd with raw dataset

* kd trainer has kd temp as part of the init

* drop top_k before softmax

* simplfy and remove zscore

* WIP chunked KD loss with autograd wrapper

* more fixes and liger-type chunked loss

* collator cls for plugins

* remove debugging

* additional plugin collator kwargs, don't scale up kd loss by t^2

* don't need temp arg to distill method

* online kd wip

* add close to comment block

* suport sampling params/max new tokens

* handle when no custom collator is used in plugins

* logsumexp trick:

* fix check

* shift off the first empty token

* fix length of padding

* use max not min

* temp scale kd loss at end

* support for dynamic plugin training args mixins and symmetric kl

* chore: lint

* fix trainer callback base class

* Fix decay

* accept compressed responses for smaller wire payload

* post-rebase lint

* more KD updates

* increase hyperparams_count for gradients for added normalize_topk

* fix to remove attention_mask

* rename vars for consistency

* fix rebase issues

* default to dropping last batch in multipack batch sampler

* improve handling of train len

* init collator_cls_and_kwargs

* explicit drop_last=False when checking for multipack completeness

* use separate v2 loader for kd

* fix kd tests to use subprocess so it picks up kd training args

* default value for kd_beta arg

* use updated dataset for ci

* longer timeout for e2e

.github/workflows/base.yml

@@ -16,60 +16,63 @@ on:
 jobs:
   build-base:
     if: github.repository_owner == 'axolotl-ai-cloud'
+    timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
-    runs-on: axolotl-gpu-runner
+    runs-on: ubuntu-latest-m
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.4.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.7.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
           - cuda: "128"
             cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.7.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: nightly
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: next
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base-nightly"
+#          # "next" is for release candidates of pytorch
+#          - cuda: "128"
+#            cuda_version: 12.8.1
+#            cudnn_version: ""
+#            python_version: "3.11"
+#            pytorch: next
+#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+#            dockerfile: "Dockerfile-base-next"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -91,7 +94,60 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
+          file: ./docker/${{ matrix.dockerfile }}
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          labels: ${{ steps.metadata.outputs.labels }}
+          build-args: |
+            CUDA_VERSION=${{ matrix.cuda_version }}
+            CUDNN_VERSION=${{ matrix.cudnn_version }}
+            CUDA=${{ matrix.cuda }}
+            PYTHON_VERSION=${{ matrix.python_version }}
+            PYTORCH_VERSION=${{ matrix.pytorch }}
+            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
+  build-base-uv:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    timeout-minutes: 480
+    runs-on: ubuntu-latest-m
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: "126"
+            cuda_version: 12.6.3
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.6.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.7.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Docker metadata
+        id: metadata
+        uses: docker/metadata-action@v5
+        with:
+          images: |
+            axolotlai/axolotl-base-uv
+      - name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Build
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./docker/${{ matrix.dockerfile }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/lint.yml

@@ -9,6 +9,7 @@ on:
        - '.github/workflows/*.yml'
        - "*.[q]md"
        - "examples/**/*.y[a]?ml"
+       - ".pre-commit-config.yaml"
   workflow_dispatch:
 
 jobs:

.github/workflows/main.yml

@@ -15,11 +15,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -34,8 +29,13 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.0
-            axolotl_extras: vllm
+            pytorch: 2.7.1
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -67,6 +67,7 @@ jobs:
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
+            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
@@ -82,11 +83,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -101,7 +97,12 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.7.1
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

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

@@ -3,12 +3,13 @@ name: docker-multigpu-tests-biweekly
 on:
   pull_request:
     paths:
-      - 'tests/e2e/multigpu/*.py'
+      - 'tests/e2e/multigpu/**.py'
       - 'requirements.txt'
       - 'setup.py'
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
       - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
+      - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
@@ -32,13 +33,6 @@ jobs:
             axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:  # no vllm support for 2.4.1
-            num_gpus: 2
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -49,7 +43,7 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.7.1
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
@@ -65,7 +59,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

.github/workflows/nightlies.yml

@@ -12,11 +12,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -70,11 +65,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/precommit-autoupdate.yml

@@ -25,7 +25,6 @@ jobs:
           pre-commit autoupdate
           if [[ -n $(git status --porcelain) ]]; then
             echo "changes=true" >> $GITHUB_OUTPUT
-            git diff .pre-commit-config.yaml > pre-commit-update.diff
           fi
 
       - name: Create Pull Request
@@ -39,11 +38,3 @@ jobs:
           commit-message: "chore: update pre-commit hooks"
           body: |
             Automated PR to update pre-commit hooks to their latest versions.
-
-            <details>
-            <summary>Changes:</summary>
-
-            ```diff
-            ${{ steps.update.outputs.diff }}
-            ```
-            </details>

.github/workflows/preview-docs.yml

@@ -4,6 +4,12 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
 
+    # Run the workflow only when one of these files changes
+    paths:
+      - '**/*.md'      # any Markdown file
+      - '**/*.qmd'     # any Quarto file
+      - '_quarto.yaml'
+
 permissions:
   checks: write
   contents: write

.github/workflows/tests-nightly.yml

@@ -18,15 +18,102 @@ jobs:
         env:
           SKIP: no-commit-to-branch
 
+  preload-cache:
+    name: Preload HF cache
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python_version: ["3.11"]
+        pytorch_version: ["2.6.0"]
+    timeout-minutes: 20
+
+    env:
+      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4
+
+      - name: Restore HF cache
+        id: hf-cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            /home/runner/.cache/huggingface/hub/datasets--*
+            /home/runner/.cache/huggingface/hub/models--*
+          key: ${{ runner.os }}-hf-hub-cache-v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python_version }}
+          cache: 'pip' # caching pip dependencies
+
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+
+      - name: Install PyTorch
+        run: |
+          pip3 install torch==${{ matrix.pytorch_version }}
+
+      - name: Install dependencies
+        run: |
+          pip3 show torch
+          pip3 install --no-build-isolation -U -e .
+          python scripts/unsloth_install.py | sh
+          python scripts/cutcrossentropy_install.py | sh
+          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+
+      - name: Make sure PyTorch version wasn't clobbered
+        run: |
+          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
+
+      - name: Ensure axolotl CLI was installed
+        run: |
+          axolotl --help
+
+      - name: Pre-Download dataset fixture
+        run: |
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+
+      - name: Run tests
+        run: |
+          pytest -v tests/conftest.py
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          files: ./coverage.xml
+          flags: unittests,pytorch-${{ matrix.pytorch_version }}
+          fail_ci_if_error: false
+
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
+      - name: Save HF cache
+        id: hf-cache
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            /home/runner/.cache/huggingface/hub/datasets--*
+            /home/runner/.cache/huggingface/hub/models--*
+          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
+
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
+    needs: [preload-cache]
     strategy:
       fail-fast: false
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -106,13 +193,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/tests.yml

@@ -27,6 +27,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
+env:
+  TRANSFORMERS_IS_CI: "yes"
+
 jobs:
   pre-commit:
     name: pre-commit
@@ -44,26 +47,23 @@ jobs:
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
+#    needs: [preload-cache]
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0", "2.7.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+      - name: Restore Cache from S3
+        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
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -118,38 +118,25 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
-
   pytest-sdist:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 1
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1", "2.6.0"]
+        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+      - name: Restore Cache from S3
+        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
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -196,22 +183,125 @@ jobs:
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
-
   docker-e2e-tests-1st:
+    # Run this job first as a gate for running the remainder of the test matrix
     if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 90
+    timeout-minutes: 120
     needs: [pre-commit, pytest, pytest-sdist]
 
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras: vllm
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras:
+            dockerfile: "Dockerfile-uv.jinja"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==1.0.2 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ 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'}}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.e2e_tests
+
+  docker-e2e-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 120
+    # Only run the remainder of the matrix if the first e2e check passed;
+    # this is to save on wasted compute costs for known failures that get caught in the first run
+    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras: llmcompressor
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.5.1
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.1
+            num_gpus: 1
+            axolotl_extras:
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==1.0.2 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+          echo "CUDA=${{ 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'}}" >> $GITHUB_ENV
+      - name: Run tests job on Modal
+        run: |
+          modal run cicd.e2e_tests
+
+  docker-e2e-cleanup:
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 90
+    needs: [docker-e2e-tests]
+
     strategy:
       fail-fast: false
       matrix:
@@ -232,7 +322,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
@@ -245,58 +335,4 @@ jobs:
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
-
-  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            num_gpus: 1
-            axolotl_extras:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.7.0
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ 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.e2e_tests
+          modal run cicd.cleanup

.pre-commit-config.yaml

@@ -19,15 +19,15 @@ repos:
     hooks:
       - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 7.1.2
+    rev: 7.2.0
     hooks:
     - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.6
+    rev: v3.3.7
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.16.0
     hooks:
     - id: mypy
       additional_dependencies:

.runpod/src/config/config.yaml

@@ -242,16 +242,12 @@
 # early_stopping_patience: 3
 
 # # Specify a scheduler and kwargs to use with the optimizer
-# lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
+# lr_scheduler: # 'one_cycle' | empty for cosine
 # lr_scheduler_kwargs:
 
 # # For one_cycle optim
 # lr_div_factor: # Learning rate div factor
 
-# # For log_sweep optim
-# log_sweep_min_lr:
-# log_sweep_max_lr:
-
 # # Specify optimizer
 # # Valid values are driven by the Transformers OptimizerNames class, see:
 # # https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134

.runpod/src/handler.py

@@ -57,8 +57,10 @@ async def handler(job):
     logger.info("Training Complete.")
 
     # Cleanup
-    del os.environ["WANDB_API_KEY"]
-    del os.environ["HF_TOKEN"]
+    if "WANDB_API_KEY" in os.environ:
+        del os.environ["WANDB_API_KEY"]
+    if "HF_TOKEN" in os.environ:
+        del os.environ["HF_TOKEN"]
 
 
 runpod.serverless.start({"handler": handler, "return_aggregate_stream": True})

.runpod/test-input.json

@@ -0,0 +1,86 @@
+{
+  "input": {
+    "name": "quick_smoke_test_sft",
+    "user_id": "user",
+    "model_id": "llama-test",
+    "run_id": "llama-test",
+    "credentials": {
+      "wandb_api_key": "",
+      "hf_token": ""
+    },
+    "args": {
+      "base_model": "HuggingFaceTB/SmolLM2-135M",
+      "model_type": "AutoModelForCausalLM",
+      "tokenizer_type": "AutoTokenizer",
+      "load_in_4bit": true,
+      "strict": false,
+      "datasets": [
+        {
+          "path": "mhenrichsen/alpaca_2k_test",
+          "type": "alpaca",
+          "split": "train[:10%]"
+        }
+      ],
+      "val_set_size": 0.02,
+      "output_dir": "./outputs/lora-out",
+      "sequence_len": 4096,
+      "sample_packing": true,
+      "eval_sample_packing": false,
+      "pad_to_sequence_len": true,
+      "adapter": "qlora",
+      "lora_r": 32,
+      "lora_alpha": 64,
+      "lora_dropout": 0.05,
+      "lora_target_linear": true,
+      "lora_modules_to_save": [
+        "embed_tokens",
+        "lm_head"
+      ],
+      "gradient_accumulation_steps": 2,
+      "micro_batch_size": 1,
+      "num_epochs": 1,
+      "optimizer": "adamw_torch_fused",
+      "lr_scheduler": "cosine",
+      "learning_rate": 0.0002,
+      "train_on_inputs": false,
+      "group_by_length": false,
+      "bf16": "auto",
+      "tf32": true,
+      "gradient_checkpointing": true,
+      "logging_steps": 1,
+      "flash_attention": true,
+      "warmup_steps": 1,
+      "evals_per_epoch": 1,
+      "eval_max_new_tokens": 128,
+      "saves_per_epoch": 1,
+      "weight_decay": 0.0,
+      "special_tokens": {
+        "pad_token": "<|endoftext|>"
+      },
+      "max_steps": 20
+    },
+    "timeout": 100000
+  },
+  "config": {
+    "gpuTypeId": "NVIDIA GeForce RTX 4090",
+    "gpuCount": 1,
+    "containerDiskInGb": 200,
+    "env": [
+      {
+        "key": "TOKENIZER",
+        "value": ""
+      },
+      {
+        "key": "DISABLE_LOG_STATS",
+        "value": "true"
+      }
+    ],
+    "allowedCudaVersions": [
+      "12.8",
+      "12.7",
+      "12.6",
+      "12.5",
+      "12.4"
+    ]
+  }
+}

.runpod/tests.json

@@ -1,65 +1,70 @@
 {
-  "input": {
-    "name": "quick_smoke_test_sft",
-    "user_id": "user",
-    "model_id": "llama-test",
-    "run_id": "llama-test",
-    "credentials": {
-      "wandb_api_key": "",
-      "hf_token": ""
-    },
-    "args": {
-      "base_model": "HuggingFaceTB/SmolLM2-135M",
-      "model_type": "AutoModelForCausalLM",
-      "tokenizer_type": "AutoTokenizer",
-      "load_in_8bit": true,
-      "load_in_4bit": false,
-      "strict": false,
-      "datasets": [
-        {
-          "path": "mhenrichsen/alpaca_2k_test",
-          "type": "alpaca"
+  "tests": [
+    {
+      "name": "quick_smoke_test_sft",
+      "input": {
+        "user_id": "user",
+        "model_id": "llama-test",
+        "run_id": "llama-test",
+        "credentials": {
+          "wandb_api_key": "",
+          "hf_token": ""
+        },
+        "args": {
+          "base_model": "HuggingFaceTB/SmolLM2-135M",
+          "model_type": "AutoModelForCausalLM",
+          "tokenizer_type": "AutoTokenizer",
+          "load_in_4bit": true,
+          "strict": false,
+          "datasets": [
+            {
+              "path": "mhenrichsen/alpaca_2k_test",
+              "type": "alpaca",
+              "split": "train[:10%]"
+            }
+          ],
+          "val_set_size": 0.02,
+          "output_dir": "./outputs/lora-out",
+          "sequence_len": 4096,
+          "sample_packing": true,
+          "eval_sample_packing": false,
+          "pad_to_sequence_len": true,
+          "adapter": "qlora",
+          "lora_r": 32,
+          "lora_alpha": 64,
+          "lora_dropout": 0.05,
+          "lora_target_linear": true,
+          "lora_modules_to_save": [
+            "embed_tokens",
+            "lm_head"
+          ],
+          "gradient_accumulation_steps": 2,
+          "micro_batch_size": 1,
+          "num_epochs": 1,
+          "optimizer": "adamw_torch_fused",
+          "lr_scheduler": "cosine",
+          "learning_rate": 0.0002,
+          "train_on_inputs": false,
+          "group_by_length": false,
+          "bf16": "auto",
+          "tf32": true,
+          "gradient_checkpointing": true,
+          "logging_steps": 1,
+          "flash_attention": true,
+          "warmup_steps": 1,
+          "evals_per_epoch": 1,
+          "eval_max_new_tokens": 128,
+          "saves_per_epoch": 1,
+          "weight_decay": 0.0,
+          "special_tokens": {
+            "pad_token": "<|endoftext|>"
+          },
+          "max_steps": 20
         }
-      ],
-      "val_set_size": 0.05,
-      "output_dir": "./outputs/lora-out",
-      "sequence_len": 4096,
-      "sample_packing": true,
-      "eval_sample_packing": false,
-      "pad_to_sequence_len": true,
-      "adapter": "lora",
-      "lora_r": 32,
-      "lora_alpha": 64,
-      "lora_dropout": 0.05,
-      "lora_target_linear": true,
-      "lora_modules_to_save": [
-        "embed_tokens",
-        "lm_head"
-      ],
-      "gradient_accumulation_steps": 4,
-      "micro_batch_size": 2,
-      "num_epochs": 1,
-      "optimizer": "adamw_torch_fused",
-      "lr_scheduler": "cosine",
-      "learning_rate": 0.0002,
-      "train_on_inputs": false,
-      "group_by_length": false,
-      "bf16": "auto",
-      "tf32": true,
-      "gradient_checkpointing": true,
-      "logging_steps": 1,
-      "flash_attention": true,
-      "warmup_steps": 1,
-      "evals_per_epoch": 1,
-      "eval_max_new_tokens": 128,
-      "saves_per_epoch": 1,
-      "weight_decay": 0.0,
-      "special_tokens": {
-        "pad_token": "<|endoftext|>"
-      }
-    },
-    "timeout": 100000
-  },
+      },
+      "timeout": 100000
+    }
+  ],
   "config": {
     "gpuTypeId": "NVIDIA GeForce RTX 4090",
     "gpuCount": 1,

README.md

@@ -22,28 +22,32 @@
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
 </p>
 
-Axolotl is a tool designed to streamline post-training for various AI models.
-Post-training refers to any modifications or additional training performed on
-pre-trained models - including full model fine-tuning, parameter-efficient tuning (like
-LoRA and QLoRA), supervised fine-tuning (SFT), instruction tuning, and alignment
-techniques. With support for multiple model architectures and training configurations,
-Axolotl makes it easy to get started with these techniques.
 
-Axolotl is designed to work with YAML config files that contain everything you need to
-preprocess a dataset, train or fine-tune a model, run model inference or evaluation,
-and much more.
+## 🎉 Latest Updates
+
+- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) 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 [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) 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!
+- 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See [docs](https://docs.axolotl.ai/docs/reward_modelling.html).
+
+## ✨ Overview
+
+Axolotl is a tool designed to streamline post-training for various AI models.
 
 Features:
 
-- Train various Huggingface models such as llama, pythia, falcon, mpt
-- Supports fullfinetune, lora, qlora, relora, and gptq
-- Customize configurations using a simple yaml file or CLI overwrite
-- Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with [xformers](https://github.com/facebookresearch/xformers), flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
-- Works with single GPU or multiple GPUs via FSDP or Deepspeed
-- Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb, mlflow or Comet
-- And more!
+- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
+- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
+- **Easy Configuration**: Re-use a single YAML file between dataset preprocess, 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), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), 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.
+
+
 
 ## 🚀 Quick Start
 
@@ -51,7 +55,7 @@ Features:
 
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
 - Python 3.11
-- PyTorch ≥2.4.1
+- PyTorch ≥2.5.1
 
 ### Installation
 
@@ -81,19 +85,12 @@ axolotl train examples/llama-3/lora-1b.yml
 
 That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough.
 
-## ✨ Key Features
-
-- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more
-- **Training Methods**: Full fine-tuning, LoRA, QLoRA, and more
-- **Easy Configuration**: Simple YAML files to control your training setup
-- **Performance Optimizations**: Flash Attention, xformers, multi-GPU training
-- **Flexible Dataset Handling**: Use various formats and custom datasets
-- **Cloud Ready**: Run on cloud platforms or local hardware
 
 ## 📚 Documentation
 
 - [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments
 - [Configuration Guide](https://docs.axolotl.ai/docs/config.html) - Full configuration options and examples
+- [Dataset Loading](https://docs.axolotl.ai/docs/dataset_loading.html) - Loading datasets from various sources
 - [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them
 - [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
 - [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
@@ -112,31 +109,6 @@ That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/ge
 
 Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details.
 
-## Supported Models
-
-|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
-|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
-| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Mixtral8X22 | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
-| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
-| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
-| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
-| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
-| Jamba       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
-
-✅: supported
-❌: not supported
-❓: untested
-
 ## ❤️ Sponsors
 
 Thank you to our sponsors who help make Axolotl possible:

_quarto.yml

@@ -17,7 +17,9 @@ quartodoc:
         - convert
         - prompt_tokenizers
         - logging_config
-        - core.trainer_builder
+        - core.builders.base
+        - core.builders.causal
+        - core.builders.rl
         - core.training_args
         - core.chat.messages
         - core.chat.format.chatml
@@ -43,13 +45,37 @@ quartodoc:
         - cli.vllm_serve
         - cli.cloud.base
         - cli.cloud.modal_
+        - cli.quantize
     - title: Trainers
       desc: Training implementations
       contents:
         - core.trainers.base
         - core.trainers.trl
+        - core.trainers.mamba
+        - core.trainers.relora
         - core.trainers.dpo.trainer
         - core.trainers.grpo.trainer
+        - core.trainers.grpo.sampler
+        - core.trainers.utils
+    - title: Model Loading
+      desc: Functionality for loading and patching models, tokenizers, etc.
+      contents:
+        - loaders.model
+        - loaders.tokenizer
+        - loaders.processor
+        - loaders.adapter
+        - loaders.patch_manager
+        - loaders.constants
+    - title: Mixins
+      desc: Mixin classes for augmenting trainers
+      contents:
+        - core.trainers.mixins.optimizer
+        - core.trainers.mixins.rng_state_loader
+        - core.trainers.mixins.scheduler
+    - title: Context Managers
+      desc: Context managers for altering trainer behaviors
+      contents:
+        - utils.ctx_managers.sequence_parallel
     - title: Prompt Strategies
       desc: Prompt formatting strategies
       contents:
@@ -86,7 +112,7 @@ quartodoc:
         - kernels.swiglu
         - kernels.quantize
         - kernels.utils
-    - title: MonkeyPatches
+    - title: Monkey Patches
       desc: Runtime patches for model optimizations
       contents:
         - monkeypatch.llama_attn_hijack_flash
@@ -103,17 +129,16 @@ quartodoc:
         - monkeypatch.trainer_fsdp_optim
         - monkeypatch.transformers_fa_utils
         - monkeypatch.unsloth_
-        - monkeypatch.attention.mllama
         - monkeypatch.data.batch_dataset_fetcher
         - monkeypatch.mixtral
+        - monkeypatch.gradient_checkpointing.offload_cpu
+        - monkeypatch.gradient_checkpointing.offload_disk
     - title: Utils
       desc: Utility functions
       contents:
-        - utils.models
         - utils.tokenization
         - utils.chat_templates
         - utils.lora
-        - utils.lora_embeddings
         - utils.model_shard_quant
         - utils.bench
         - utils.freeze
@@ -124,7 +149,7 @@ quartodoc:
         - utils.optimizers.adopt
         - utils.data.pretraining
         - utils.data.sft
-        - utils.gradient_checkpointing.unsloth
+        - utils.quantization
     - title: Schemas
       desc: Pydantic data models for Axolotl config
       contents:
@@ -174,12 +199,14 @@ quartodoc:
         - utils.callbacks.lisa
         - utils.callbacks.mlflow_
         - utils.callbacks.comet_
-
+        - utils.callbacks.qat
 website:
   title: "Axolotl"
   description: "We make fine-tuning accessible, scalable, and fun"
   favicon: favicon.jpg
 
+  google-analytics: "G-9KYCVJBNMQ"
+
   navbar:
     logo: image/axolotl_logo_digital_white.svg
     title: false
@@ -232,6 +259,8 @@ website:
             - docs/lr_groups.qmd
             - docs/lora_optims.qmd
             - docs/dataset_loading.qmd
+            - docs/qat.qmd
+            - docs/quantize.qmd
 
         - section: "Core Concepts"
           contents:

cicd/Dockerfile-uv.jinja

@@ -0,0 +1,52 @@
+FROM axolotlai/axolotl-base-uv:{{ BASE_TAG }}
+
+ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
+ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
+ENV CUDA="{{ CUDA }}"
+ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
+ENV GITHUB_REF="{{ GITHUB_REF }}"
+ENV GITHUB_SHA="{{ GITHUB_SHA }}"
+ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
+ENV HF_HOME="{{ HF_HOME }}"
+
+RUN apt-get update && \
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
+
+WORKDIR /workspace
+
+RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
+
+WORKDIR /workspace/axolotl
+
+RUN git fetch origin +$GITHUB_REF && \
+    git checkout FETCH_HEAD
+
+# If AXOLOTL_EXTRAS is set, append it in brackets
+RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
+        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
+        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
+        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
+        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
+        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
+    fi
+
+RUN uv pip install packaging==23.2 setuptools==75.8.0
+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 \
+        uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+    fi
+
+RUN python scripts/unsloth_install.py --uv | sh
+RUN python scripts/cutcrossentropy_install.py --uv | sh
+
+# So we can test the Docker image
+RUN uv pip install -r requirements-dev.txt -r requirements-tests.txt
+
+# fix so that git fetch/pull from remote works
+RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+    git config --get remote.origin.fetch
+
+# helper for huggingface-login cli
+RUN git config --global credential.helper store

cicd/cicd.sh

@@ -18,7 +18,7 @@ pytest -v --durations=10 \
   --cov-append
 
 # Run patched tests excluding lora kernels with coverage append
-pytest -v --durations=10 \
+pytest --full-trace -vvv --durations=10 \
   --ignore=tests/e2e/patched/lora_kernels \
   /workspace/axolotl/tests/e2e/patched \
   --cov=axolotl \

cicd/cleanup.py

@@ -0,0 +1,19 @@
+"""Modal app to run axolotl GPU cleanup"""
+
+from .single_gpu import VOLUME_CONFIG, app, cicd_image, run_cmd
+
+
+@app.function(
+    image=cicd_image,
+    timeout=60 * 60,
+    cpu=8.0,
+    memory=131072,
+    volumes=VOLUME_CONFIG,
+)
+def cleanup():
+    run_cmd("./cicd/cleanup.sh", "/workspace/axolotl")
+
+
+@app.local_entrypoint()
+def main():
+    cleanup.remote()

cicd/cleanup.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+set -e
+
+# cleanup old cache files for datasets processing and intermediate mappings
+find /workspace/data/huggingface-cache/hub/datasets -name "cache-*" -type f -mtime +1 -exec rm {} \;
+find /workspace/data/huggingface-cache/hub/datasets -name "*.lock" -type f -mtime +1 -exec rm {} \;

cicd/e2e_tests.py

@@ -1,75 +1,12 @@
 """Modal app to run axolotl GPU tests"""
 
-# pylint: disable=duplicate-code
-
-import os
-import pathlib
-import tempfile
-
-import jinja2
-import modal
-from jinja2 import select_autoescape
-from modal import App, Image
-
-cicd_path = pathlib.Path(__file__).parent.resolve()
-
-template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
-template_env = jinja2.Environment(
-    loader=template_loader, autoescape=select_autoescape()
-)
-df_template = template_env.get_template("Dockerfile.jinja")
-
-df_args = {
-    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
-    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
-    "CUDA": os.environ.get("CUDA", "121"),
-    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
-    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
-    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
-    "HF_HOME": "/workspace/data/huggingface-cache/hub",
-}
-
-dockerfile_contents = df_template.render(**df_args)
-
-temp_dir = tempfile.mkdtemp()
-with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
-    f.write(dockerfile_contents)
-
-cicd_image = Image.from_dockerfile(
-    pathlib.Path(temp_dir) / "Dockerfile",
-    context_mount=None,
-    force_build=True,
-    gpu="A10G",
-).env(df_args)
-
-app = App("Axolotl CI/CD", secrets=[])
-
-hf_cache_volume = modal.Volume.from_name(
-    "axolotl-ci-hf-hub-cache", create_if_missing=True
-)
-VOLUME_CONFIG = {
-    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
-}
-
-N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
-
-
-def run_cmd(cmd: str, run_folder: str):
-    import subprocess  # nosec
-
-    # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+from .single_gpu import GPU_CONFIG, VOLUME_CONFIG, app, cicd_image, run_cmd
 
 
 @app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=90 * 60,  # 90 min
     cpu=8.0,
     memory=131072,
     volumes=VOLUME_CONFIG,

cicd/multigpu.py

@@ -24,9 +24,9 @@ df_template = template_env.get_template("Dockerfile.jinja")
 df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
     "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
-    "CUDA": os.environ.get("CUDA", "121"),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
+    "CUDA": os.environ.get("CUDA", "124"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
     "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
@@ -55,7 +55,7 @@ VOLUME_CONFIG = {
 }
 
 N_GPUS = int(os.environ.get("N_GPUS", 2))
-GPU_CONFIG = modal.gpu.H100(count=N_GPUS)
+GPU_CONFIG = f"H100:{N_GPUS}"
 
 
 def run_cmd(cmd: str, run_folder: str):
@@ -70,7 +70,7 @@ def run_cmd(cmd: str, run_folder: str):
     image=cicd_image,
     gpu=GPU_CONFIG,
     timeout=90 * 60,
-    cpu=8.0,
+    cpu=16.0,
     memory=131072 * N_GPUS,
     volumes=VOLUME_CONFIG,
 )

cicd/single_gpu.py

@@ -0,0 +1,68 @@
+"""Modal app to run axolotl GPU tests"""
+
+# pylint: disable=duplicate-code
+
+import os
+import pathlib
+import tempfile
+
+import jinja2
+import modal
+import modal.experimental
+from jinja2 import select_autoescape
+from modal import App
+
+cicd_path = pathlib.Path(__file__).parent.resolve()
+
+template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
+template_env = jinja2.Environment(
+    loader=template_loader, autoescape=select_autoescape()
+)
+dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
+df_template = template_env.get_template(dockerfile)
+
+df_args = {
+    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
+    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
+    "CUDA": os.environ.get("CUDA", "124"),
+    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
+    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
+    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
+    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
+    "HF_HOME": "/workspace/data/huggingface-cache/hub",
+}
+
+dockerfile_contents = df_template.render(**df_args)
+
+temp_dir = tempfile.mkdtemp()
+with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
+    f.write(dockerfile_contents)
+
+cicd_image = modal.experimental.raw_dockerfile_image(
+    pathlib.Path(temp_dir) / "Dockerfile",
+    # context_mount=None,
+    force_build=True,
+    # gpu="A10G",
+).env(df_args)
+
+app = App("Axolotl CI/CD", secrets=[])
+
+hf_cache_volume = modal.Volume.from_name(
+    "axolotl-ci-hf-hub-cache", create_if_missing=True
+)
+VOLUME_CONFIG = {
+    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
+}
+
+N_GPUS = int(os.environ.get("N_GPUS", 1))
+GPU_CONFIG = f"L40S:{N_GPUS}"
+
+
+def run_cmd(cmd: str, run_folder: str):
+    import subprocess  # nosec
+
+    # Propagate errors from subprocess.
+    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
+        exit(exit_code)  # pylint: disable=consider-using-sys-exit

codecov.yml

@@ -19,7 +19,7 @@ coverage:
         if_no_uploads: error
         if_not_found: success
         if_ci_failed: error
-        only_pulls: false
+        only_pulls: true
         flags: null
         paths: null
     patch:

deepspeed_configs/zero2_torch_compile.json

@@ -0,0 +1,31 @@
+{
+  "compile": {
+    "disable": false,
+    "backend": "inductor"
+  },
+  "zero_optimization": {
+    "stage": 2,
+    "offload_optimizer": {
+      "device": "cpu"
+    },
+    "contiguous_gradients": true,
+    "overlap_comm": true
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

docker/Dockerfile-base

@@ -38,6 +38,6 @@ RUN git lfs install --skip-repo && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
 
-RUN if [ "$PYTORCH_VERSION" = "2.7.0" ] ; then \
+RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
         pip3 install flash-attn==2.7.4.post1; \
     fi

docker/Dockerfile-base-next

@@ -29,7 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
+    python3 -m pip install --no-cache-dir -U torch==2.7.1 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
 

docker/Dockerfile-uv-base

@@ -0,0 +1,40 @@
+ARG CUDA_VERSION="12.6.3"
+ARG CUDNN_VERSION=""
+ARG UBUNTU_VERSION="22.04"
+ARG MAX_JOBS=4
+
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+
+ARG PYTHON_VERSION="3.11"
+ARG PYTORCH_VERSION="2.6.0"
+ARG CUDA="126"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+
+ENV PYTHON_VERSION=$PYTHON_VERSION
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
+ENV UV_TORCH_BACKEND="cu${CUDA}"
+
+RUN apt-get update \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config curl && rm -rf /var/lib/apt/lists/* \
+    && git lfs install --skip-repo \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh
+
+ENV PATH="/root/.local/bin:${PATH}"
+
+RUN uv python install ${PYTHON_VERSION}
+
+WORKDIR /workspace
+
+RUN uv venv --no-project --relocatable axolotl-venv
+
+ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
+
+RUN uv pip install packaging setuptools wheel psutil \
+    && uv pip install torch==${PYTORCH_VERSION} \
+    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
+    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
+    && uv pip install awscli pydantic
+
+RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
+        uv pip install --no-build-isolation flash-attn==2.7.4.post1; \
+    fi

docs/cli.qmd

@@ -209,6 +209,16 @@ axolotl delinearize-llama4 --model path/to/model_dir --output path/to/output_dir
 
 This would be necessary to use with other frameworks. If you have an adapter, merge it with the non-quantized linearized model before delinearizing.
 
+### quantize
+
+Quantizes a model using the quantization configuration specified in your YAML file.
+
+```bash
+axolotl quantize config.yml
+```
+
+See [Quantization](./quantize.qmd) for more details.
+
 
 ## Legacy CLI Usage
 

docs/config.qmd

@@ -27,11 +27,15 @@ trust_remote_code:
 tokenizer_use_fast:
 # Whether to use the legacy tokenizer setting, defaults to True
 tokenizer_legacy:
+# Whether to use mistral-common tokenizer. If set to True, it will use the mistral-common tokenizer.
+tokenizer_use_mistral_common:
 # Resize the model embeddings when new tokens are added to multiples of 32
 # This is reported to improve training speed on some models
 resize_token_embeddings_to_32x:
 # Optional[bool] Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
 shrink_embeddings:
+# Optional[bool] Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs
+embeddings_skip_upcast:
 # Whether to load the model with randomly initialized weights. Useful for
 # pre-training a model from scratch or debugging purposes.
 random_init_weights:
@@ -63,6 +67,20 @@ bnb_config_kwargs:
   bnb_4bit_quant_type: nf4
   bnb_4bit_use_double_quant: true
 
+# quantization aware training
+qat:
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4" and "int8"
+  group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
+  fake_quant_after_n_steps: # Optional[int] = None. The number of steps to apply fake quantization after
+
+# post-training quantization
+quantization:
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are uintX for X in [1, 2, 3, 4, 5, 6, 7], or int4, or int8
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
+  group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
+  quantize_embedding: # Optional[bool] = False. Whether to quantize the embedding layer.
+
 
 # Whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -73,11 +91,12 @@ load_in_8bit: true
 load_in_4bit:
 
 # Use CUDA bf16
-bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
+bf16: true # bool or 'full' for `bf16_full_eval`, or 'auto' for automatic detection. require >=ampere
 # Use CUDA fp16
 fp16: true
 # Use CUDA tf32
 tf32: true # require >=ampere
+# Note: if bf16 is set to 'auto', and fp16 is set to true, we will prefer the explict fp16 setting
 
 # No AMP (automatic mixed precision)
 bfloat16: true # require >=ampere
@@ -95,8 +114,10 @@ plugins:
   # - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
 
 # A list of one or more datasets to finetune the model with
+# See https://docs.axolotl.ai/docs/dataset_loading.html for guide on loading datasets
+# See https://docs.axolotl.ai/docs/dataset-formats/ for guide on dataset formats
 datasets:
-  # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
+  # HuggingFace dataset repo | s3:// | gs:// | path to local file or directory
   - path: vicgalle/alpaca-gpt4
     # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
@@ -154,6 +175,10 @@ datasets:
     # Key containing the messages (default: "messages")
     field_messages: messages
 
+    # Key containing the tools (default: "tools")
+    # Must be a list[dict] and follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
+    field_tools: tools
+
     # Key containing the system message (default: "system")
     # If the system message is not present in the dataset sample, it will be loaded from the field_system property.
     field_system: system
@@ -184,6 +209,10 @@ datasets:
     # adding a system turn with empty content.
     drop_system_message:
 
+    # Optional[bool]. (for Qwen3 template only) Whether to split the assistant content based on a reasoning trace inside delimited tags
+    # See example at `docs/dataset-formats/conversation.qmd`
+    split_thinking:
+
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
@@ -214,7 +243,7 @@ datasets:
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
 
-Deduplicates datasets and test_datasets with identical entries.
+# Deduplicates datasets and test_datasets with identical entries.
 dataset_exact_deduplication: true
 
 # A list of one or more datasets to eval the model with.
@@ -263,10 +292,25 @@ trl:
 
   num_generations: # Optional[int]. Number of generations to sample.
   log_completions: # Optional[bool]. Whether to log completions.
+  num_completions_to_print: # Optional[int]. Number of completions to print when log_completions is True.
 
   sync_ref_model: # Optional[bool]. Whether to sync the reference model.
   ref_model_mixup_alpha: # Optional[float]. Mixup alpha for the reference model.
   ref_model_sync_steps: # Optional[int]. Sync steps for the reference model.
+  scale_rewards: # Optional[bool]. Whether to scale rewards by their standard deviation.
+
+  temperature: # Optional[float]. Sampling temperature for the GRPO policy.
+  top_p: # Optional[float]. Top-p sampling probability for the generation policy.
+  top_k: # Optional[int]. Top-k sampling for the generation policy.
+  min_p: # Optional[float]. Minimum probability for the generation policy.
+  repetition_penalty: # Optional[float]. Penalty for tokens that appear in prompt and generated text.
+
+  num_iterations: # Optional[int]. Number of iterations per batch (μ) for GRPO.
+  epsilon: # Optional[float]. Epsilon value for clipping in the GRPO algorithm.
+  epsilon_high: # Optional[float]. Upper-bound epsilon value for clipping in the GRPO algorithm.
+  use_liger_loss: # Optional[bool]. Whether to use Liger loss for GRPO.
+  loss_type: # Optional[str]. Loss formulation to use. Supported values: grpo, bnpo, dr_grpo.
+  mask_truncated_completions: # Optional[bool]. Whether to exclude truncated completions from loss calculation.
 
 
 # reward modelling: `True` or `False`
@@ -476,6 +520,7 @@ output_dir: ./completed-model
 # setting to `auto` will enable torch compile when torch>=2.5.1
 torch_compile:  # Optional[Union[Literal["auto"], bool]]
 torch_compile_backend:  # Optional[str]
+torch_compile_mode:  # 'default' | 'reduce-overhead' | 'max-autotune'
 
 # Training hyperparameters
 
@@ -498,6 +543,7 @@ save_strategy: # Set to `"no"` to skip checkpoint saves, `"epoch"` at end of eac
 save_steps: # Leave empty to save at each epoch, integer for every N steps. float for fraction of total steps
 saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
 save_total_limit: # Checkpoints saved at a time
+save_only_model: # Save only the model weights, skipping the optimizer. Using this means you can't resume from checkpoints.
 # Maximum number of iterations to train for. It precedes num_epochs which means that
 # if both are set, num_epochs will not be guaranteed.
 # e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
@@ -521,7 +567,7 @@ profiler_steps: # enable the pytorch profiler to capture the first N steps of tr
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
 
-# Save model as safetensors (require safetensors package)
+# Save model as safetensors (require safetensors package). Default True
 save_safetensors:
 
 # Whether to mask out or include the human's prompt from the training labels
@@ -531,7 +577,7 @@ train_on_inputs: false
 # Note that training loss may have an oscillating pattern with this enabled.
 group_by_length: false
 
-# Whether to use gradient checkpointing. Available options are: true, false, "offload".
+# Whether to use gradient checkpointing. Available options are: true, false, "offload", "offload_disk".
 # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 # additional kwargs to pass to the trainer for gradient checkpointing
@@ -543,7 +589,24 @@ gradient_checkpointing: false
 early_stopping_patience: 3
 
 # Specify a scheduler and kwargs to use with the optimizer
-lr_scheduler: # 'one_cycle' | 'rex' | 'log_sweep' | empty for cosine
+# Valid values are driven by the Transformers SchedulerType class, see:
+# https://github.com/huggingface/transformers/blob/5f4ecf2d9f867a1255131d2461d75793c0cf1db2/src/transformers/trainer_utils.py#L420
+# Valid values include
+# - 'linear'
+# - 'cosine' (default)
+# - 'cosine_with_restarts'
+# - 'polynomial'
+# - 'constant'
+# - 'constant_with_warmup'
+# - 'inverse_sqrt'
+# - 'reduce_lr_on_plateau'
+# - 'cosine_with_min_lr'
+# - 'warmup_stable_decay'
+
+# Additional schedulers include:
+# - 'one_cycle'
+# - 'rex'
+lr_scheduler:
 lr_scheduler_kwargs:
 cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
 cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
@@ -561,7 +624,7 @@ lr_div_factor: # Learning rate div factor
 #
 # Valid values for 'optimizer' include:
 # - adamw_torch
-# - adamw_torch_fused
+# - adamw_torch_fused (default)
 # - adamw_torch_xla
 # - adamw_torch_npu_fused
 # - adamw_apex_fused
@@ -605,6 +668,7 @@ lr_div_factor: # Learning rate div factor
 # - optimi_adamw
 # - ao_adamw_8bit
 # - ao_adamw_fp8
+# - came_pytorch
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -624,7 +688,9 @@ weight_decay:
 # adamw hyperparams
 adam_beta1:
 adam_beta2:
+adam_beta3:  # only used for CAME Optimizer
 adam_epsilon:
+adam_epsilon2:  # only used for CAME Optimizer
 # Gradient clipping max norm
 max_grad_norm:
 

docs/custom_integrations.qmd

@@ -49,7 +49,8 @@ sections = [
     ("Knowledge Distillation (KD)", "kd"),
     ("Liger Kernels", "liger"),
     ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
+    ("Spectrum", "spectrum"),
+    ("LLMCompressor", "llm_compressor")
 ]
 
 for section_name, folder_name in sections:

docs/dataset-formats/conversation.qmd

@@ -52,7 +52,9 @@ We recommend checking the below examples for other usecases.
 
 ### Examples
 
-1. (Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
+#### Training on last message
+
+(Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
 
 ```yaml
 datasets:
@@ -66,7 +68,9 @@ datasets:
 If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
 :::
 
-2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
+#### Overriding default chat template
+
+Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 chat_template: gemma # this overwrites the tokenizer's chat_template
@@ -76,7 +80,13 @@ datasets:
     roles_to_train: ["assistant"]  # default value
 ```
 
-3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
+::: {.callout-note}
+If you want to use built-in chat_template, use `chat_template: tokenizer_default` (this is set by default).
+:::
+
+#### Using default chat template with fallback
+
+Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
@@ -85,7 +95,9 @@ datasets:
     type: chat_template
 ```
 
-4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
+#### Custom Jinja template
+
+Using a custom jinja template on OpenAI messages format, training on all assistant messages.
 
 ```yaml
 # chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
@@ -100,7 +112,9 @@ datasets:
 Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
 :::
 
-5. If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
+#### Using template with different token for EOT and EOS
+
+- If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.
 
 ```yaml
 eot_tokens:
@@ -125,7 +139,7 @@ Using `eot_tokens` requires each token that exists in `chat_template` to be a si
 You can add those tokens as new tokens under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `. See [config](../config.qmd) for more details.
 :::
 
-6. Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
+- Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.
 
 ```yaml
 eot_tokens:
@@ -145,7 +159,73 @@ If EOS token only appears at the end of a prompt, `train_on_eos: last` is equiva
 :::
 
 
-7. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
+#### Using tool use
+
+Instead of passing `tools` via the system prompt, an alternative method would be to have the `tools` in a separate column and loaded via `chat_template` to let the template dynamically build it.
+
+```json
+{
+    "tools": [
+        {
+            "type": "...",
+            "function": {
+                "name": "...",
+                "description": "...",
+                "parameters": {
+                    "type": "...",
+                    "properties": {
+                        // ...
+                    },
+                    "required": ["..."],
+                },
+            },
+        },
+    ],
+    "messages": [
+        // ...
+        {
+            "role": "assistant", // call the function via assistant
+            "tool_calls": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "...",
+                        "arguments": {
+                            "...": "...",
+                        }
+                    }
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "name": "...",
+            "content": "..."
+        },
+    ],
+}
+```
+
+::: {.callout-note}
+Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
+:::
+
+```yaml
+chat_template: llama4
+datasets:
+  - path: ...
+    type: chat_template
+    # field_tools: tools # default is `tools`
+```
+
+::: {.callout-tip}
+Look into the `chat_template` you are using to see if it supports `tools` and what the expected role is for the tool answer. In the example above, the tool answer is expected to be in the `tool` or `ipython` role for `llama4` template.
+:::
+
+
+#### Using fine-grained control over token masking
+
+(Advanced) Using fine-grained control over tokens and turns to train in a conversation
 
 For a data sample that looks like:
 
@@ -196,6 +276,36 @@ datasets:
 It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
 :::
 
+#### Reasoning split
+
+(For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    chat_template: qwen3
+    split_thinking: true
+```
+
+For example, a content can look like:
+
+```json
+{
+  "content": "<think>Some thinking outputs</think>Output after thinking."
+}
+```
+
+After split, it will look like:
+
+```json
+{
+  "reasoning_content": "Some thinking outputs",
+  "content": "Output after thinking..."
+}
+```
+
+
 ## sharegpt
 
 ::: {.callout-important}

docs/dataset-formats/index.qmd

@@ -36,10 +36,6 @@ It is typically recommended to save your dataset as `.jsonl` due to its flexibil
 
 Axolotl supports loading from a Hugging Face hub repo or from local files.
 
-::: {.callout-important}
-For pre-training only, Axolotl would split texts if it exceeds the context length into multiple smaller prompts.
-:::
-
 ### Pre-training from Hugging Face hub datasets
 
 As an example, to train using a Hugging Face dataset `hf_org/name`, you can pass the following config:
@@ -77,18 +73,21 @@ datasets:
     type: completion
 ```
 
-From local files (either example works):
+From local files:
 
 ```yaml
 datasets:
   - path: A.jsonl
     type: completion
 
-  - path: json
-    data_files: ["A.jsonl", "B.jsonl", "C.jsonl"]
+  - path: B.jsonl
     type: completion
 ```
 
+::: {.callout-important}
+For `completion` only, Axolotl would split texts if it exceeds the context length into multiple smaller prompts. If you are interested in having this for `pretraining_dataset` too, please let us know or help make a PR!
+:::
+
 ### Pre-training dataset configuration tips
 
 #### Setting max_steps

docs/dataset_loading.qmd

@@ -54,7 +54,7 @@ datasets:
 
 #### Files
 
-Usually, to load a JSON file, you would do something like this:
+To load a JSON file, you would do something like this:
 
 ```python
 from datasets import load_dataset
@@ -66,20 +66,12 @@ Which translates to the following config:
 
 ```yaml
 datasets:
-  - path: json
-    data_files: /path/to/your/file.jsonl
-```
-
-However, to make things easier, we have added a few shortcuts for loading local dataset files.
-
-You can just point the `path` to the file or directory along with the `ds_type` to load the dataset. The below example shows for a JSON file:
-
-```yaml
-datasets:
-  - path: /path/to/your/file.jsonl
+  - path: data.json
     ds_type: json
 ```
 
+In the example above, it can be seen that we can just point the `path` to the file or directory along with the `ds_type` to load the dataset.
+
 This works for CSV, JSON, Parquet, and Arrow files.
 
 ::: {.callout-tip}

docs/docker.qmd

@@ -8,6 +8,10 @@ format:
 
 This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
 
+::: {.callout-important}
+For Blackwell GPUs, please use the tags with Pytorch 2.7.1 and CUDA 12.8.
+:::
+
 ## Base
 
 The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
@@ -28,11 +32,10 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
 
 Tags examples:
 
-- `main-base-py3.11-cu128-2.7.0`
-- `main-base-py3.11-cu126-2.7.0`
+- `main-base-py3.11-cu128-2.7.1`
+- `main-base-py3.11-cu126-2.7.1`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
-- `main-base-py3.11-cu124-2.4.1`
 
 ## Main
 
@@ -73,12 +76,10 @@ Tags examples:
 - `main-py3.11-cu126-2.7.0`
 - `main-py3.11-cu124-2.6.0`
 - `main-py3.11-cu124-2.5.1`
-- `main-py3.11-cu124-2.4.1`
 - `main-latest`
 - `main-20250303-py3.11-cu124-2.6.0`
 - `main-20250303-py3.11-cu124-2.5.1`
-- `main-20250303-py3.11-cu124-2.4.1`
-- `0.7.1`
+- `0.9.2`
 
 ## Cloud
 

docs/faq.qmd

@@ -110,3 +110,17 @@ description: Frequently asked questions
 > A: If `eot_tokens: ` is not provided, the default behavior is the same as before. EOS tokens used to delimit turns are masked/unmasked depending on whether the turn is trainable.
 
 > Internally, `eot_tokens: tokenizer.eos_token` and `train_on_eot: train_on_eos` (which defaults to `turn`). This transition helps clarify the naming and behavior of EOT/EOS tokens.
+
+**Q: `Data processing error: CAS service error`**
+
+> A: Try disabling XET with `export HF_HUB_DISABLE_XET=1`
+
+**Q: `torch._inductor.exc.LoweringException: NoValidChoicesError: No choices to select, please consider adding ATEN into max_autotune_gemm_backends config (defined in torch/_inductor/config.py) to allow at least one choice. `**
+
+> A: Depending on the version of torch, you may need to include this in your YAML:
+
+> ```yaml
+> flex_attn_compile_kwargs:
+>   dynamic: false
+>   mode: max-autotune-no-cudagraphs
+> ```

docs/getting-started.qmd

@@ -104,7 +104,7 @@ the `alpaca` dataset format, which has the following format:
 Please see our [Dataset Formats](dataset-formats) for more dataset formats and how to
 format them.
 
-2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca
+2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca`
 format):
 
 ```json
@@ -120,6 +120,12 @@ axolotl train my_training.yml
 
 ## Common Tasks {#sec-common-tasks}
 
+::: {.callout-tip}
+
+The same yaml file is used for training, inference, and merging.
+
+:::
+
 ### Testing Your Model {#sec-testing}
 
 After training, test your model:
@@ -128,6 +134,16 @@ After training, test your model:
 axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
+More details can be found in [Inference](inference.qmd).
+
+### Using a UI {#sec-ui}
+
+Launch a Gradio interface:
+
+```bash
+axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
+```
+
 ### Preprocessing Data {#sec-preprocessing}
 
 For large datasets, preprocess first:
@@ -136,14 +152,22 @@ For large datasets, preprocess first:
 axolotl preprocess my_training.yml
 ```
 
-### Using a UI {#sec-ui}
+Please make sure to set `dataset_prepared_path: ` in your config to set the path to save the prepared dataset.
 
-Launch a Gradio interface:
+More details can be found in [Dataset Preprocessing](dataset_preprocessing.qmd).
+
+### Merging LoRA weights {#sec-merging-lora}
+
+To merge the LoRA weights back into the base model, run:
 
 ```bash
-axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
+axolotl merge-lora my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
+The merged model will be saved in the `{output_dir}/merged` directory.
+
+More details can be found in [Merging LoRA weights](inference.qmd#sec-merging).
+
 ## Next Steps {#sec-next-steps}
 
 Now that you have the basics, you might want to:
@@ -156,6 +180,7 @@ Now that you have the basics, you might want to:
 Check our other guides for details on these topics:
 
 - [Configuration Guide](config.qmd) - Full configuration options
+- [Dataset Loading](dataset_loading.qmd) - Loading datasets from various sources
 - [Dataset Formats](dataset-formats) - Working with different data formats
 - [Multi-GPU Training](multi-gpu.qmd)
 - [Multi-Node Training](multi-node.qmd)

docs/installation.qmd

@@ -15,7 +15,7 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 
 - NVIDIA GPU (Ampere architecture or newer for `bf16` and Flash Attention) or AMD GPU
 - Python ≥3.10
-- PyTorch ≥2.4.1
+- PyTorch ≥2.5.1
 
 ## Installation Methods {#sec-installation-methods}
 
@@ -25,6 +25,10 @@ Please make sure to have Pytorch installed before installing Axolotl in your loc
 Follow the instructions at: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
 :::
 
+::: {.callout-important}
+For Blackwell GPUs, please use Pytorch 2.7.0 and CUDA 12.8.
+:::
+
 ### PyPI Installation (Recommended) {#sec-pypi}
 
 ```{.bash}
@@ -37,6 +41,40 @@ installed) in order not to clobber it, and so that we set the correct version of
 dependencies that are specific to the PyTorch version or other installed
 co-dependencies.
 
+### uv Installation {#sec-uv}
+
+uv is a fast, reliable Python package installer and resolver built in Rust. It offers significant performance improvements over pip and provides better dependency resolution, making it an excellent choice for complex environments.
+
+Install uv if not already installed
+```{.bash}
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+```
+
+Choose your CUDA version to use with PyTorch; e.g. `cu124`, `cu126`, `cu128`,
+then create the venv and activate
+```{.bash}
+export UV_TORCH_BACKEND=cu126
+uv venv --no-project --relocatable
+source .venv/bin/activate
+```
+
+Install PyTorch
+- PyTorch 2.6.0 recommended
+```{.bash}
+uv pip install packaging setuptools wheel
+uv pip install torch==2.6.0
+uv pip install awscli pydantic
+```
+
+Install axolotl from PyPi
+```{.bash}
+uv pip install --no-build-isolation axolotl[deepspeed,flash-attn]
+
+# optionally install with vLLM if you're using torch==2.6.0 and want to train w/ GRPO
+uv pip install --no-build-isolation axolotl[deepspeed,flash-attn,vllm]
+```
+
 ### Edge/Development Build {#sec-edge-build}
 
 For the latest features between releases:
@@ -72,6 +110,10 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
 ```
 :::
 
+::: {.callout-important}
+For Blackwell GPUs, please use `axolotlai/axolotl:main-py3.11-cu128-2.7.0` or the cloud variant `axolotlai/axolotl-cloud:main-py3.11-cu128-2.7.0`.
+:::
+
 Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.
 
 ## Cloud Environments {#sec-cloud}

docs/lora_optims.qmd

@@ -84,6 +84,10 @@ lora_qkv_kernel: true
 lora_o_kernel: true
 ```
 
+::: {.callout-note}
+Currently, LoRA kernels are not supported for RLHF training, only SFT.
+:::
+
 ## Requirements
 
 - One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)

docs/multi-gpu.qmd

@@ -87,20 +87,7 @@ We support sequence parallelism (SP) via the
 allows one to split up sequences across GPUs, which is useful in the event that a
 single sequence causes OOM errors during model training.
 
-First, install `ring-flash-attn`, recommended via `pip install axolotl[ring-flash-attn]`,
-or from source with `pip install .[ring-flash-attn]`.
-
-Your Axolotl YAML config should contain the following lines:
-
-```{.yaml}
-sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # Required with sequence parallelism
-
-# Optional; strides across the key dimension. Larger values use more memory but will make training faster.
-heads_k_stride: 1
-```
-
-See our [dedicated guide](sequence_parallelism.qmd) for more details.
+See our [dedicated guide](sequence_parallelism.qmd) for more information.
 
 ### FSDP + QLoRA {#sec-fsdp-qlora}
 

docs/multimodal.qmd

@@ -43,7 +43,7 @@ datasets:
 # leave the vision model and vision tower frozen
 # load_in_8bit: true
 adapter: lora
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 # (optional) if you want to resize images to a set size
 image_size: 512
@@ -164,7 +164,7 @@ Here is an example of a multi-modal dataset:
         {
             "role": "user",
             "content": [
-                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
                 {"type": "text", "text": "Describe this image in detail."}
             ]
         },

docs/qat.qmd

@@ -0,0 +1,32 @@
+---
+title: "Quantization Aware Training (QAT)"
+back-to-top-navigation: true
+toc: true
+toc-expand: 2
+toc-depth: 4
+---
+
+## Overview
+
+[Quantization Aware Training](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) (QAT) is a technique for improving the accuracy of models which are quantized
+by applying "fake" quantizations to the model's weights (and optionally, activations) during training. This fake
+quantization allows for the model to adjust for noise introduced by the quantization, so when the model is eventually
+quantized, the accuracy loss is minimized. We use the quantization techniques implemented in [torchao](https://github.com/pytorch/ao) to provide
+support for QAT and post-training quantization (PTQ) in axolotl.
+
+We recommend reviewing the excellent QAT tutorial in the [torchtune library](https://pytorch.org/torchtune/main/tutorials/qat_finetune.html#quantizing-the-qat-model),
+and the QAT documentation in the [torchao library](https://github.com/pytorch/ao/tree/main/torchao/quantization/qat), for more details.
+
+## Configuring QAT in Axolotl
+
+To enable QAT in axolotl, add the following to your configuration file:
+
+```yaml
+qat:
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4" and "int8"
+  group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
+  fake_quant_after_n_steps: # Optional[int] = None. The number of steps to apply fake quantization after
+```
+
+Once you have finished training, you must quantize your model by using the same quantization configuration which you used to train the model with. You can use the [`quantize`](./quantize.qmd) command to do this.

docs/quantize.qmd

@@ -0,0 +1,53 @@
+---
+title: "Quantization with torchao"
+back-to-top-navigation: true
+toc: true
+toc-expand: 2
+toc-depth: 4
+---
+
+Quantization is a technique to lower the memory footprint of your model, potentially at the cost of accuracy or model performance. We support quantizing your model using the [torchao](https://github.com/pytorch/ao) library. Quantization is supported for both post-training quantization (PTQ) and quantization-aware training (QAT).
+
+
+::: {.callout-note}
+
+We do not currently support quantization techniques such as GGUF/GPTQ,EXL2 at the moment.
+
+:::
+
+## Configuring Quantization in Axolotl
+
+Quantization is configured using the `quantization` key in your configuration file.
+
+```yaml
+base_model: # The path to the model to quantize.
+quantization:
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are uintX for X in [1, 2, 3, 4, 5, 6, 7], or int4, or int8
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
+  group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
+  quantize_embedding: # Optional[bool] = False. Whether to quantize the embedding layer.
+
+output_dir:  # The path to the output directory.
+```
+
+Once quantization is complete, your quantized model will be saved in the `{output_dir}/quantized` directory.
+
+You may also use the `quantize` command to quantize a model which has been trained with [QAT](./qat.md) - you can do this by using the existing QAT configuration file which
+you used to train the model:
+
+```yaml
+# qat.yml
+qat:
+  activation_dtype: int8
+  weight_dtype: int8
+  group_size: 256
+  quantize_embedding: true
+
+output_dir: # The path to the output directory used during training where the final checkpoint has been saved.
+```
+
+```bash
+axolotl quantize qat.yml
+```
+
+This ensures that an identical quantization configuration is used to quantize the model as was used to train it.

docs/rlhf.qmd

@@ -16,7 +16,8 @@ feedback. Various methods include, but not limited to:
 - [Identity Preference Optimization (IPO)](#ipo)
 - [Kahneman-Tversky Optimization (KTO)](#kto)
 - [Odds Ratio Preference Optimization (ORPO)](#orpo)
-- Proximal Policy Optimization (PPO) (not yet supported in axolotl)
+- [Group Relative Policy Optimization (GRPO)](#grpo)
+- Proximal Policy Optimization (PPO) (not yet supported in axolotl, if you're interested in contributing, please reach out!)
 
 
 ## RLHF using Axolotl
@@ -499,7 +500,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 ### GRPO
 
 ::: {.callout-tip}
-Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
+Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/grpo_code).
 :::
 
 In the latest GRPO implementation, `vLLM` is used to significantly speedup trajectory generation during training. In this example, we're using 4 GPUs - 2 for training, and 2 for vLLM:
@@ -582,7 +583,20 @@ datasets:
 
 To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).
 
-To see description of the configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/models/input/v0_4_1/trl.py).
+To see all configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/src/axolotl/utils/schemas/trl.py).
+
+#### GRPO with DAPO/Dr. GRPO loss
+
+The DAPO paper and subsequently Dr. GRPO paper proposed an alternative loss function for GRPO to remediate the penalty in longer responses.
+
+```yaml
+trl:
+  loss_type: dr_grpo
+  # Normalizes loss based on max completion length (default: 256)
+  max_completion_length:
+```
+
+For more information, see [GRPO docs](https://huggingface.co/docs/trl/v0.17.0/en/grpo_trainer#loss-types).
 
 ### SimPO
 

docs/sequence_parallelism.qmd

@@ -3,8 +3,6 @@ title: Sequence Parallelism
 description: Train with long sequences split across multiple GPUs.
 ---
 
-# Sequence Parallelism
-
 Sequence parallelism is a technique that splits sequences across multiple GPUs,
 allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
 GPU processes a different portion of the sequence, and the results are aggregated
@@ -27,7 +25,7 @@ To enable sequence parallelism, add the following to your configuration file:
 sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
-# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
+# Optional; one of "varlen_llama3" or "batch_ring". Defaults to
 # "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
 ring_attn_func:
 ```
@@ -43,7 +41,7 @@ When sequence parallelism is enabled:
 
 1. Each sequence is divided into equal chunks across the GPUs in a sequence parallel group
 2. The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids
-3. Position IDs are adjusted to maintain proper relative positions, especially for packed sequences
+3. Position IDs are adjusted to maintain proper relative positions
 4. The trainer uses special ring communication patterns for attention operations
 
 ## Requirements
@@ -69,9 +67,11 @@ sequence_len: 8192
 ...
 
 sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
-flash_attention: true  # Required with sequence parallelism
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
+# Optional; one of "varlen_llama3" or "batch_ring". Defaults to
+# "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
+ring_attn_func:
 
 ...
 ```

examples/gemma3/gemma-3-4b-qlora.yml

@@ -28,7 +28,7 @@ pad_to_sequence_len: true
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -30,7 +30,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/llama-3-vision/lora-11b.yaml

@@ -29,7 +29,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/llama-3/3b-qat-fsdp2.yaml

@@ -0,0 +1,79 @@
+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
+
+output_dir: ./outputs/qat_out/
+
+sample_packing: true
+pad_to_sequence_len: true
+sequence_len: 512
+
+flex_attention: true
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+qat:
+  activation_dtype: int8
+  weight_dtype: int4
+  group_size: 32
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 16
+num_epochs: 1
+optimizer: adamw_torch_fused
+
+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_steps: 10
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+
+fsdp_config:
+  fsdp_version: 2
+  fsdp_offload_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_reshard_after_forward: true
+  fsdp_activation_checkpointing: true
+
+special_tokens:
+  pad_token: <|end_of_text|>

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -5,6 +5,10 @@ tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot_id|>
+
 load_in_8bit: true
 load_in_4bit: false
 

examples/llama-3/lora-1b.yml

@@ -5,7 +5,7 @@ base_model: NousResearch/Llama-3.2-1B
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+
 val_set_size: 0.1
 output_dir: ./outputs/lora-out
 
@@ -38,6 +38,7 @@ wandb_log_model:
 gradient_accumulation_steps: 2
 micro_batch_size: 2
 num_epochs: 1
+
 optimizer: adamw_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002

examples/llama-3/sparse-finetuning.yaml

@@ -0,0 +1,77 @@
+base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
+
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true

examples/llama-4/README.md

@@ -34,3 +34,5 @@ We provide a script to delinearize Llama 4 linearized models into regular Huggin
 ```bash
 axolotl delinearize-llama4 --model path/to/model_dir --output path/to/output_dir
 ```
+
+Note: This only works with the non-quantized linearized model. If you have an adapter, merge it with the *non-quantized linearized* model before delinearizing.

examples/llava/lora-7b.yaml

@@ -25,7 +25,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/magistral/README.md

@@ -0,0 +1,71 @@
+# Finetune Magistral Small with Axolotl
+
+Magistral Small is a 24B parameter opensource model from MistralAI found on [HuggingFace](https://huggingface.co/mistralai/Magistral-Small-2506). This guide shows how to fine-tune it with Axolotl with multi-turn conversations with proper masking.
+
+MistralAI has also released a proprietary medium-sized version called Magistral Medium.
+
+Thanks to the team at MistralAI for giving us early access to prepare for this release.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Magistral is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 recommended)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn,mistral]'
+```
+
+2. Download the example config:
+
+```bash
+axolotl fetch examples
+```
+
+3. Run the finetuning example:
+
+```bash
+axolotl train examples/magistral/magistral-small-qlora.yaml
+```
+
+This config uses about 24GB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### TIPS
+
+- For inference, the official MistralAI team recommends `top_p: 0.95` and `temperature: 0.7` with `max_tokens: 40960`.
+- 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 at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format is the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Limitations
+
+We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
+
+The tokenizer does not work with `dataset.map` with multiprocessing, so we had to disable it. In addition, we do not support overriding tokens yet.
+
+## Related Resources
+
+- [MistralAI Magistral Blog](https://mistral.ai/news/magistral/)
+- [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)
+
+
+## Future Work
+
+- Add parity to Preference Tuning, RL, Multi-modal, etc.
+- Add parity to other tokenizer configs like overriding tokens.

examples/magistral/magistral-small-fsdp-qlora.yaml

@@ -0,0 +1,72 @@
+base_model: mistralai/Magistral-Small-2506
+
+# Enable to use mistral-common tokenizer
+tokenizer_use_mistral_common: true
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: 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/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+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_fused
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing:
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
+  fsdp_activation_checkpointing: true

examples/magistral/magistral-small-qlora.yaml

@@ -0,0 +1,63 @@
+base_model: mistralai/Magistral-Small-2506
+
+# Enable to use mistral-common tokenizer
+tokenizer_use_mistral_common: true
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: 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/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+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

examples/mistral/mistral-small-3.1-24B-lora.yml

@@ -27,7 +27,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/orpheus/README.md

@@ -0,0 +1,341 @@
+# Finetuning LLMs to output audio
+
+In this example, we finetune Orpcanopylabs/orpheus-tts-0.1-pretrained (a LLaMA 3.2 3b model) to output audio.
+
+The `finetune.yml` withe current settings will run on any Nvidia GPU with 45GB VRAM or more. If you adjust the batch size it can easily run on any GPU under 24GB.
+
+## Dataset pre-processing for pre-training
+If you are adding another voice in English, please jump ahead to finetuning pre-processing.
+
+For this to work, we need to preprocess our dataset. Since we are expecting to output audio, we will need to add tokens to the tokenizer.
+
+Using this code, it will download the SNAC model and add the correct tokens and upload the final dataset.
+
+```python
+import torch
+from snac import SNAC
+from datasets import load_dataset
+from huggingface_hub import snapshot_download
+from datasets import load_dataset
+import random
+import torchaudio.transforms as T
+from transformers import AutoTokenizer
+import os
+
+my_original_dataset_name = "<huggingface-id-of-dataset-that-we-want-to-preprocess>"
+name_to_push_dataset_to = "<huggingface-id-of-where-to-save-dataset>"
+
+dsn = my_original_dataset_name
+
+snapshot_download(
+    repo_id=dsn,
+    repo_type="dataset",
+    revision="main",
+    max_workers=64,
+)
+
+
+ds = load_dataset(dsn, split="train")
+ds_sample_rate = ds[0]["audio"]["sampling_rate"]
+
+model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+model = model.to("mps")
+
+def tokenise_audio(waveform):
+  waveform = torch.from_numpy(waveform).unsqueeze(0)
+  waveform = waveform.to(dtype=torch.float32)
+  resample_transform = T.Resample(orig_freq=ds_sample_rate, new_freq=24000)
+  waveform = resample_transform(waveform)
+
+  waveform = waveform.unsqueeze(0).to("cuda")
+
+  #generate the codes from snac
+  with torch.inference_mode():
+    codes = model.encode(waveform)
+
+  all_codes = []
+  for i in range(codes[0].shape[1]):
+    all_codes.append(codes[0][0][i].item()+128266)
+    all_codes.append(codes[1][0][2*i].item()+128266+4096)
+    all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))
+    all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))
+    all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))
+    all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))
+    all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))
+
+
+  return all_codes
+
+def add_codes(example):
+    # Always initialize codes_list to None
+    codes_list = None
+
+    try:
+        answer_audio = example.get("audio")
+        # If there's a valid audio array, tokenise it
+        if answer_audio and "array" in answer_audio:
+            audio_array = answer_audio["array"]
+            codes_list = tokenise_audio(audio_array)
+    except Exception as e:
+        print(f"Skipping row due to error: {e}")
+        # Keep codes_list as None if we fail
+    example["codes_list"] = codes_list
+
+    return example
+
+ds = ds.map(add_codes, remove_columns=["audio"])
+
+#@title Load Tokenizer
+tokeniser_length = 128256
+start_of_text = 128000
+end_of_text = 128009
+
+start_of_speech = tokeniser_length + 1
+end_of_speech = tokeniser_length + 2
+
+start_of_human = tokeniser_length + 3
+end_of_human = tokeniser_length + 4
+
+start_of_ai = tokeniser_length + 5
+end_of_ai =  tokeniser_length + 6
+pad_token = tokeniser_length + 7
+
+audio_tokens_start = tokeniser_length + 10
+
+tokenizer_name = "canopylabs/orpheus-3b-0.1-pretrained"
+
+
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+num_proc = os.cpu_count() - 2
+
+ds = ds.filter(lambda x: x["codes_list"] is not None)
+ds = ds.filter(lambda x: len(x["codes_list"]) > 0)
+
+#@title Create Input Ids
+def remove_duplicate_frames(example):
+    vals = example["codes_list"]
+    if len(vals) % 7 != 0:
+        raise ValueError("Input list length must be divisible by 7")
+
+    result = vals[:7]
+
+    removed_frames = 0
+
+    for i in range(7, len(vals), 7):
+        current_first = vals[i]
+        previous_first = result[-7]
+
+        if current_first != previous_first:
+            result.extend(vals[i:i+7])
+        else:
+            removed_frames += 1
+
+    example["codes_list"] = result
+
+    return example
+
+ds = ds.map(remove_duplicate_frames, num_proc=num_proc)
+
+
+def create_input_ids(example):
+    text_ids = tokenizer.encode({example['text']},  add_special_tokens=True)
+    text_ids.append(end_of_text)
+    example["text_tokens"] = text_ids
+    input_ids = (
+        [start_of_human]
+        + example["text_tokens"]
+        + [end_of_human]
+        + [start_of_ai]
+        + [start_of_speech]
+        + example["codes_list"]
+        + [end_of_speech]
+        + [end_of_ai]
+    )
+    example["input_ids"] = input_ids
+    example["labels"] = input_ids
+    example["attention_mask"] = [1] * len(input_ids)
+
+    return example
+
+ds = ds.map(create_input_ids, num_proc=num_proc, remove_columns=["text", "codes_list"])
+
+#@title Remove unnecessary columns
+columns_to_keep = ["input_ids", "labels", "attention_mask"]
+columns_to_remove = [col for col in ds.column_names if col not in columns_to_keep]
+
+ds = ds.remove_columns(columns_to_remove)
+
+ds.push_to_hub(name_to_push_dataset_to)
+```
+
+
+## Finetune pre-processing
+Use this code to add a new voice.
+
+```python
+import torch
+from snac import SNAC
+from datasets import load_dataset
+from huggingface_hub import snapshot_download
+from datasets import load_dataset
+import random
+import torchaudio.transforms as T
+from transformers import AutoTokenizer
+import os
+
+my_original_dataset_name = "<huggingface-id-of-dataset-that-we-want-to-preprocess>"
+name_to_push_dataset_to = "<huggingface-id-of-where-to-save-dataset>"
+
+dsn = my_original_dataset_name
+
+snapshot_download(
+    repo_id=dsn,
+    repo_type="dataset",
+    revision="main",
+    max_workers=64,
+)
+
+
+ds = load_dataset(dsn, split="train")
+ds_sample_rate = ds[0]["audio"]["sampling_rate"]
+
+model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+model = model.to("mps")
+
+def tokenise_audio(waveform):
+  waveform = torch.from_numpy(waveform).unsqueeze(0)
+  waveform = waveform.to(dtype=torch.float32)
+  resample_transform = T.Resample(orig_freq=ds_sample_rate, new_freq=24000)
+  waveform = resample_transform(waveform)
+
+  waveform = waveform.unsqueeze(0).to("cuda")
+
+  #generate the codes from snac
+  with torch.inference_mode():
+    codes = model.encode(waveform)
+
+  all_codes = []
+  for i in range(codes[0].shape[1]):
+    all_codes.append(codes[0][0][i].item()+128266)
+    all_codes.append(codes[1][0][2*i].item()+128266+4096)
+    all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))
+    all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))
+    all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))
+    all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))
+    all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))
+
+
+  return all_codes
+
+def add_codes(example):
+    # Always initialize codes_list to None
+    codes_list = None
+
+    try:
+        answer_audio = example.get("audio")
+        # If there's a valid audio array, tokenise it
+        if answer_audio and "array" in answer_audio:
+            audio_array = answer_audio["array"]
+            codes_list = tokenise_audio(audio_array)
+    except Exception as e:
+        print(f"Skipping row due to error: {e}")
+        # Keep codes_list as None if we fail
+    example["codes_list"] = codes_list
+
+    return example
+
+ds = ds.map(add_codes, remove_columns=["audio"])
+
+#@title Load Tokenizer
+tokeniser_length = 128256
+start_of_text = 128000
+end_of_text = 128009
+
+start_of_speech = tokeniser_length + 1
+end_of_speech = tokeniser_length + 2
+
+start_of_human = tokeniser_length + 3
+end_of_human = tokeniser_length + 4
+
+start_of_ai = tokeniser_length + 5
+end_of_ai =  tokeniser_length + 6
+pad_token = tokeniser_length + 7
+
+audio_tokens_start = tokeniser_length + 10
+
+tokenizer_name = "canopylabs/orpheus-3b-0.1-pretrained"
+
+
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+num_proc = os.cpu_count() - 2
+
+ds = ds.filter(lambda x: x["codes_list"] is not None)
+ds = ds.filter(lambda x: len(x["codes_list"]) > 0)
+
+#@title Create Input Ids
+def remove_duplicate_frames(example):
+    vals = example["codes_list"]
+    if len(vals) % 7 != 0:
+        raise ValueError("Input list length must be divisible by 7")
+
+    result = vals[:7]
+
+    removed_frames = 0
+
+    for i in range(7, len(vals), 7):
+        current_first = vals[i]
+        previous_first = result[-7]
+
+        if current_first != previous_first:
+            result.extend(vals[i:i+7])
+        else:
+            removed_frames += 1
+
+    example["codes_list"] = result
+
+    return example
+
+ds = ds.map(remove_duplicate_frames, num_proc=num_proc)
+
+tok_info = '''*** HERE you can modify the text prompt
+i.e. if you wanted a multispeaker model like canopylabs/orpheus-3b-0.1-ft, you can pass:
+f"{example["source"]}:  {example["text"]}", as is passed.
+'''
+print(tok_info)
+
+def create_input_ids(example):
+    text_ids = tokenizer.encode(f"{example['speaker_id']}: {example['text']}",  add_special_tokens=True)
+    text_ids.append(end_of_text)
+    example["text_tokens"] = text_ids
+    input_ids = (
+        [start_of_human]
+        + example["text_tokens"]
+        + [end_of_human]
+        + [start_of_ai]
+        + [start_of_speech]
+        + example["codes_list"]
+        + [end_of_speech]
+        + [end_of_ai]
+    )
+    example["input_ids"] = input_ids
+    example["labels"] = input_ids
+    example["attention_mask"] = [1] * len(input_ids)
+
+    return example
+
+ds = ds.map(create_input_ids, num_proc=num_proc, remove_columns=["text", "codes_list"])
+
+#@title Remove unnecessary columns
+columns_to_keep = ["input_ids", "labels", "attention_mask"]
+columns_to_remove = [col for col in ds.column_names if col not in columns_to_keep]
+
+ds = ds.remove_columns(columns_to_remove)
+
+ds.push_to_hub(name_to_push_dataset_to)
+```
+
+## Training
+After preprocessing is done, fill out the blanks in finetune.yml and simply run `axolotl train finetune.yml`
+
+## Inference
+For inference, please refer to the original [orpheus github](https://github.com/canopyai/Orpheus-TTS/tree/main).

examples/orpheus/finetune.yml

@@ -0,0 +1,52 @@
+base_model: canopylabs/orpheus-3b-0.1-pretrained
+
+hub_model_id: <your-hub-model-id>
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_fused_linear_cross_entropy: true
+
+datasets:
+  - path: <your-hf-dataset-id>
+    type:  # leave empty to load pre-tokenized
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 4
+num_epochs: 3
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 20
+evals_per_epoch: 5
+saves_per_epoch: 5
+weight_decay: 0.05
+
+special_tokens:
+  pad_token: <custom_token_7>

examples/pixtral/lora-12b.yml

@@ -25,7 +25,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/qwen2-vl/lora-7b.yaml

@@ -25,7 +25,7 @@ pad_to_sequence_len: false
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
-lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 wandb_project:
 wandb_entity:

examples/qwen2/dpo.yaml

@@ -2,7 +2,6 @@ base_model: Qwen/Qwen2.5-0.5B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
-
 chat_template: qwen_25
 rl: dpo
 datasets:

examples/qwen3/8b-qat-fsdp2.yml

@@ -0,0 +1,78 @@
+base_model: Qwen/Qwen3-8B
+# 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: tatsu-lab/alpaca
+    type: alpaca
+
+output_dir: ./outputs/qat_out/
+
+sequence_len: 2048
+sample_packing: true
+flex_attention: true
+pad_to_sequence_len: true
+
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+qat:
+  activation_dtype: int8
+  weight_dtype: int4
+  group_size: 256
+  fake_quant_after_n_steps: 1000
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 2
+max_steps: 2000
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+resume_from_checkpoint:
+logging_steps: 1
+
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+warmup_steps: 10
+weight_decay: 0.0
+fsdp:
+  - full_shard
+  - auto_wrap
+
+fsdp_config:
+  fsdp_version: 2
+  fsdp_offload_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_reshard_after_forward: true
+  fsdp_activation_checkpointing: true
+
+special_tokens:

requirements.txt

@@ -6,20 +6,20 @@ triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.8
+liger-kernel==0.5.10
 # END section
 
 packaging==23.2
 
+huggingface_hub==0.32.2
 peft==0.15.2
-transformers==4.51.3
+transformers==4.52.3
 tokenizers>=0.21.1
-accelerate==1.6.0
-datasets==3.5.0
-deepspeed>=0.15.4
-trl==0.17.0
-hf_xet==1.0.0
-hqq==0.2.5
+accelerate==1.7.0
+datasets==3.6.0
+deepspeed>=0.17.0
+trl==0.18.1
+hf_xet==1.1.2
 
 optimum==1.16.2
 hf_transfer
@@ -62,8 +62,10 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.9.0
+torchao==0.10.0
 schedulefree==1.4.1
 
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3
+
+mistral-common==1.6.0

scripts/cutcrossentropy_install.py

@@ -9,6 +9,8 @@ except ImportError as exc:
     raise ImportError("Install torch via `pip install torch`") from exc
 from packaging.version import Version as V
 
+USE_UV = "--uv" in sys.argv[1:]
+
 v = V(torch.__version__)
 
 # no cut-cross-entropy support for torch < 2.4.0
@@ -23,7 +25,9 @@ if cce_spec:
     if not importlib.util.find_spec("cut_cross_entropy.transformers"):
         UNINSTALL_PREFIX = "pip uninstall -y cut-cross-entropy && "
 
+UV_PREFIX = "uv " if USE_UV else ""
+
 print(
     UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@a1174ca"'
 )

scripts/motd

@@ -11,7 +11,7 @@
                                  =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
     @@@@  @@@@@@@@@@@@@@@@
 
-Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace and the axolotl directory ie empty, run the following commands:
+Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace and the axolotl directory is empty, run the following commands:
 
 ```
 cd /workspace

scripts/unsloth_install.py

@@ -1,11 +1,15 @@
 # noqa
 # pylint: skip-file
+import sys
+
 try:
     import torch
 except ImportError:
     raise ImportError("Install torch via `pip install torch`")
 from packaging.version import Version as V
 
+use_uv = "--uv" in sys.argv[1:]
+
 v = V(torch.__version__)
 cuda = str(torch.version.cuda)
 try:
@@ -31,6 +35,7 @@ elif v < V("2.6.0"):
 else:
     raise RuntimeError(f"Torch = {v} too new!")
 x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
+uv_prefix = "uv " if use_uv else ""
 print(
-    f'pip install unsloth-zoo==2024.12.1 && pip install --no-deps "unsloth[{x}]==2024.12.4"'
+    f'{uv_prefix}pip install unsloth-zoo==2024.12.1 && {uv_prefix}pip install --no-deps "unsloth[{x}]==2024.12.4"'
 )

setup.py

@@ -67,13 +67,13 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 # _install_requires.append("xformers==0.0.29.post3")  # xformers seems to be hard pinned to 2.6.0
-                extras_require_map["vllm"] = ["vllm==0.8.4"]
+                extras_require_map["vllm"] = ["vllm==0.8.5.post1"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append(
                     "xformers==0.0.29.post2"
                 )  # vllm needs post2 w torch 2.6
-                extras_require_map["vllm"] = ["vllm==0.8.4"]
+                extras_require_map["vllm"] = ["vllm==0.8.5.post1"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
@@ -118,7 +118,7 @@ extras_require = {
         "yunchang==0.6.0",
     ],
     "deepspeed": [
-        "deepspeed==0.15.4",
+        "deepspeed==0.17.0",
         "deepspeed-kernels",
     ],
     "mamba-ssm": [
@@ -142,6 +142,7 @@ extras_require = {
         "apollo-torch",
         "lomo-optim==0.1.1",
         "torch-optimi==0.2.1",
+        "came_pytorch==0.1.3",
     ],
     "ray": [
         "ray[train]",
@@ -149,6 +150,9 @@ extras_require = {
     "vllm": [
         "vllm==0.7.2",
     ],
+    "llmcompressor": [
+        "llmcompressor==0.5.1",
+    ],
 }
 
 install_requires, dependency_links, extras_require_build = parse_requirements(

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.9.0"
+__version__ = "0.10.0"

src/axolotl/cli/__init__.py

@@ -2,4 +2,7 @@
 
 import os
 
+from axolotl.logging_config import configure_logging
+
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+configure_logging()

src/axolotl/cli/args.py

@@ -28,7 +28,6 @@ class TrainerCliArgs:
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
     debug_num_examples: int = field(default=0)
-    merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)
     shard: bool = field(default=False)
     main_process_port: Optional[int] = field(default=None)
@@ -82,6 +81,32 @@ class VllmServeCliArgs:
             "hardware support this feature."
         },
     )
+    serve_module: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Module to serve. If not set, the default module will be used."
+        },
+    )
+
+    enable_reasoning: Optional[bool] = field(
+        default=None,
+    )
+
+    reasoning_parser: Optional[str] = field(
+        default=None,
+    )
+
+
+@dataclass
+class QuantizeCliArgs:
+    """Dataclass with CLI arguments for `axolotl quantize` command."""
+
+    base_model: Optional[str] = field(default=None)
+    weight_dtype: Optional[str] = field(default=None)
+    activation_dtype: Optional[str] = field(default=None)
+    quantize_embedding: Optional[bool] = field(default=None)
+    group_size: Optional[int] = field(default=None)
+    output_dir: Optional[str] = field(default=None)
 
 
 @dataclass

src/axolotl/cli/art.py

@@ -16,8 +16,15 @@ AXOLOTL_LOGO = """
     @@@@  @@@@@@@@@@@@@@@@
 """
 
+HAS_PRINTED_LOGO = False
+
 
 def print_axolotl_text_art():
     """Prints axolotl ASCII art."""
+
+    global HAS_PRINTED_LOGO  # pylint: disable=global-statement
+    if HAS_PRINTED_LOGO:
+        return
     if is_main_process():
+        HAS_PRINTED_LOGO = True
         print(AXOLOTL_LOGO)

src/axolotl/cli/checks.py

@@ -1,6 +1,5 @@
 """Various checks for Axolotl CLI."""
 
-import logging
 import os
 from pathlib import Path
 
@@ -8,10 +7,9 @@ from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 
-from axolotl.logging_config import configure_logging
+from axolotl.utils.logging import get_logger
 
-configure_logging()
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def check_accelerate_default_config() -> None:

src/axolotl/cli/cloud/modal_.py

@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.5.1"
+        docker_tag = "main-py3.11-cu124-2.6.0"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"

src/axolotl/cli/config.py

@@ -1,10 +1,10 @@
 """Configuration loading and processing."""
 
 import json
-import logging
 import os
 import tempfile
 from pathlib import Path
+from tempfile import NamedTemporaryFile
 from typing import Union
 from urllib.parse import urlparse
 
@@ -21,11 +21,12 @@ from axolotl.utils.config import (
     validate_config,
 )
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__, use_environ=True)
 
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
@@ -118,12 +119,12 @@ def choose_config(path: Path) -> str:
         )
 
     if len(yaml_files) == 1:
-        print(f"Using default YAML file '{yaml_files[0]}'")
+        LOG.info(f"Using default YAML file '{yaml_files[0]}'")
         return str(yaml_files[0])
 
-    print("Choose a YAML file:")
+    LOG.info("Choose a YAML file:")
     for idx, file in enumerate(yaml_files):
-        print(f"{idx + 1}. {file}")
+        LOG.info(f"{idx + 1}. {file}")
 
     chosen_file = None
     while chosen_file is None:
@@ -132,9 +133,9 @@ def choose_config(path: Path) -> str:
             if 1 <= choice <= len(yaml_files):
                 chosen_file = str(yaml_files[choice - 1])
             else:
-                print("Invalid choice. Please choose a number from the list.")
+                LOG.info("Invalid choice. Please choose a number from the list.")
         except ValueError:
-            print("Invalid input. Please enter a number.")
+            LOG.info("Invalid input. Please enter a number.")
 
     return chosen_file
 
@@ -152,7 +153,15 @@ def prepare_plugins(cfg: DictDefault):
             plugin_manager.register(plugin_name)
 
 
-def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefault:
+def plugin_set_cfg(cfg: DictDefault):
+    if cfg.get("plugins"):
+        plugin_manager = PluginManager.get_instance()
+        plugin_manager.cfg = cfg
+
+
+def load_cfg(
+    config: str | Path | DictDefault = Path("examples/"), **kwargs
+) -> DictDefault:
     """
     Loads the `axolotl` configuration stored at `config`, validates it, and performs
     various setup.
@@ -164,13 +173,24 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     Returns:
         `DictDefault` mapping configuration keys to values.
     """
-    config = check_remote_config(config)
-    if Path(config).is_dir():
-        config = choose_config(Path(config))
+    if isinstance(config, (str, Path)):
+        config = check_remote_config(config)
+        if Path(config).is_dir():
+            config = choose_config(Path(config))
 
-    # Load the config from the yaml file
-    with open(config, encoding="utf-8") as file:
-        cfg: DictDefault = DictDefault(yaml.safe_load(file))
+        # Load the config from the yaml file
+        with open(config, encoding="utf-8") as file:
+            cfg: DictDefault = DictDefault(yaml.safe_load(file))
+
+        cfg.axolotl_config_path = config
+    else:
+        cfg = config
+        with NamedTemporaryFile(
+            mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+        ) as temp_file:
+            temp_file.write(yaml.dump(config.to_dict()))
+            temp_file.close()
+        cfg.axolotl_config_path = temp_file.name
 
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
@@ -184,8 +204,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
             else:
                 cfg[k] = kwargs[k]
 
-    cfg.axolotl_config_path = config
-
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
@@ -213,5 +231,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs) -> DictDefa
     setup_wandb_env_vars(cfg)
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
+    plugin_set_cfg(cfg)
 
     return cfg

src/axolotl/cli/evaluate.py

@@ -1,6 +1,6 @@
 """CLI to run evaluation on a model."""
 
-import logging
+import os
 from pathlib import Path
 from typing import Union
 
@@ -14,9 +14,11 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.evaluate import evaluate
+from axolotl.utils import patch_optimized_env
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
@@ -29,10 +31,14 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: CLI arguments.
     """
+    # Enable expandable segments for cuda allocation to improve VRAM usage
+    patch_optimized_env()
+
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     check_accelerate_default_config()
-    check_user_token()
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        check_user_token()
 
     if cfg.rl:
         dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/cli/inference.py

@@ -1,7 +1,6 @@
 """CLI to run inference on a trained model."""
 
 import importlib
-import logging
 import sys
 from pathlib import Path
 from threading import Thread
@@ -22,8 +21,9 @@ from axolotl.utils.chat_templates import (
     get_chat_template_from_config,
 )
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def get_multi_line_input() -> str:

src/axolotl/cli/main.py

@@ -2,7 +2,6 @@
 
 # pylint: disable=redefined-outer-name
 
-import logging
 import os
 import subprocess  # nosec B404
 import tempfile
@@ -17,6 +16,7 @@ import axolotl
 from axolotl.cli.args import (
     EvaluateCliArgs,
     PreprocessCliArgs,
+    QuantizeCliArgs,
     TrainerCliArgs,
     VllmServeCliArgs,
 )
@@ -28,11 +28,13 @@ from axolotl.cli.utils import (
     fetch_from_github,
     filter_none_kwargs,
 )
-from axolotl.cli.vllm_serve import do_vllm_serve
 from axolotl.integrations.lm_eval.cli import lm_eval
-from axolotl.utils import set_pytorch_cuda_alloc_conf
+from axolotl.utils import patch_optimized_env
+from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.config import AxolotlInputConfig
 
+LOG = get_logger(__name__)
+
 
 @click.group()
 @click.version_option(version=axolotl.__version__, prog_name="axolotl")
@@ -56,6 +58,8 @@ def preprocess(config: str, cloud: Optional[str] = None, **kwargs) -> None:
         kwargs: Additional keyword arguments which correspond to CLI args or `axolotl`
             config options.
     """
+    patch_optimized_env()
+
     if cloud:
         from axolotl.cli.cloud import do_cli_preprocess
 
@@ -101,7 +105,7 @@ def train(
             config options.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
+    patch_optimized_env()
 
     if "use_ray" in kwargs and kwargs["use_ray"]:
         accelerate = False
@@ -175,7 +179,7 @@ def train(
 
                     do_cli(config=cfg_file, **kwargs)
         except subprocess.CalledProcessError as exc:
-            logging.error(f"Failed to train/fine-tune config '{cfg_file}': {exc}")
+            LOG.error(f"Failed to train/fine-tune config '{cfg_file}': {exc}")
             if not sweep:
                 raise exc
 
@@ -327,9 +331,21 @@ def fetch(directory: str, dest: Optional[str]) -> None:
 @add_options_from_dataclass(VllmServeCliArgs)
 @filter_none_kwargs
 def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
+    from axolotl.cli.vllm_serve import do_vllm_serve
+
     do_vllm_serve(config, cli_args)
 
 
+@cli.command()
+@click.argument("config", type=click.Path(exists=True, path_type=str))
+@add_options_from_dataclass(QuantizeCliArgs)
+@filter_none_kwargs
+def quantize(config: str, **cli_args: QuantizeCliArgs):
+    from axolotl.cli.quantize import do_quantize
+
+    do_quantize(config, cli_args)
+
+
 @cli.command()
 @click.argument("model", type=click.Path(exists=True, path_type=str))
 @click.argument("output", type=click.Path(exists=False, path_type=str))

src/axolotl/cli/merge_lora.py

@@ -1,20 +1,18 @@
 """CLI to merge a trained LoRA into a base model."""
 
-import logging
 from pathlib import Path
 from typing import Union
 
 import fire
-import transformers
 from dotenv import load_dotenv
 
-from axolotl.cli.args import TrainerCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def do_merge_lora(*, cfg: DictDefault) -> None:
@@ -68,12 +66,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     Raises:
         ValueError: If target directory for LoRA merged model does not exist.
     """
-    # pylint: disable=duplicate-code
-    parser = transformers.HfArgumentParser(TrainerCliArgs)
-    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
-        return_remaining_strings=True
-    )
-    parsed_cli_args.merge_lora = True
 
     parsed_cfg = load_cfg(
         config,

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -1,7 +1,6 @@
 """CLI to merge sharded FSDP model checkpoints into a single combined checkpoint."""
 
 import json
-import logging
 import os
 import shutil
 from pathlib import Path
@@ -11,7 +10,6 @@ import fire
 import torch
 import torch.distributed.checkpoint as dist_cp
 import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
-import transformers
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
@@ -24,11 +22,11 @@ from huggingface_hub import split_torch_state_dict_into_shards
 from safetensors.torch import save_file as safe_save_file
 from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
-from axolotl.cli.args import TrainerCliArgs
 from axolotl.cli.art import print_axolotl_text_art
 from axolotl.cli.config import load_cfg
+from axolotl.utils.logging import get_logger
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
@@ -197,11 +195,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     """
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parser = transformers.HfArgumentParser(TrainerCliArgs)
-    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
-        return_remaining_strings=True
-    )
-    parsed_cli_args.merge_lora = True
     parsed_cfg = load_cfg(config, **kwargs)
 
     fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"

src/axolotl/cli/preprocess.py

@@ -1,6 +1,5 @@
 """CLI to run preprocessing of a dataset."""
 
-import logging
 import warnings
 from pathlib import Path
 from typing import Union
@@ -18,10 +17,12 @@ from axolotl.cli.checks import check_accelerate_default_config, check_user_token
 from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
+from axolotl.integrations.base import PluginManager
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.logging import get_logger
 from axolotl.utils.trainer import disable_datasets_caching
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
@@ -47,7 +48,10 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
         cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
     with disable_datasets_caching():
-        if cfg.rl:
+        plugin_manager = PluginManager.get_instance()
+        if plugin_manager.load_datasets(cfg, preprocess=True):
+            pass
+        elif cfg.rl:
             load_preference_datasets(cfg=cfg, cli_args=cli_args)
         else:
             load_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/cli/quantize.py

@@ -0,0 +1,90 @@
+"""
+CLI to post-training quantize a model using torchao
+"""
+
+from pathlib import Path
+from typing import Union
+
+from transformers import AutoModelForCausalLM
+
+from axolotl.cli.art import print_axolotl_text_art
+from axolotl.cli.config import load_cfg
+from axolotl.loaders import load_tokenizer
+from axolotl.utils.logging import get_logger
+from axolotl.utils.quantization import TorchIntDType, quantize_model_for_ptq
+
+LOG = get_logger(__name__)
+
+
+def do_quantize(
+    config: Union[Path, str],
+    cli_args: dict,
+):
+    """
+    Quantizes a model's model's weights
+
+    Args:
+        config (Union[Path, str]): The path to the config file
+        cli_args (dict): Additional command-line arguments
+    """
+    print_axolotl_text_art()
+
+    cfg = load_cfg(config)
+
+    if cfg.qat and cfg.quantization:
+        raise ValueError(
+            "QAT and quantization cannot be used together. Please specify only one of qat or quantization in your config file."
+        )
+
+    if cfg.qat:
+        quantize_cfg = cfg.qat
+    elif cfg.quantization:
+        quantize_cfg = cfg.quantization
+    else:
+        raise ValueError(
+            "No quantization configuration found. Please specify either qat or quantization in your config file."
+        )
+
+    model_path = cli_args.get("model_path") or cfg.output_dir
+    if weight_dtype := cli_args.get("weight_dtype"):
+        weight_dtype = TorchIntDType[weight_dtype]
+    else:
+        weight_dtype = quantize_cfg.weight_dtype
+    if activation_dtype := cli_args.get("activation_dtype"):
+        activation_dtype = TorchIntDType[activation_dtype]
+    else:
+        activation_dtype = quantize_cfg.activation_dtype
+    group_size = cli_args.get("group_size") or quantize_cfg.group_size
+    quantize_embedding = (
+        cli_args.get("quantize_embedding") or quantize_cfg.quantize_embedding
+    )
+    output_dir = cli_args.get("output_dir") or cfg.output_dir
+
+    LOG.info(f"Loading model from {model_path}...")
+    tokenizer = load_tokenizer(cfg)
+    model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
+
+    LOG.info(
+        f"Quantizing model with configuration: \n"
+        f"\tweight_dtype: {weight_dtype}\n"
+        f"\tactivation_dtype: {activation_dtype}\n"
+        f"\tgroup_size: {group_size}\n"
+        f"\tquantize_embedding: {quantize_embedding}"
+    )
+
+    quantize_model_for_ptq(
+        model, weight_dtype, group_size, activation_dtype, quantize_embedding
+    )
+
+    LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}...")
+    model.save_pretrained(
+        str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
+        progressbar=True,
+    )
+    tokenizer.save_pretrained(
+        str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
+        progressbar=True,
+    )
+    LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}...")

src/axolotl/cli/train.py

@@ -1,7 +1,6 @@
 """CLI to run training on a model."""
 
 import gc
-import logging
 import os
 from pathlib import Path
 from typing import Union
@@ -18,12 +17,10 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import set_pytorch_cuda_alloc_conf
+from axolotl.utils import patch_optimized_env
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
-LOG = logging.getLogger(__name__)
-
 
 def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     """
@@ -36,17 +33,20 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         cli_args: Training-specific CLI arguments.
     """
     # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
+    patch_optimized_env()
 
     print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()
 
-    if cfg.rl:
-        dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
-    else:
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+    plugin_manager = PluginManager.get_instance()
+    dataset_meta = plugin_manager.load_datasets(cfg, preprocess=False)
+    if not dataset_meta:
+        if cfg.rl:
+            dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
+        else:
+            dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
 

src/axolotl/cli/utils.py

@@ -4,7 +4,6 @@ import concurrent.futures
 import dataclasses
 import hashlib
 import json
-import logging
 from functools import wraps
 from pathlib import Path
 from types import NoneType
@@ -20,12 +19,12 @@ from transformers import (
     ProcessorMixin,
 )
 
-from axolotl.logging_config import configure_logging
+from axolotl.loaders import load_processor, load_tokenizer
+from axolotl.loaders.model import ModelLoader
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.logging import get_logger
 
-configure_logging()
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 def strip_optional_type(field_type: type | str | None):
@@ -320,7 +319,8 @@ def load_model_and_tokenizer(
     tokenizer = load_tokenizer(cfg)
 
     LOG.info("loading model...")
-    model, _ = load_model(cfg, tokenizer, inference=inference)
+    model_loader = ModelLoader(cfg, tokenizer, inference=inference)
+    model, _ = model_loader.load()
 
     processor = None
     if cfg.is_multimodal:

src/axolotl/cli/vllm_serve.py

@@ -2,15 +2,27 @@
 CLI to start the vllm server for online RL
 """
 
+import os
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Union
 
+import trl
 from trl.scripts.vllm_serve import ScriptArguments
-from trl.scripts.vllm_serve import main as vllm_serve_main
 
 from axolotl.cli.config import load_cfg
 
 
+@dataclass
+class AxolotlScriptArguments(ScriptArguments):
+    """
+    Additional arguments for the VLLM server
+    """
+
+    reasoning_parser: str = field(default="", kw_only=True)
+    enable_reasoning: bool | None = field(default=None, kw_only=True)
+
+
 def do_vllm_serve(
     config: Union[Path, str],
     cli_args: dict,
@@ -25,9 +37,13 @@ def do_vllm_serve(
     Returns:
         process_id: the process id of the started VLLM server
     """
+    patch_vllm_worker()
     cfg = load_cfg(config)
     model = cfg.base_model
 
+    serve_module = cli_args.get("serve_module", "trl.scripts.vllm_serve")
+    vllm_serve_main = getattr(__import__(serve_module, fromlist=["main"]), "main")
+
     tensor_parallel_size = (
         cli_args.get("tensor_parallel_size") or cfg.vllm.tensor_parallel_size
     )
@@ -41,9 +57,16 @@ def do_vllm_serve(
     enable_prefix_caching = (
         cli_args.get("enable_prefix_caching") or cfg.vllm.enable_prefix_caching
     )
+    reasoning_parser = (
+        cli_args.get("reasoning_parser") or cfg.vllm.reasoning_parser or ""
+    )
+    enable_reasoning = (
+        cli_args.get("enable_reasoning") or cfg.vllm.enable_reasoning or False
+    )
 
-    vllm_script_args = ScriptArguments(
-        model,
+    # pylint: disable=unexpected-keyword-arg
+    vllm_script_args = AxolotlScriptArguments(
+        model=model,
         tensor_parallel_size=tensor_parallel_size,
         host=host,
         port=port,
@@ -51,5 +74,67 @@ 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,
     )
     vllm_serve_main(vllm_script_args)
+
+
+def patch_vllm_worker():
+    from multiprocessing.connection import Connection
+
+    from vllm import LLM
+
+    def llm_worker(
+        script_args: AxolotlScriptArguments,
+        data_parallel_rank: int,
+        master_port: int,
+        connection: Connection,
+    ) -> None:
+        # Set required environment variables for DP to work with vLLM
+        os.environ["VLLM_DP_RANK"] = str(data_parallel_rank)
+        os.environ["VLLM_DP_RANK_LOCAL"] = str(data_parallel_rank)
+        os.environ["VLLM_DP_SIZE"] = str(script_args.data_parallel_size)
+        os.environ["VLLM_DP_MASTER_PORT"] = str(master_port)
+
+        llm = LLM(
+            model=script_args.model,
+            revision=script_args.revision,
+            tensor_parallel_size=script_args.tensor_parallel_size,
+            gpu_memory_utilization=script_args.gpu_memory_utilization,
+            enforce_eager=script_args.enforce_eager,
+            dtype=script_args.dtype,
+            # Automatic Prefix Caching caches the KV cache of existing queries, so that a new query can
+            # directly reuse the KV cache if it shares the same prefix with one of the existing queries.
+            # This is particularly useful here because we generate completions from the same prompts.
+            enable_prefix_caching=script_args.enable_prefix_caching,
+            kv_cache_dtype=script_args.kv_cache_dtype,
+            max_model_len=script_args.max_model_len,
+            worker_extension_cls="trl.scripts.vllm_serve.WeightSyncWorkerExtension",
+            enable_reasoning=script_args.enable_reasoning,
+            reasoning_parser=script_args.reasoning_parser,
+        )
+
+        # Send ready signal to parent process
+        connection.send({"status": "ready"})
+
+        while True:
+            # Wait for commands from the parent process
+            try:
+                command = connection.recv()
+            except KeyboardInterrupt:
+                llm.collective_rpc(method="close_communicator")
+                break
+
+            # Handle commands
+            if command["type"] in ["call", "fire_and_forget"]:
+                method_name = command["method"]
+                args, kwargs = command.get("args", ()), command.get("kwargs", {})
+                method = getattr(llm, method_name)
+                result = method(*args, **kwargs)
+                if command["type"] == "call":
+                    connection.send(result)
+            elif command["type"] == "shutdown":
+                break
+
+    trl.scripts.vllm_serve.llm_worker = llm_worker

src/axolotl/common/architectures.py

@@ -11,5 +11,6 @@ MOE_ARCH_BLOCK = {
     ],
     "mixtral": "MixtralSparseMoeBlock",
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
+    "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
 }

src/axolotl/common/const.py

@@ -1,5 +1,3 @@
-"""
-Various shared constants
-"""
+"""Various shared constants"""
 
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"

src/axolotl/common/datasets.py

@@ -1,22 +1,21 @@
 """Dataset loading utilities."""
 
-import logging
 import math
 import random
 from dataclasses import dataclass
-from typing import Optional, Union
 
 from datasets import Dataset
 
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
-from axolotl.utils.data import prepare_dataset
-from axolotl.utils.data.rl import load_prepare_preference_datasets
+from axolotl.loaders import load_processor, load_tokenizer
+from axolotl.utils.data import prepare_datasets, prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_processor, load_tokenizer
+from axolotl.utils.logging import get_logger
+from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.tokenization import check_dataset_labels
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 @dataclass
@@ -29,16 +28,7 @@ class TrainDatasetMeta:
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
-    """
-    Randomly sample `num_samples` samples from `dataset`.
-
-    Args:
-        dataset: Dataset.
-        num_samples: Number of samples to return.
-
-    Returns:
-        Random sample (with replacement) of examples in `dataset`.
-    """
+    """Randomly sample `num_samples` samples with replacement from `dataset`."""
     return dataset.select(
         [random.randrange(0, len(dataset) - 1) for _ in range(num_samples)]  # nosec
     )
@@ -47,29 +37,27 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
 def load_datasets(
     *,
     cfg: DictDefault,
-    cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
+    cli_args: PreprocessCliArgs | TrainerCliArgs | None = None,
+    debug: bool = False,
 ) -> TrainDatasetMeta:
-    """
-    Loads one or more training or evaluation datasets, calling
-    `axolotl.utils.data.prepare_dataset`. Optionally, logs out debug information.
+    """Loads one or more training or evaluation datasets, calling
+    `axolotl.utils.data.prepare_datasets`. Optionally, logs out debug information.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Command-specific CLI arguments.
+        debug: Whether to print out tokenization of sample. This is duplicated in
+            `cfg` and `cli_args`, but is kept due to use in our Colab notebooks.
 
     Returns:
         Dataclass with fields for training and evaluation datasets and the computed
-        `total_num_steps`.
+            `total_num_steps`.
     """
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
-    preprocess_iterable = (
-        hasattr(cli_args, "iterable")
-        and cli_args.iterable is not None
-        and cli_args.iterable
-    )
+    preprocess_iterable = getattr(cli_args, "iterable", False)
 
-    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
+    train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
         cfg,
         tokenizer,
         processor=processor,
@@ -77,19 +65,22 @@ def load_datasets(
     )
 
     if (
-        cli_args.debug
-        or cfg.debug
-        or cli_args.debug_text_only
-        or int(cli_args.debug_num_examples) > 0
+        cfg.debug
+        or getattr(cli_args, "debug", False)
+        or getattr(cli_args, "debug_text_only", False)
+        or getattr(cli_args, "debug_num_examples", 0) > 0
+        or debug
     ):
         LOG.info("check_dataset_labels...")
 
-        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
+        num_examples = cli_args.debug_num_examples if cli_args else 1
+        text_only = cli_args.debug_text_only if cli_args else False
+        train_samples = sample_dataset(train_dataset, num_examples)
         check_dataset_labels(
             train_samples,
             tokenizer,
-            num_examples=cli_args.debug_num_examples,
-            text_only=cli_args.debug_text_only,
+            num_examples=num_examples,
+            text_only=text_only,
         )
 
         LOG.info("printing prompters...")
@@ -104,13 +95,10 @@ def load_datasets(
 
 
 def load_preference_datasets(
-    *,
-    cfg: DictDefault,
-    cli_args: Union[PreprocessCliArgs, TrainerCliArgs],
+    *, cfg: DictDefault, cli_args: PreprocessCliArgs | TrainerCliArgs | None = None
 ) -> TrainDatasetMeta:
-    """
-    Loads one or more training or evaluation datasets for RL training using paired
-    preference data, calling `axolotl.utils.data.rl.load_prepare_preference_datasets`.
+    """Loads one or more training or evaluation datasets for RL training using paired
+    preference data, calling `axolotl.utils.data.rl.prepare_preference_datasets`.
     Optionally, logs out debug information.
 
     Args:
@@ -121,23 +109,28 @@ def load_preference_datasets(
         Dataclass with fields for training and evaluation datasets and the computed
         `total_num_steps`.
     """
-    train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-    total_num_steps: Optional[int] = int(
-        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-    )
-    if cfg.rl == "grpo":
-        total_num_steps = None
+    tokenizer = load_tokenizer(cfg)
+    train_dataset, eval_dataset = prepare_preference_datasets(cfg, tokenizer)
 
-    if cli_args.debug or cfg.debug:
+    total_num_steps: int | None = None
+    if cfg.rl is not RLType.GRPO:
+        total_num_steps = int(
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+        )
+
+    if (cli_args and cli_args.debug) or cfg.debug:
         LOG.info("check_dataset_labels...")
 
+        num_examples = cli_args.debug_num_examples if cli_args else 1
+        text_only = cli_args.debug_text_only if cli_args else False
+
         tokenizer = load_tokenizer(cfg)
-        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
+        train_samples = sample_dataset(train_dataset, num_examples)
         check_dataset_labels(
-            train_samples,
-            tokenizer,
-            num_examples=cli_args.debug_num_examples,
-            text_only=cli_args.debug_text_only,
+            dataset=train_samples,
+            tokenizer=tokenizer,
+            num_examples=num_examples,
+            text_only=text_only,
             rl_mode=True,
         )
 

src/axolotl/core/builders/__init__.py

@@ -0,0 +1,6 @@
+"""Trainer builder classes"""
+
+from .causal import HFCausalTrainerBuilder
+from .rl import HFRLTrainerBuilder
+
+__all__ = ["HFCausalTrainerBuilder", "HFRLTrainerBuilder"]

src/axolotl/core/builders/base.py

@@ -0,0 +1,508 @@
+# Copyright 2024 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.
+
+"""Base class for trainer builder"""
+
+import abc
+import importlib
+import logging
+import sys
+from abc import abstractmethod
+from contextlib import suppress
+from pathlib import Path
+from typing import Any
+
+import torch
+from transformers import (
+    TrainerCallback,
+)
+from transformers.training_args import OptimizerNames
+
+from axolotl.integrations.base import PluginManager
+from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
+from axolotl.utils import is_comet_available, is_mlflow_available
+from axolotl.utils.callbacks import (
+    GCCallback,
+    GPUStatsCallback,
+    SaveAxolotlConfigtoWandBCallback,
+)
+from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
+
+LOG = logging.getLogger(__name__)
+
+with suppress(ImportError):
+    import torch._dynamo  # pylint: disable=ungrouped-imports
+
+
+class TrainerBuilderBase(abc.ABC):
+    """Base class for trainer builder."""
+
+    def __init__(self, cfg, model, tokenizer, processor=None):
+        self.cfg = cfg
+        self.model = model
+        self.tokenizer = tokenizer
+        self.processor = processor
+
+        self._train_dataset = None
+        self._eval_dataset = None
+        self._model_ref = None
+        self._peft_config = None
+
+        # If the model supports tagging, add the axolotl tag.
+        # This makes sure the tag is correctly pushed even if a user calls
+        # model.push_to_hub instead of trainer.push_to_hub.
+        if hasattr(model, "add_model_tags"):
+            model.add_model_tags(["axolotl"])
+
+        patch_trainer_get_lr()
+
+    @property
+    def model_ref(self):
+        return self._model_ref
+
+    @model_ref.setter
+    def model_ref(self, model):
+        self._model_ref = model
+
+    @property
+    def train_dataset(self):
+        return self._train_dataset
+
+    @train_dataset.setter
+    def train_dataset(self, dataset):
+        self._train_dataset = dataset
+
+    @property
+    def eval_dataset(self):
+        return self._eval_dataset
+
+    @eval_dataset.setter
+    def eval_dataset(self, dataset):
+        self._eval_dataset = dataset
+
+    @property
+    def peft_config(self):
+        return self._peft_config
+
+    @peft_config.setter
+    def peft_config(self, peft_config):
+        self._peft_config = peft_config
+
+    @abstractmethod
+    def build(self, total_num_steps):
+        pass
+
+    def get_callbacks(self) -> list[TrainerCallback]:
+        callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
+        )
+
+        if self.cfg.profiler_steps:
+            callbacks.append(
+                PytorchProfilerCallback(
+                    steps_to_profile=self.cfg.profiler_steps,
+                )
+            )
+
+        if self.cfg.gc_steps:
+            callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
+
+        if self.cfg.use_wandb:
+            callbacks.append(
+                SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
+            )
+        if self.cfg.use_mlflow and is_mlflow_available():
+            from axolotl.utils.callbacks.mlflow_ import (
+                SaveAxolotlConfigtoMlflowCallback,
+            )
+
+            callbacks.extend(
+                [
+                    SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path),
+                ]
+            )
+        if self.cfg.use_comet and is_comet_available():
+            from axolotl.utils.callbacks.comet_ import SaveAxolotlConfigtoCometCallback
+
+            callbacks.append(
+                SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
+            )
+
+        callbacks.append(GPUStatsCallback(cfg=self.cfg))
+
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        """
+        Callbacks added after the trainer is created, usually b/c these need access to the trainer
+        """
+        callbacks = []
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            callbacks.extend(
+                [
+                    cb
+                    for cb in plugin_manager.add_callbacks_post_trainer(
+                        self.cfg, trainer
+                    )
+                    if cb
+                ]
+            )
+        return callbacks
+
+    def hook_pre_create_training_args(self, training_arguments_kwargs):
+        # TODO
+        return training_arguments_kwargs
+
+    def hook_post_create_training_args(self, training_arguments):
+        # TODO
+        return training_arguments
+
+    def hook_pre_create_trainer(self, trainer_kwargs, trainer_cls):
+        # TODO
+        return trainer_kwargs, trainer_cls
+
+    def hook_post_create_trainer(self, trainer):
+        # TODO
+        return trainer
+
+    def _configure_warmup_and_logging(
+        self, total_num_steps: int, training_args_kwargs: dict
+    ):
+        warmup_steps = 0
+        warmup_ratio = 0.0
+        if self.cfg.warmup_steps:
+            warmup_steps = self.cfg.warmup_steps
+        elif self.cfg.warmup_ratio:
+            if total_num_steps:
+                warmup_steps = max(int(self.cfg.warmup_ratio * total_num_steps), 0)
+            else:
+                warmup_ratio = self.cfg.warmup_ratio
+        elif total_num_steps:
+            warmup_steps = min(int(0.03 * total_num_steps), 100)
+        else:
+            warmup_ratio = 0.03
+
+        if warmup_steps == 1:
+            warmup_steps = 2
+
+        if self.cfg.logging_steps is not None:
+            training_args_kwargs["logging_steps"] = self.cfg.logging_steps
+        else:
+            training_args_kwargs["logging_steps"] = (
+                500  # transformers defaults to 500
+                if not total_num_steps
+                else max(min(int(0.005 * total_num_steps), 10), 1)
+            )
+
+        training_args_kwargs["warmup_ratio"] = warmup_ratio
+        training_args_kwargs["warmup_steps"] = warmup_steps
+
+    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
+        if self.cfg.bf16 == "full":
+            training_args_kwargs["bf16_full_eval"] = True
+        else:
+            training_args_kwargs["bf16"] = self.cfg.bf16 or self.cfg.bfloat16
+
+    def _configure_scheduler(self, training_args_kwargs: dict):
+        if self.cfg.lr_scheduler in ["one_cycle", "rex"]:
+            training_args_kwargs["lr_scheduler_type"] = "cosine"
+            training_args_kwargs["alternate_lr_scheduler_type"] = self.cfg.lr_scheduler
+        else:
+            training_args_kwargs["lr_scheduler_type"] = (
+                self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
+            )
+        training_args_kwargs["lr_scheduler_kwargs"] = (
+            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
+        )
+
+    def _configure_optimizer(self, training_args_kwargs: dict, trainer_kwargs: dict):
+        def _configure_custom_optimizer(
+            training_args_kwargs: dict, trainer_kwargs: dict
+        ):
+            # Common optimizer kwargs
+            optimizer_kwargs = {
+                "lr": training_args_kwargs["learning_rate"],
+                "weight_decay": training_args_kwargs["weight_decay"],
+            }
+
+            # Adam-specific kwargs
+            adam_kwargs: dict = {}
+            if training_args_kwargs.get("adam_beta1") and training_args_kwargs.get(
+                "adam_beta2"
+            ):
+                adam_kwargs["betas"] = (
+                    training_args_kwargs.get("adam_beta1"),
+                    training_args_kwargs.get("adam_beta2"),
+                )
+            if training_args_kwargs.get("adam_epsilon"):
+                adam_kwargs["eps"] = training_args_kwargs.get("adam_epsilon")
+
+            if self.cfg.optimizer == "muon":
+                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
+                    MuonOptimizerFactory,
+                )
+
+                optimizer_cls = MuonOptimizerFactory
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                optimizer_kwargs["foreach"] = False
+                optimizer_cls = AdamW
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_4bit":
+                # TODO remove 20250401
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                optimizer_cls = AdamW4bit
+                optimizer_kwargs.update(adam_kwargs)
+
+                LOG.warning(
+                    f"`ao_adamw_4bit` will be deprecated soon. Please use `{OptimizerNames.ADAMW_TORCH_4BIT}` instead."
+                )
+            elif self.cfg.optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                optimizer_cls = AdamW8bit
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                optimizer_cls = AdamWFp8
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                optimizer_cls = ADOPT
+                adam_kwargs["decouple"] = True
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "came_pytorch":
+                from came_pytorch import CAME
+
+                optimizer_cls = CAME
+
+                beta1 = training_args_kwargs.get("adam_beta1", 0.9)
+                beta2 = training_args_kwargs.get("adam_beta2", 0.999)
+                beta3 = training_args_kwargs.get("adam_beta3", 0.9999)
+                eps1 = training_args_kwargs.get("adam_epsilon", 1e-30)
+                eps2 = training_args_kwargs.get("adam_epsilon2", 1e-16)
+                adam_kwargs["betas"] = (beta1, beta2, beta3)
+                adam_kwargs["eps"] = (eps1, eps2)
+
+                optimizer_kwargs.update(adam_kwargs)
+            else:
+                raise ValueError(
+                    f"Unhandled optimizer: {self.cfg.optimizer}. Please raise an Issue."
+                )
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optimizer_kwargs.update(self.cfg.optim_args)
+                else:
+                    # Parse string format "key1=value1,key2=value2"
+                    for mapping in self.cfg.optim_args.replace(" ", "").split(","):
+                        key, value = mapping.split("=")
+                        optimizer_kwargs[key] = value
+
+            # Note: This is not used in training_args_kwargs, but in trainer_kwargs
+            trainer_kwargs["optimizer_cls_and_kwargs"] = (
+                optimizer_cls,
+                optimizer_kwargs,
+            )
+
+        # Handle custom optimizer
+        custom_supported_optimizers = [opt.value for opt in CustomSupportedOptimizers]
+        if self.cfg.optimizer in custom_supported_optimizers:
+            _configure_custom_optimizer(training_args_kwargs, trainer_kwargs)
+        else:
+            # Use transformers' optimizer
+            training_args_kwargs["optim"] = self.cfg.optimizer
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optim_args = ",".join(
+                        [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                    )
+                else:
+                    optim_args = self.cfg.optim_args
+                training_args_kwargs["optim_args"] = optim_args
+
+            if (
+                self.cfg.optimizer == "adamw_anyprecision"
+                and Path(self.cfg.torchdistx_path).exists()
+            ):
+                sys.path.append(self.cfg.torchdistx_path)
+                importlib.import_module("torchdistx")
+
+    def _configure_hub_parameters(self, training_args_kwargs: dict):
+        if self.cfg.hub_model_id:
+            training_args_kwargs["hub_model_id"] = self.cfg.hub_model_id
+            training_args_kwargs["push_to_hub"] = True
+            training_args_kwargs["hub_private_repo"] = True
+            training_args_kwargs["hub_always_push"] = True
+
+            if self.cfg.hub_strategy:
+                training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
+
+    def _configure_save_and_eval_strategy(self, training_args_kwargs: dict):
+        # save_strategy and save_steps
+        if self.cfg.save_steps:
+            training_args_kwargs["save_strategy"] = "steps"
+            training_args_kwargs["save_steps"] = self.cfg.save_steps
+        elif self.cfg.save_strategy:
+            training_args_kwargs["save_strategy"] = self.cfg.save_strategy
+        else:
+            # default to saving each epoch if not defined
+            training_args_kwargs["save_strategy"] = "epoch"
+
+        training_args_kwargs["save_total_limit"] = (
+            self.cfg.save_total_limit if self.cfg.save_total_limit else 4
+        )
+
+        # eval_strategy and eval_steps
+        if not self.eval_dataset and self.cfg.val_set_size == 0:
+            # do not eval if no eval_dataset and val_set_size=0
+            training_args_kwargs["eval_strategy"] = "no"
+        elif self.cfg.eval_steps:
+            training_args_kwargs["eval_strategy"] = "steps"
+            training_args_kwargs["eval_steps"] = self.cfg.eval_steps
+            training_args_kwargs["eval_on_start"] = True
+        elif self.cfg.eval_strategy:
+            training_args_kwargs["eval_strategy"] = self.cfg.eval_strategy
+            training_args_kwargs["eval_on_start"] = True
+
+    def _configure_reporting(self, training_args_kwargs: dict):
+        report_to = []
+        if self.cfg.use_wandb:
+            report_to.append("wandb")
+        if self.cfg.use_mlflow:
+            report_to.append("mlflow")
+        if self.cfg.use_tensorboard:
+            report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
+
+        training_args_kwargs["report_to"] = report_to
+
+        if self.cfg.use_wandb:
+            training_args_kwargs["run_name"] = self.cfg.wandb_name
+        elif self.cfg.use_mlflow:
+            training_args_kwargs["run_name"] = self.cfg.mlflow_run_name
+        else:
+            training_args_kwargs["run_name"] = None
+
+    def _configure_torch_compile(self, training_args_kwargs: dict):
+        if self.cfg.torch_compile and getattr(torch, "_dynamo", None):
+            torch._dynamo.config.suppress_errors = (  # pylint: disable=protected-access
+                True
+            )
+            training_args_kwargs["torch_compile"] = self.cfg.torch_compile
+            if self.cfg.torch_compile_backend:
+                training_args_kwargs["torch_compile_backend"] = (
+                    self.cfg.torch_compile_backend
+                )
+            if self.cfg.torch_compile_mode:
+                training_args_kwargs["torch_compile_mode"] = self.cfg.torch_compile_mode
+
+    def _configure_gradient_checkpointing(self, training_args_kwargs: dict):
+        if self.cfg.gradient_checkpointing:
+            training_args_kwargs["gradient_checkpointing"] = (
+                self.cfg.gradient_checkpointing
+            )
+            if self.cfg.gradient_checkpointing_kwargs is not None:
+                training_args_kwargs["gradient_checkpointing_kwargs"] = (
+                    self.cfg.gradient_checkpointing_kwargs
+                )
+            else:
+                training_args_kwargs["gradient_checkpointing_kwargs"] = {
+                    "use_reentrant": False
+                }
+
+    def _set_base_training_args(
+        self, total_num_steps
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        training_args_kwargs: dict[str, Any] = {}
+        trainer_kwargs: dict[str, Any] = {}
+
+        self._configure_warmup_and_logging(total_num_steps, training_args_kwargs)
+        self._configure_precision_settings(training_args_kwargs)
+        self._configure_save_and_eval_strategy(training_args_kwargs)
+        self._configure_gradient_checkpointing(training_args_kwargs)
+
+        # set arg into trainer_args_kwargs with same name if value not None
+        for arg in [
+            # optim/scheduler
+            "adam_beta1",
+            "adam_beta2",
+            "adam_beta3",
+            "adam_epsilon",
+            "adam_epsilon2",
+            "cosine_min_lr_ratio",
+            "cosine_constant_lr_ratio",
+            "optim_target_modules",
+            # trainer
+            "max_grad_norm",
+            "dataloader_num_workers",
+            "dataloader_pin_memory",
+            "dataloader_prefetch_factor",
+            "gradient_accumulation_steps",
+            "learning_rate",
+            "embedding_lr",
+            "embedding_lr_scale",
+            "lr_groups",
+            "loraplus_lr_ratio",
+            "loraplus_lr_embedding",
+            "output_dir",
+            "save_safetensors",
+            "save_only_model",
+            "include_tokens_per_second",
+            "weight_decay",
+            "seed",
+        ]:
+            if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
+                training_args_kwargs[arg] = getattr(self.cfg, arg)
+
+        training_args_kwargs["per_device_train_batch_size"] = self.cfg.micro_batch_size
+
+        if self.cfg.eval_batch_size:
+            training_args_kwargs["per_device_eval_batch_size"] = (
+                self.cfg.eval_batch_size
+            )
+
+        training_args_kwargs["max_steps"] = self.cfg.max_steps or total_num_steps or -1
+        training_args_kwargs["num_train_epochs"] = self.cfg.num_epochs
+
+        if self.cfg.dataset_processes:
+            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+
+        # max_length is not used in CausalTrainer
+        if self.cfg.reward_model or self.cfg.rl:
+            training_args_kwargs["max_length"] = self.cfg.sequence_len
+
+        self._configure_reporting(training_args_kwargs)
+        self._configure_hub_parameters(training_args_kwargs)
+        self._configure_scheduler(training_args_kwargs)
+        self._configure_optimizer(training_args_kwargs, trainer_kwargs)
+        self._configure_torch_compile(training_args_kwargs)
+
+        return training_args_kwargs, trainer_kwargs

src/axolotl/core/builders/causal.py

@@ -0,0 +1,488 @@
+"""Builder for causal trainers"""
+
+import inspect
+import math
+import os
+from pathlib import Path
+from typing import Type, Union
+
+import transformers
+from transformers import (
+    DataCollatorWithFlattening,
+    EarlyStoppingCallback,
+)
+from trl.trainer.utils import RewardDataCollatorWithPadding
+
+from axolotl.core.builders.base import TrainerBuilderBase
+from axolotl.core.trainers import (
+    AxolotlMambaTrainer,
+    AxolotlPRMTrainer,
+    AxolotlRewardTrainer,
+    AxolotlTrainer,
+    ReLoRATrainer,
+)
+from axolotl.integrations.base import PluginManager
+from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
+from axolotl.monkeypatch.relora import ReLoRACallback
+from axolotl.processing_strategies import get_processing_strategy
+from axolotl.utils import is_comet_available, is_mlflow_available
+from axolotl.utils.callbacks import (
+    LossWatchDogCallback,
+    SaveBetterTransformerModelCallback,
+    bench_eval_callback_factory,
+    causal_lm_bench_eval_callback_factory,
+    colab_inference_post_train_callback,
+    log_prediction_callback_factory,
+)
+from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.callbacks.qat import QATCallback
+from axolotl.utils.chat_templates import get_chat_template_from_config
+from axolotl.utils.collators import (
+    BatchSamplerDataCollatorForSeq2Seq,
+    DataCollatorForSeq2Seq,
+    MambaDataCollator,
+    V2BatchSamplerDataCollatorForSeq2Seq,
+)
+from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+class HFCausalTrainerBuilder(TrainerBuilderBase):
+    """
+    Build the HuggingFace training args/trainer for causal models and reward modeling
+    using TRL.
+    """
+
+    def get_callbacks(self):
+        callbacks = super().get_callbacks()
+
+        if self.cfg.relora_steps:
+            callbacks.append(ReLoRACallback(self.cfg))
+
+        if (
+            hasattr(self.model, "use_bettertransformer")
+            and self.model.use_bettertransformer is True
+        ):
+            callbacks.append(SaveBetterTransformerModelCallback())
+
+        # TODO: check if can move to base class
+        if self.cfg.loss_watchdog_threshold is not None:
+            callbacks.append(LossWatchDogCallback(self.cfg))
+
+        if self.cfg.qat:
+            callbacks.append(QATCallback(self.cfg.qat))
+
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        callbacks = []
+        if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "wandb"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
+        if (
+            self.cfg.use_mlflow
+            and is_mlflow_available()
+            and self.cfg.eval_table_size > 0
+        ):
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "mlflow"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
+        if self.cfg.use_comet and is_comet_available() and self.cfg.eval_table_size > 0:
+            LogPredictionCallback = log_prediction_callback_factory(
+                trainer, self.tokenizer, "comet_ml"
+            )
+            callbacks.append(LogPredictionCallback(self.cfg))
+
+        if self.cfg.do_bench_eval:
+            callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
+        if self.cfg.do_causal_lm_eval:
+            CausalLMBenchEvalCallback = causal_lm_bench_eval_callback_factory(
+                trainer, self.tokenizer
+            )
+            callbacks.append(CausalLMBenchEvalCallback(self.cfg))
+
+        if self.cfg.early_stopping_patience:
+            early_stop_cb = EarlyStoppingCallback(
+                self.cfg.early_stopping_patience,
+            )
+            callbacks.append(early_stop_cb)
+
+        if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
+            callbacks.append(lisa_callback_factory(trainer))
+
+        if any("COLAB_" in key for key in os.environ):
+            ColabCallback = colab_inference_post_train_callback(trainer)
+            callbacks.append(ColabCallback(self.cfg))
+
+        callbacks.extend(super().get_post_trainer_create_callbacks(trainer=trainer))
+        return callbacks
+
+    def _get_trainer_cls(self):
+        """
+        Gets the trainer class for the given configuration.
+        """
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
+            if trainer_cls:
+                return trainer_cls
+        if self.cfg.relora_steps:
+            return ReLoRATrainer
+        if self.cfg.model_config_type == "mamba":
+            return AxolotlMambaTrainer
+        if self.cfg.reward_model:
+            return AxolotlRewardTrainer
+        if self.cfg.process_reward_model:
+            return AxolotlPRMTrainer
+        return AxolotlTrainer
+
+    def build(self, total_num_steps):
+        from axolotl.core.training_args import (
+            AxolotlPRMConfig,
+            AxolotlRewardConfig,
+            AxolotlTrainingArguments,
+        )
+
+        training_arguments_kwargs, trainer_kwargs = self._set_base_training_args(
+            total_num_steps
+        )
+
+        if self.cfg.fsdp:
+            training_arguments_kwargs["fsdp"] = self.cfg.fsdp
+            if self.cfg.fsdp_config:
+                training_arguments_kwargs["fsdp_config"] = {
+                    k.lstrip("fsdp_"): v for k, v in dict(self.cfg.fsdp_config).items()
+                }
+
+        if self.cfg.adapter == "qlora":
+            training_arguments_kwargs["qlora"] = True
+
+        # deepspeed
+        if self.cfg.deepspeed:
+            training_arguments_kwargs["deepspeed"] = self.cfg.deepspeed
+
+        if self.cfg.lr_quadratic_warmup is not None:
+            training_arguments_kwargs["lr_quadratic_warmup"] = (
+                self.cfg.lr_quadratic_warmup
+            )
+
+        if self.cfg.dataloader_drop_last is not None:
+            training_arguments_kwargs["dataloader_drop_last"] = (
+                self.cfg.dataloader_drop_last
+            )
+        elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
+            training_arguments_kwargs["dataloader_drop_last"] = True
+
+        if self.cfg.remove_unused_columns is not None:
+            training_arguments_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
+
+        if self.cfg.do_bench_eval:
+            training_arguments_kwargs["do_bench_eval"] = self.cfg.do_bench_eval
+            if self.cfg.bench_dataset:
+                training_arguments_kwargs["bench_dataset"] = self.cfg.bench_dataset
+        if self.cfg.do_causal_lm_eval:
+            training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
+        if self.cfg.metric_for_best_model:
+            training_arguments_kwargs["metric_for_best_model"] = (
+                self.cfg.metric_for_best_model
+            )
+        if self.cfg.greater_is_better:
+            training_arguments_kwargs["greater_is_better"] = self.cfg.greater_is_better
+
+        # DDP Config
+        if self.cfg.ddp_timeout:
+            training_arguments_kwargs["ddp_timeout"] = self.cfg.ddp_timeout
+        # see https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
+        if self.cfg.ddp_bucket_cap_mb:
+            training_arguments_kwargs["ddp_bucket_cap_mb"] = self.cfg.ddp_bucket_cap_mb
+        if self.cfg.ddp_broadcast_buffers is not None:
+            training_arguments_kwargs["ddp_broadcast_buffers"] = (
+                self.cfg.ddp_broadcast_buffers
+            )
+
+        # these are all the "standard" kwargs that are def used
+        training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
+
+        if self.cfg.auto_find_batch_size is not None:
+            training_arguments_kwargs["auto_find_batch_size"] = (
+                self.cfg.auto_find_batch_size
+            )
+
+        training_arguments_kwargs["eval_accumulation_steps"] = (
+            self.cfg.gradient_accumulation_steps
+        )
+
+        training_arguments_kwargs["load_best_model_at_end"] = (
+            (
+                self.cfg.load_best_model_at_end is not False
+                or self.cfg.early_stopping_patience
+            )
+            and (
+                (not self.cfg.test_datasets and self.cfg.val_set_size > 0)
+                or (self.cfg.test_datasets and self.cfg.val_set_size == 0)
+            )
+            and self.cfg.save_steps
+            and self.cfg.eval_steps
+            and self.cfg.save_steps % self.cfg.eval_steps == 0
+        ) or False
+
+        # handle ddp
+        ddp_find_unused_parameters = None
+        if self.cfg.ddp:
+            ddp_find_unused_parameters = bool(self.cfg.ddp_find_unused_parameters)
+        training_arguments_kwargs["ddp_find_unused_parameters"] = (
+            ddp_find_unused_parameters
+        )
+
+        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
+        training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
+
+        training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
+        training_arguments_kwargs["multipack_real_batches"] = (
+            self.cfg.multipack_real_batches
+            if self.cfg.multipack_real_batches is not None
+            else not self.cfg.flash_attention
+        )
+        training_arguments_kwargs["eval_sample_packing"] = bool(
+            self.cfg.eval_sample_packing
+        )
+        if self.cfg.sample_packing_bin_size is not None:
+            training_arguments_kwargs["sample_packing_bin_size"] = (
+                self.cfg.sample_packing_bin_size
+            )
+        if self.cfg.sample_packing_group_size is not None:
+            training_arguments_kwargs["sample_packing_group_size"] = (
+                self.cfg.sample_packing_group_size
+            )
+        if self.cfg.sample_packing_eff_est:
+            training_arguments_kwargs["sample_packing_efficiency"] = (
+                self.cfg.sample_packing_eff_est
+            )
+
+        if self.cfg.relora_steps:
+            training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
+            training_arguments_kwargs["relora_warmup_steps"] = (
+                self.cfg.relora_warmup_steps
+            )
+            if self.cfg.relora_anneal_steps:
+                training_arguments_kwargs["relora_anneal_steps"] = (
+                    self.cfg.relora_anneal_steps
+                )
+            if self.cfg.relora_prune_ratio:
+                training_arguments_kwargs["relora_prune_ratio"] = (
+                    self.cfg.relora_prune_ratio
+                )
+
+        if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
+            training_arguments_kwargs["lisa_n_layers"] = self.cfg.lisa_n_layers
+            training_arguments_kwargs["lisa_step_interval"] = (
+                self.cfg.lisa_step_interval
+            )
+            training_arguments_kwargs["lisa_layers_attribute"] = (
+                self.cfg.lisa_layers_attribute
+            )
+
+        training_arguments_kwargs = self.hook_pre_create_training_args(
+            training_arguments_kwargs
+        )
+        training_arguments_kwargs["model_type"] = self.cfg.model_config_type
+        training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+        if self.cfg.chat_template:
+            training_arguments_kwargs["chat_template"] = get_chat_template_from_config(
+                cfg=self.cfg,
+                tokenizer=self.tokenizer,
+            )
+
+        if self.cfg.neftune_noise_alpha is not None:
+            training_arguments_kwargs["neftune_noise_alpha"] = (
+                self.cfg.neftune_noise_alpha
+            )
+
+        if self.cfg.accelerator_config:
+            training_arguments_kwargs["accelerator_config"] = (
+                self.cfg.accelerator_config
+            )
+
+        if self.cfg.image_size:
+            training_arguments_kwargs["image_size"] = self.cfg.image_size
+        if self.cfg.image_resize_algorithm:
+            training_arguments_kwargs["image_resize_algorithm"] = (
+                self.cfg.image_resize_algorithm
+            )
+
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            plugin_training_args = plugin_manager.get_training_args(self.cfg)
+            if plugin_training_args:
+                training_arguments_kwargs.update(plugin_training_args)
+
+        if self.cfg.reward_model:
+            training_args_cls = AxolotlRewardConfig
+        elif self.cfg.process_reward_model:
+            training_args_cls = AxolotlPRMConfig
+        else:
+            training_args_cls = AxolotlTrainingArguments
+        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+            **training_arguments_kwargs,
+        )
+        training_args = self.hook_post_create_training_args(training_args)
+
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
+        data_collator_kwargs = {
+            "padding": True,  # True/"longest" is the default
+        }
+        multiple = 64
+        if self.cfg.pad_to_sequence_len:
+            data_collator_kwargs["pad_to_multiple_of"] = multiple * math.ceil(
+                self.cfg.sequence_len / multiple
+            )
+        else:
+            # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
+            # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
+            data_collator_kwargs["pad_to_multiple_of"] = multiple
+
+        trainer_cls = self._get_trainer_cls()
+
+        trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
+            trainer_kwargs, trainer_cls
+        )
+        if eval_data_collator := self.build_collator(
+            training_args, is_eval=True, **data_collator_kwargs
+        ):
+            if not (self.cfg.reward_model or self.cfg.process_reward_model):
+                trainer_kwargs["eval_data_collator"] = eval_data_collator
+        if not (self.cfg.reward_model or self.cfg.process_reward_model):
+            trainer_kwargs["bench_data_collator"] = transformers.DataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            )
+        sig = inspect.signature(trainer_cls)
+        if "processing_class" in sig.parameters:
+            trainer_kwargs["processing_class"] = self.tokenizer
+        elif "tokenizer" in sig.parameters:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+        if (
+            trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
+            and self.cfg.datasets is not None
+        ):
+            trainer_kwargs["dataset_tags"] = [
+                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
+            ]
+        trainer = trainer_cls(
+            model=self.model,
+            train_dataset=self.train_dataset,
+            eval_dataset=self.eval_dataset,
+            args=training_args,
+            data_collator=self.build_collator(training_args, **data_collator_kwargs),
+            callbacks=self.get_callbacks(),
+            **trainer_kwargs,
+        )
+        trainer = self.hook_post_create_trainer(trainer)
+        for callback in self.get_post_trainer_create_callbacks(trainer):
+            trainer.add_callback(callback)
+
+        if self.cfg.deepspeed and self.cfg.sample_packing:
+            trainer.accelerator.state.deepspeed_plugin.deepspeed_config[
+                "train_micro_batch_size_per_gpu"
+            ] = self.cfg.micro_batch_size
+
+        return trainer
+
+    def build_collator(
+        self,
+        training_args,  # type: "AxolotlTrainingArguments"  # type: ignore
+        is_eval=False,
+        **kwargs,
+    ):
+        if training_args.pretraining:
+            if (
+                self.cfg.pretraining_sample_concatenation is False
+                or self.cfg.micro_batch_size > 1
+            ):
+                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
+            return None
+
+        if self.cfg.model_config_type == "mamba":
+            return MambaDataCollator(tokenizer=self.tokenizer)
+
+        use_batch_sampler_collator = False
+        if is_eval is False and training_args.sample_packing:
+            use_batch_sampler_collator = True
+        if is_eval and training_args.eval_sample_packing:
+            use_batch_sampler_collator = True
+
+        collator: Type[
+            Union[
+                V2BatchSamplerDataCollatorForSeq2Seq,
+                BatchSamplerDataCollatorForSeq2Seq,
+                DataCollatorForSeq2Seq,
+                DataCollatorWithFlattening,
+                RewardDataCollatorWithPadding,
+            ]
+        ]
+        collator_args = [self.tokenizer]
+
+        collator_cls_and_kwargs = None
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            collator_cls_and_kwargs = plugin_manager.get_collator_cls_and_kwargs(
+                self.cfg, is_eval=is_eval
+            )
+
+        if collator_cls_and_kwargs:
+            collator = collator_cls_and_kwargs[0]
+            if kwargs and isinstance(kwargs, dict):
+                kwargs.update(collator_cls_and_kwargs[1])
+        elif self.cfg.reward_model:
+            collator = RewardDataCollatorWithPadding
+        elif use_batch_sampler_collator:
+            # Use V2BatchSamplerDataCollatorForSeq2Seq for flex attention,
+            # supported multipack models, or non-flash-attention llama
+            if (
+                self.cfg.flex_attention
+                or self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES
+                or (
+                    self.cfg.model_config_type in ["llama"]
+                    and self.cfg.flash_attention is not True
+                )
+            ):
+                collator = V2BatchSamplerDataCollatorForSeq2Seq
+            else:
+                collator = BatchSamplerDataCollatorForSeq2Seq
+        else:
+            if self.cfg.processor_type and self.processor:
+                collator = MultiModalChatDataCollator
+                kwargs["processing_strategy"] = get_processing_strategy(
+                    self.processor,
+                    training_args.chat_template,
+                    self.cfg.chat_template,
+                    image_size=training_args.image_size,
+                    image_resize_algorithm=training_args.image_resize_algorithm,
+                )
+            elif self.cfg.batch_flattening:
+                collator = DataCollatorWithFlattening
+                collator_args.pop(0)
+                kwargs.pop("pad_to_multiple_of", None)
+                kwargs.pop("padding", None)
+            else:
+                collator = DataCollatorForSeq2Seq
+
+        kwargs["return_tensors"] = "pt"
+
+        return collator(
+            *collator_args,
+            **kwargs,
+        )

src/axolotl/core/builders/rl.py

@@ -0,0 +1,238 @@
+"""Builder for RLHF trainers"""
+
+import inspect
+from pathlib import Path
+
+from axolotl.core.builders.base import TrainerBuilderBase
+from axolotl.core.trainers import (
+    AxolotlCPOTrainer,
+    AxolotlKTOTrainer,
+    AxolotlORPOTrainer,
+)
+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
+from axolotl.utils.logging import get_logger
+from axolotl.utils.schemas.enums import RLType
+
+LOG = get_logger(__name__)
+
+
+class HFRLTrainerBuilder(TrainerBuilderBase):
+    """Trainer factory class for TRL-based RLHF trainers (e.g. DPO)"""
+
+    def get_callbacks(self):
+        callbacks = super().get_callbacks()
+
+        if self.cfg.qat:
+            callbacks.append(QATCallback(self.cfg.qat))
+
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        return callbacks
+
+    def _get_trainer_cls(self, trainer_kwargs: dict):
+        """
+        Returns trainer_cls and trainer_cls_args
+        """
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
+            trainer_cls_args = []  # type: ignore
+
+            if trainer_cls is not None:
+                return trainer_cls, trainer_cls_args
+
+        trainer_cls = None
+        trainer_cls_args = [self.model]
+
+        if self.cfg.rl is RLType.GRPO:
+            trainer_cls = GRPOStrategy.get_trainer_class(
+                sequence_parallel=self.cfg.sequence_parallel_degree > 1
+            )
+            trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
+
+            trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
+
+        elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
+            trainer_cls = DPOStrategy.get_trainer_class()
+            trainer_cls_args.append(self.model_ref)
+
+        elif self.cfg.rl is RLType.ORPO:
+            trainer_cls = AxolotlORPOTrainer
+        elif self.cfg.rl is RLType.KTO:
+            trainer_cls = AxolotlKTOTrainer
+        elif self.cfg.rl is RLType.SIMPO:
+            trainer_cls = AxolotlCPOTrainer
+        else:
+            raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        return trainer_cls, trainer_cls_args
+
+    def _build_training_arguments(self, total_num_steps):
+        """
+        Returns training_args and trainer_kwargs
+        """
+        from axolotl.core.training_args import (
+            AxolotlCPOConfig,
+            AxolotlKTOConfig,
+            AxolotlORPOConfig,
+        )
+
+        training_args_kwargs, trainer_kwargs = self._set_base_training_args(
+            total_num_steps=total_num_steps
+        )
+
+        if self.cfg.remove_unused_columns is not None:
+            training_args_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
+        else:
+            training_args_kwargs["remove_unused_columns"] = False
+
+        if self.cfg.trl and self.cfg.trl.beta is not None:
+            training_args_kwargs["beta"] = self.cfg.trl.beta
+        elif self.cfg.rl_beta is not None:
+            training_args_kwargs["beta"] = self.cfg.rl_beta
+        elif self.cfg.orpo_alpha is not None:
+            # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
+            training_args_kwargs["beta"] = self.cfg.orpo_alpha
+
+        if self.cfg.rpo_alpha is not None:
+            training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
+
+        if self.cfg.use_wandb:
+            training_args_kwargs["run_name"] = self.cfg.wandb_name
+
+        training_args_cls = None
+        blocklist_args_kwargs = []
+        if self.cfg.rl is RLType.SIMPO:
+            training_args_cls = AxolotlCPOConfig
+            training_args_kwargs["loss_type"] = "simpo"
+            training_args_kwargs["simpo_gamma"] = self.cfg.simpo_gamma
+            if self.cfg.cpo_alpha is not None:
+                training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
+
+        elif self.cfg.rl is RLType.ORPO:
+            training_args_cls = AxolotlORPOConfig
+            if self.cfg.max_prompt_len:
+                training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
+
+        elif self.cfg.rl is RLType.KTO:
+            training_args_cls = AxolotlKTOConfig
+
+            training_args_kwargs["desirable_weight"] = (
+                self.cfg.kto_desirable_weight or 1.0
+            )
+            training_args_kwargs["undesirable_weight"] = (
+                self.cfg.kto_undesirable_weight or 1.0
+            )
+
+            if self.cfg.max_prompt_len:
+                training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
+
+        elif self.cfg.rl is RLType.GRPO:
+            training_args_cls = GRPOStrategy.get_training_args_class()
+            training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
+            blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
+
+        elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
+            training_args_cls = AxolotlDPOConfig
+            training_args_kwargs.update(DPOStrategy.set_training_args_kwargs(self.cfg))
+        else:
+            raise ValueError(f"Unsupported RL: {self.cfg.rl}")
+
+        for blocklist_key in blocklist_args_kwargs:
+            if blocklist_key in training_args_kwargs:
+                del training_args_kwargs[blocklist_key]
+
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            plugin_training_args = plugin_manager.get_training_args(self.cfg)
+            if plugin_training_args:
+                training_args_kwargs.update(plugin_training_args)
+
+        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+            logging_first_step=True,
+            **training_args_kwargs,
+        )
+
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
+        return training_args, trainer_kwargs
+
+    def build(self, total_num_steps):
+        training_args, trainer_kwargs = self._build_training_arguments(total_num_steps)
+
+        if self.eval_dataset:
+            trainer_kwargs["eval_dataset"] = self.eval_dataset
+        if self.cfg.adapter and self.peft_config and self.cfg.rl is not RLType.GRPO:
+            trainer_kwargs["peft_config"] = self.peft_config
+        if self.cfg.precompute_ref_log_probs is not None:
+            trainer_kwargs["precompute_ref_log_probs"] = (
+                self.cfg.precompute_ref_log_probs
+            )
+
+        trainer_cls, trainer_cls_args = self._get_trainer_cls(trainer_kwargs)
+
+        sig = inspect.signature(trainer_cls)
+        if "tokenizer" in sig.parameters:
+            trainer_kwargs["tokenizer"] = self.tokenizer
+        else:
+            trainer_kwargs["processing_class"] = self.tokenizer
+
+        if self.cfg.datasets is not None and (
+            trainer_cls is DPOStrategy.get_trainer_class()
+        ):
+            trainer_kwargs["dataset_tags"] = [
+                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
+            ]
+
+        trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
+            trainer_kwargs, trainer_cls
+        )
+
+        trainer = trainer_cls(
+            *trainer_cls_args,
+            args=training_args,
+            train_dataset=self.train_dataset,
+            callbacks=self.get_callbacks(),
+            **trainer_kwargs,
+        )
+        if 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:
+                ensure_dtype(trainer.ref_model, dtype=self.cfg.torch_dtype)
+
+        trainer = self.hook_post_create_trainer(trainer)
+        for callback in self.get_post_trainer_create_callbacks(trainer):
+            trainer.add_callback(callback)
+
+        return trainer
+
+
+class HFPPOTrainerBuilder(TrainerBuilderBase):
+    """
+    HF Factory class for PPO Trainer
+    """
+
+    def get_callbacks(self):
+        callbacks = super().get_callbacks()
+        return callbacks
+
+    def get_post_trainer_create_callbacks(self, trainer):
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
+        return callbacks
+
+    def build(self, total_num_steps):
+        # TODO: build PPOConfig
+        raise NotImplementedError("PPO trainer builder is not implemented yet.")

src/axolotl/core/chat/messages.py

@@ -156,7 +156,6 @@ class Messages(BaseModel):
                         len(input_ids) : len(input_ids) + len(pending_input_ids)
                     ]
                     if new_pending_inputs != pending_input_ids:
-                        # logging.warning("tokenization mismatch from concatenation.")
                         pending_input_ids = new_pending_inputs
                     input_ids.extend(pending_input_ids)
                     if pending_weight:

src/axolotl/core/trainers/__init__.py

@@ -5,7 +5,7 @@
 
 from .base import AxolotlTrainer
 from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOTrainer
+from .grpo.trainer import AxolotlGRPOSequenceParallelTrainer, AxolotlGRPOTrainer
 from .mamba import AxolotlMambaTrainer
 from .relora import ReLoRATrainer
 from .trl import (

src/axolotl/core/trainers/base.py

@@ -4,11 +4,10 @@
 
 from __future__ import annotations
 
-import logging
 import os
 from collections import defaultdict
-from functools import wraps
-from typing import Literal
+from functools import partial, wraps
+from typing import Callable, Literal, Optional
 
 import datasets
 import torch
@@ -26,22 +25,24 @@ from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
 from axolotl.core.trainers.mixins import (
+    CheckpointSaveMixin,
     OptimizerMixin,
     RngLoaderMixin,
     SchedulerMixin,
-    SequenceParallelMixin,
 )
 from axolotl.core.trainers.utils import (
     sanitize_kwargs_for_ds_tagging,
     sanitize_kwargs_for_tagging,
 )
+from axolotl.utils import get_not_null
+from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
-LOG = logging.getLogger(__name__)
+LOG = get_logger(__name__)
 
 
 class AxolotlTrainer(
-    SchedulerMixin, OptimizerMixin, RngLoaderMixin, SequenceParallelMixin, Trainer
+    SchedulerMixin, OptimizerMixin, RngLoaderMixin, CheckpointSaveMixin, Trainer
 ):
     """Extend the base Trainer for axolotl helpers"""
 
@@ -68,10 +69,6 @@ class AxolotlTrainer(
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
-        # Initialize sequence parallelism if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._setup_sequence_parallel()
-
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.torch_compile:
             torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
@@ -108,20 +105,28 @@ class AxolotlTrainer(
             )
             batch_max_len = train_batch_size * self.args.max_seq_length
 
-        return MultipackBatchSampler(
+        sampler = MultipackBatchSampler(
             base_sampler,
             lengths=get_dataset_lengths(dataset),
             packing_efficiency_estimate=self.args.sample_packing_efficiency,
             batch_max_len=batch_max_len,
             batch_size=batch_size,
+            group_size=self.args.sample_packing_group_size,
+            bin_size=self.args.sample_packing_bin_size,
             sequential=self.args.sample_packing_sequentially,
             drop_last=True,
+            num_processes=self.args.dataset_num_proc,
         )
 
-    def _get_train_sampler(self) -> Sampler | None:
+        len(sampler)
+        return sampler
+
+    def _get_train_sampler(
+        self, train_dataset: Optional[Dataset] = None
+    ) -> Optional[Sampler]:
         """
-        Helper method to get the sampler for training. Handles cases for sequence
-        parallelism, sample packing, and curriculum sampling (sequential).
+        Helper method to get the sampler for training. Handles cases for sample packing
+        and curriculum sampling (sequential).
 
         Returns:
             If the dataset is non-empty, a sampler is returned, the type of which
@@ -130,9 +135,7 @@ class AxolotlTrainer(
         use_sample_packing = self.args.sample_packing and not self.args.pretraining
 
         # Determine the base sampler first
-        if self.args.sequence_parallel_degree > 1:
-            base_sampler = self._sp_get_train_sampler(self.train_dataset)
-        elif self.args.curriculum_sampling:
+        if self.args.curriculum_sampling:
             base_sampler = SequentialSampler(self.train_dataset)
         elif use_sample_packing:
             base_sampler = RandomSampler(self.train_dataset)
@@ -144,31 +147,26 @@ class AxolotlTrainer(
         if use_sample_packing:
             return self._create_multipack_sampler(
                 base_sampler=base_sampler,
-                dataset=self.train_dataset,
+                dataset=train_dataset,
             )
 
         return base_sampler
 
     def _get_eval_sampler(self, eval_dataset: Dataset | None = None) -> Sampler | None:
         """
-        Helper method to get the sampler for evaluation. Handles sequence parallelism
-        and sample packing cases.
+        Helper method to get the sampler for evaluation. Handles sample packing case.
 
         Returns:
             If the dataset is non-empty, a sampler is returned, the type of which
                 depends on the passed training args.
         """
-        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
-
         # Multipacking enabled if training is enabled and eval is not explicitly disabled
         use_multipack = (
             self.args.sample_packing and self.args.eval_sample_packing is not False
         )
 
         # Determine the base sampler
-        if self.args.sequence_parallel_degree > 1:
-            base_sampler = self._sp_get_eval_sampler(eval_dataset)
-        elif use_multipack:
+        if use_multipack:
             base_sampler = SequentialSampler(eval_dataset)
         else:
             return super()._get_eval_sampler(eval_dataset)
@@ -182,149 +180,93 @@ class AxolotlTrainer(
 
         return base_sampler
 
-    def _create_dataloader_params(self, is_eval=False, custom_batch_size=None):
-        """Create common dataloader parameters for train or eval."""
-        batch_size = custom_batch_size or (
-            self.args.eval_batch_size if is_eval else self._train_batch_size
-        )
+    def _get_dataloader(
+        self,
+        dataset: Dataset,
+        description: str,
+        batch_size: int,
+        sampler_fn: Optional[Callable[[Dataset], torch.utils.data.Sampler]] = None,
+        is_training: bool = False,
+        dataloader_key: Optional[str] = None,
+    ) -> DataLoader:
+        """Create a [`~torch.utils.data.DataLoader`] from the given dataset."""
 
-        params = {
+        data_collator = self.data_collator if is_training else self.eval_data_collator
+
+        if dataset.column_names and "length" in dataset.column_names:
+            dataset = dataset.remove_columns(["length"])
+
+        if isinstance(dataset, datasets.Dataset):
+            if is_training:
+                if not self.args.sample_packing or self.args.pretraining:
+                    dataset = self._remove_unused_columns(
+                        dataset, description="training"
+                    )
+            elif (
+                not is_training
+                and self.args.sample_packing
+                and self.args.eval_sample_packing is not False
+            ):
+                batch_size = (
+                    batch_size
+                    if self.args.sample_packing
+                    else self.args.per_device_eval_batch_size
+                )
+            else:
+                dataset = self._remove_unused_columns(dataset, description=description)
+        else:
+            data_collator = self._get_collator_with_removed_columns(
+                self.data_collator, description=description
+            )
+
+        dataloader_params = {
             "batch_size": batch_size,
-            "collate_fn": self.data_collator,
+            "collate_fn": data_collator,
             "num_workers": self.args.dataloader_num_workers,
             "pin_memory": self.args.dataloader_pin_memory,
+            "persistent_workers": self.args.dataloader_persistent_workers,
         }
 
-        # Add persistent workers only for training
-        if not is_eval and hasattr(self.args, "dataloader_persistent_workers"):
-            params["persistent_workers"] = self.args.dataloader_persistent_workers
-
-        # Add prefetch factor if specified
-        if self.args.dataloader_prefetch_factor:
-            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return params
-
-    def _prepare_dataloader(
-        self, dataset, sampler, is_eval=False, custom_batch_size=None
-    ):
-        """Prepare a dataloader with the given dataset and sampler."""
-        # Get base parameters
-        dataloader_params = self._create_dataloader_params(is_eval, custom_batch_size)
-
-        # Add sampler configuration
         if not isinstance(dataset, torch.utils.data.IterableDataset):
-            if isinstance(sampler, BatchSampler):
-                # batch_size and batch_sampler are mutually exclusive
-                dataloader_params["batch_sampler"] = sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-            if not is_eval:
-                dataloader_params["worker_init_fn"] = seed_worker
-
-        # Create the dataloader
-        dataloader = DataLoader(dataset, **dataloader_params)
+            dataloader_params["drop_last"] = get_not_null(
+                self.args.dataloader_drop_last, True
+            )
+            if sampler_fn is not None:
+                sampler = sampler_fn(dataset)
+                if isinstance(sampler, BatchSampler):
+                    # batch_size and batch_sampler are mutually exclusive
+                    dataloader_params["batch_sampler"] = sampler
+                    del dataloader_params["batch_size"]
+                    del dataloader_params["drop_last"]
+                else:
+                    dataloader_params["sampler"] = sampler
 
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+            if is_training:
+                dataloader_params["worker_init_fn"] = partial(
+                    seed_worker,
+                    num_workers=self.args.dataloader_num_workers,
+                    rank=self.args.process_index,
+                )
         if self.args.sample_packing and (
-            (not is_eval and not self.args.pretraining)
-            or (is_eval and self.args.eval_sample_packing is not False)
+            (is_training and not self.args.pretraining)
+            or (not is_training and self.args.eval_sample_packing is not False)
         ):
             self.accelerator.even_batches = False
 
-        # Return unprepared dataloader if using sequence parallelism
-        # TODO(djsaunde): We might be able to use `accelerate`'s dataloader preparation
-        # if we use `dispatch_batches` and `slice_fn_for_dispatch` properly (i.e.,
-        # slice each batch along the sequence dimension).
-        if self.args.sequence_parallel_degree > 1:
-            return dataloader
+        dataloader = DataLoader(dataset, **dataloader_params)
 
-        # Otherwise prepare with accelerator
-        return self.accelerator.prepare_data_loader(dataloader)
+        # Accelerator.free_memory() will destroy the references, so
+        # we need to store the non-prepared version for eval dataloaders.
+        # fmt: off
+        if dataloader_key is not None and self.args.dataloader_persistent_workers:
+            if hasattr(self, "_eval_dataloaders"):
+                self._eval_dataloaders[dataloader_key] = dataloader  # type: ignore  # pylint: disable=access-member-before-definition
+            else:
+                self._eval_dataloaders = {dataloader_key: dataloader}  # pylint: disable=attribute-defined-outside-init
+        # fmt: on
 
-    def get_train_dataloader(self) -> DataLoader:
-        """Get dataloader for training"""
-        train_dataset = self.train_dataset
-        data_collator = self.data_collator  # type: ignore
-
-        # Handle dataset preprocessing
-        if isinstance(train_dataset, datasets.Dataset):
-            if self.args.sample_packing and not self.args.pretraining:
-                train_dataset = train_dataset.remove_columns(["length"])
-            if not self.args.sample_packing or self.args.pretraining:
-                train_dataset = self._remove_unused_columns(
-                    train_dataset, description="training"
-                )
-        else:
-            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
-                data_collator,
-                description="training",
-            )
-
-        # Get sampler and create dataloader
-        sampler = self._get_train_sampler()
-        return self._prepare_dataloader(train_dataset, sampler, is_eval=False)
-
-    def get_eval_dataloader(self, eval_dataset: Dataset | None = None) -> DataLoader:
-        """Get dataloader for evaluation"""
-        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
-
-        # Handle special case: sample packing is enabled but eval_sample_packing is False
-        if self.args.sample_packing and self.args.eval_sample_packing is False:
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.eval_data_collator
-            )
-            if "length" in eval_dataset.column_names:
-                eval_dataset = eval_dataset.remove_columns(["length"])
-            dataloader = super().get_eval_dataloader(eval_dataset)
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.train_data_collator
-            )
-
-            return dataloader
-
-        # Handle sample packing or sequence parallelism
-        if (
-            self.args.sample_packing
-            and self.args.eval_sample_packing is not False
-            or self.args.sequence_parallel_degree > 1
-        ):
-            # Get appropriate data collator
-            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
-                self.eval_data_collator
-                if hasattr(self, "eval_data_collator") and self.eval_data_collator
-                else self.data_collator
-            )
-            if "length" in eval_dataset.column_names:
-                eval_dataset = eval_dataset.remove_columns(["length"])
-
-            # Handle dataset preprocessing for SP
-            if self.args.sequence_parallel_degree > 1:
-                if isinstance(eval_dataset, datasets.Dataset):
-                    eval_dataset = self._remove_unused_columns(
-                        eval_dataset, description="evaluation"
-                    )
-                else:
-                    self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
-                        self.data_collator, description="evaluation"
-                    )
-
-            # Use eval_batch_size for sample packing, per_device_eval_batch_size otherwise
-            batch_size = (
-                self.args.eval_batch_size
-                if self.args.sample_packing
-                else self.args.per_device_eval_batch_size
-            )
-            sampler = self._get_eval_sampler(eval_dataset)
-            dataloader = self._prepare_dataloader(
-                eval_dataset, sampler, is_eval=True, custom_batch_size=batch_size
-            )
-
-            return dataloader
-
-        return super().get_eval_dataloader(eval_dataset)
+        return self.accelerator.prepare(dataloader)
 
     def _get_bench_sampler(
         self, bench_dataset: Dataset
@@ -371,15 +313,13 @@ class AxolotlTrainer(
                 num_items_in_batch=num_items_in_batch,
             )
 
-        loss = super().compute_loss(
+        return super().compute_loss(
             model,
             inputs,
             return_outputs=return_outputs,
             num_items_in_batch=num_items_in_batch,
         )
 
-        return loss
-
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}

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

@@ -1,14 +1,11 @@
-"""
-DPO Specific Strategy for training
-"""
+"""DPO Specific Strategy for training"""
 
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
+from axolotl.utils.schemas.enums import RLType
 
 
 class DPOStrategy:
-    """
-    Strategy for DPO training
-    """
+    """Strategy for DPO training"""
 
     @classmethod
     def get_trainer_class(cls):
@@ -23,12 +20,21 @@ class DPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         training_args_kwargs = {}
-        if cfg.rl == "ipo":
+        if cfg.rl is RLType.IPO:
             training_args_kwargs["loss_type"] = "ipo"
-        training_args_kwargs["max_length"] = cfg.sequence_len
+        # 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["max_prompt_length"] = cfg.sequence_len
         training_args_kwargs["generate_during_eval"] = cfg.use_wandb
         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
         return training_args_kwargs

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

@@ -14,3 +14,5 @@ class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
     """
     DPO config for DPO training
     """
+
+    dpo_norm_loss: bool | None = False