update tags

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

.coveragerc

@@ -2,7 +2,6 @@
 source = axolotl
 omit =
     */tests/*
-    setup.py
 
 [report]
 exclude_lines =

.github/CONTRIBUTING.md

@@ -29,13 +29,18 @@ PRs are **greatly welcome**!
 2. Set up the development environment by following the instructions in the [README.md](https://github.com/axolotl-ai-cloud/axolotl/tree/main/README.md) file.
 3. Explore the codebase, run tests, and verify that everything works as expected.
 
-Please run below to setup env
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
+Please run the below to setup:
 
-# test
-pytest tests/
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+uv sync --dev && uv pip install flash-attn --no-build-isolation
+source .venv/bin/activate
+
+pre-commit install  # install pre-commit hooks
+
+pytest tests/  # optional; run test suite
 ```
 
 ## How to Contribute

.github/FUNDING.yml

@@ -1,13 +1,13 @@
 # These are supported funding model platforms
 
-github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+github: [winglian, OpenAccess-AI-Collective] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
-ko_fi: # Replace with a single Ko-fi username
+ko_fi: axolotl_ai # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
-custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
+custom: ['https://quickchart.io/qr?text=bitcoin%3Abc1qxlgwlqwfea5s2cxm42xqsfmwjct0rj8w8ea5np&size=480&centerImageUrl=https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2Fthumb%2F4%2F46%2FBitcoin.svg%2F64px-Bitcoin.svg.png'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.github/workflows/base.yml

@@ -25,11 +25,18 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - 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: 2.7.0
+            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"
@@ -53,20 +60,6 @@ jobs:
             pytorch: 2.8.0
             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: 2.9.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-base"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -90,6 +83,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl-base
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
@@ -104,7 +98,9 @@ jobs:
           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 }}
+          tags: |
+            ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            ${{ steps.metadata.outputs.tags }}-base-uv-py${{ 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 }}
@@ -121,6 +117,13 @@ jobs:
       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: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
@@ -142,20 +145,6 @@ jobs:
             pytorch: 2.8.0
             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: 2.9.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/docs.yml

@@ -20,10 +20,14 @@ jobs:
           uses: actions/setup-python@v5
           with:
             python-version: '3.11'
+        - name: Install uv
+          uses: astral-sh/setup-uv@v4
+          with:
+            version: "latest"
         - name: Install dependencies
           run: |
-            python3 -m pip install jupyter quartodoc
-            python3 -m pip install -e .
+            uv pip install --system jupyter quartodoc
+            uv pip install --system -e .
         - name: Build autodoc
           run: quartodoc build
         - name: Publish to GitHub Pages (and render)

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
       types: [opened, synchronize, reopened, ready_for_review]
       paths:
        - '**.py'
-       - 'requirements.txt'
+       - 'pyproject.toml'
        - '.github/workflows/*.yml'
        - "*.[q]md"
        - "examples/**/*.y[a]?ml"
@@ -23,5 +23,4 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-          cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1

.github/workflows/main.yml

@@ -18,13 +18,14 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.6.0
             axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras: vllm
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -35,17 +36,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            is_latest: true
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -55,6 +45,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl
             axolotlai/axolotl
           tags: |
             type=ref,event=branch
@@ -77,6 +68,8 @@ jobs:
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
             AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}
+            GIT_REF=${{ github.ref }}
+            GIT_SHA=${{ github.sha }}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
@@ -95,7 +88,7 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.0
+            pytorch: 2.6.0
             axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
@@ -108,6 +101,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras: vllm
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -118,17 +112,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            is_latest: true
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -138,6 +121,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl-cloud
             axolotlai/axolotl-cloud
           tags: |
             type=ref,event=branch
@@ -156,6 +140,8 @@ jobs:
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
+            GIT_REF=${{ github.ref }}
+            GIT_SHA=${{ github.sha }}
           file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
@@ -170,6 +156,11 @@ jobs:
     strategy:
       matrix:
         include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -197,6 +188,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl-cloud-term
             axolotlai/axolotl-cloud-term
           tags: |
             type=ref,event=branch
@@ -215,6 +207,8 @@ jobs:
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
+            GIT_REF=${{ github.ref }}
+            GIT_SHA=${{ github.sha }}
           file: ./docker/Dockerfile-cloud-no-tmux
           push: ${{ github.event_name != 'pull_request' }}
           tags: |

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

@@ -4,8 +4,6 @@ on:
   pull_request:
     paths:
       - 'tests/e2e/multigpu/**.py'
-      - 'requirements.txt'
-      - 'setup.py'
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
       - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
@@ -26,6 +24,13 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras:
+            num_gpus: 2
+            nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -40,13 +45,6 @@ jobs:
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
             nightly_build: "true"
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras: fbgemm-gpu
-            num_gpus: 2
-            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -56,13 +54,17 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          pip install modal==1.0.2 jinja2 protobuf
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=${{ github.ref_name }}-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}}" >> $GITHUB_ENV
@@ -72,4 +74,4 @@ jobs:
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.multigpu
+          modal run -m cicd.multigpu

.github/workflows/nightlies.yml

@@ -15,12 +15,12 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.6.0
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -31,6 +31,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl
             axolotlai/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
@@ -51,6 +52,8 @@ jobs:
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
+            GIT_REF=${{ github.ref }}
+            GIT_SHA=${{ github.sha }}
           file: ./docker/Dockerfile
           push: ${{ github.event_name != 'pull_request' }}
           tags: |
@@ -67,12 +70,12 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.6.0
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -83,6 +86,7 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
+            winglian/axolotl-cloud
             axolotlai/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
@@ -100,6 +104,8 @@ jobs:
           build-args: |
             BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
+            GIT_REF=${{ github.ref }}
+            GIT_SHA=${{ github.sha }}
           file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
           tags: |

.github/workflows/precommit-autoupdate.yml

@@ -2,7 +2,7 @@ name: Pre-commit auto-update
 
 on:
   schedule:
-    - cron: '0 0 1 * *'  # Run monthly
+    - cron: '0 0 * * 0'  # Run weekly
   workflow_dispatch:  # Manual kickoff
 
 jobs:
@@ -18,10 +18,15 @@ jobs:
         with:
           python-version: '3.11'
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
       - name: Update pre-commit hooks
         id: update
         run: |
-          pip install pre-commit
+          uv pip install --system pre-commit
           pre-commit autoupdate
           if [[ -n $(git status --porcelain) ]]; then
             echo "changes=true" >> $GITHUB_OUTPUT

.github/workflows/preview-docs.yml

@@ -40,10 +40,15 @@ jobs:
         with:
           python-version: '3.11'
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
       - name: Install dependencies
         run: |
-          python3 -m pip install jupyter quartodoc
-          python3 -m pip install -e .
+          uv pip install --system jupyter quartodoc
+          uv pip install --system -e .
 
       - name: Build autodoc
         run: quartodoc build

.github/workflows/pypi.yml

@@ -38,23 +38,24 @@ jobs:
         with:
           python-version: "3.11"
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging==23.2
-          pip3 install --no-build-isolation -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          uv pip install --system wheel packaging==23.2
+          uv pip install --system --no-build-isolation -e ".[dev]"
 
       - name: Extract tag name
         id: tag
-        run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
+        run: echo "TAG_NAME=$(echo "$GITHUB_REF" | cut -d / -f 3)" >> "$GITHUB_OUTPUT"
 
-      - name: Update version in setup.py
+      - name: Build package
         run: |
-          sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
-
-      - name: Build a source dist
-        run: |
-          python setup.py sdist
+          uv pip install --system build
+          python -m build
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests-nightly.yml

@@ -13,7 +13,6 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-          cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
@@ -26,7 +25,7 @@ jobs:
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.7.1", "2.8.0"]
+        pytorch_version: ["2.6.0", "2.7.0"]
     timeout-minutes: 20
 
     steps:
@@ -43,32 +42,30 @@ jobs:
         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 uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install --system torch==${{ matrix.pytorch_version }} torchvision
 
-      - name: Update requirements.txt
+      - name: Update pyproject.toml for nightly builds
         run: |
-          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
+          sed -i 's#"transformers==.*"#"transformers @ git+https://github.com/huggingface/transformers.git@main"#' pyproject.toml
+          sed -i 's#"peft==.*"#"peft @ git+https://github.com/huggingface/peft.git@main"#' pyproject.toml
+          sed -i 's#"accelerate==.*"#"accelerate @ git+https://github.com/huggingface/accelerate.git@main"#' pyproject.toml
+          sed -i 's#"trl==.*"#"trl @ git+https://github.com/huggingface/trl.git@main"#' pyproject.toml
+          sed -i 's#"datasets==.*"#"datasets @ git+https://github.com/huggingface/datasets.git@main"#' pyproject.toml
 
       - name: Install dependencies
         run: |
-          pip3 show torch
-          pip3 install --no-build-isolation -U -e .
+          uv pip show --system torch
+          uv pip install --system --no-build-isolation -e ".[dev]"
           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: |
@@ -84,9 +81,6 @@ jobs:
           pytest -v --durations=10 tests/patched/
           pytest -v --durations=10 tests/cli/
 
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
@@ -102,14 +96,14 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.6.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
@@ -120,13 +114,16 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
-          python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          uv pip install --system modal==1.0.2 jinja2
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=main-base-uv-py${{ 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}}" >> $GITHUB_ENV
@@ -136,7 +133,7 @@ jobs:
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.e2e_tests
+          modal run -m cicd.e2e_tests
   docker-e2e-multigpu-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
@@ -162,13 +159,16 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
-          python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          uv pip install --system modal==1.0.2 jinja2
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=main-base-uv-py${{ 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

.github/workflows/tests.yml

@@ -7,18 +7,16 @@ on:
       - "main"
     paths:
       - '**.py'
-      - 'requirements.txt'
+      - 'pyproject.toml'
       - '.github/workflows/*.yml'
-      - 'requirements-tests.txt'
       - 'cicd/cicd.sh'
       - 'cicd/Dockerfile.jinja'
   pull_request:
       types: [opened, synchronize, reopened, ready_for_review]
       paths:
        - '**.py'
-       - 'requirements.txt'
+       - 'pyproject.toml'
        - '.github/workflows/*.yml'
-       - 'requirements-tests.txt'
        - 'cicd/cicd.sh'
        - 'cicd/Dockerfile.jinja'
   workflow_dispatch:
@@ -41,7 +39,6 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-          cache: 'pip' # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
@@ -55,49 +52,42 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
+        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
-      - name: cleanup node
-        run: |
-          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
-
       - name: Check out repository code
         uses: actions/checkout@v4
 
-#      - name: Restore Cache from S3
-#        id: hf-cache-restore-s3
-#        run: |
-#          mkdir -p ~/.cache/huggingface/hub
-#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
-#
+      - 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
         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 uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
 
       - name: Install PyTorch
         run: |
-          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install --system torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
-          pip3 show torch
-          pip3 install --no-cache-dir --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: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+          uv pip show --system torch
+          uv pip install --system wheel
+          printf "torch==${{ matrix.pytorch_version }}\n" > torch-constraints.txt
+          uv pip install --system --no-cache-dir --no-build-isolation -e ".[dev]" --constraints torch-constraints.txt
+          set -o pipefail
+          python scripts/unsloth_install.py | bash
+          python scripts/cutcrossentropy_install.py | bash
 
       - name: Make sure PyTorch version wasn't clobbered
         run: |
@@ -113,14 +103,10 @@ jobs:
 
       - name: Run tests
         run: |
-          df -h
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
-          df -h
-          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
-          df -h
-          pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
-          df -h
-          pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
+          python -m pytest -v --durations=10 -n 8 --dist loadfile --cov=axolotl --cov-report=xml --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/
+          python -m pytest -v --durations=10 -n 8 --cov=axolotl --cov-append --cov-report=xml tests/monkeypatch/
+          python -m pytest -v --durations=10 -n 8 --cov=axolotl --cov-append --cov-report=xml tests/patched/
+          python -m pytest -v --durations=10 -n 8 --cov=axolotl --cov-append --cov-report=xml tests/cli/
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
@@ -130,6 +116,7 @@ jobs:
           flags: unittests,pytorch-${{ matrix.pytorch_version }}
           fail_ci_if_error: false
 
+
   pytest-sdist:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
@@ -138,50 +125,43 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
+        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
-      - name: cleanup node
-        run: |
-          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
-
       - name: Check out repository code
         uses: actions/checkout@v4
 
-#      - name: Restore Cache from S3
-#        id: hf-cache-restore-s3
-#        run: |
-#          mkdir -p ~/.cache/huggingface/hub
-#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
-#
+      - 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
         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 setuptools_scm build wheel psutil
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
 
       - name: Install PyTorch
         run: |
-          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install --system torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
-          pip3 show torch
-          python -m build --no-isolation --sdist
-          pip3 install --no-cache-dir --no-build-isolation dist/axolotl*.tar.gz
+          uv pip show --system torch
+          uv pip install --system wheel build setuptools_scm
+          python -m build --sdist
+          printf "torch==${{ matrix.pytorch_version }}\n" > torch-constraints.txt
+          tarball_path=$(echo dist/axolotl*.tar.gz)
+          uv pip install --no-cache-dir --no-build-isolation --system "${tarball_path}[dev]" --constraints torch-constraints.txt
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
       - name: Make sure PyTorch version wasn't clobbered
         run: |
@@ -192,13 +172,13 @@ jobs:
           axolotl --help
 
       - name: Show HF cache
-        run: hf cache scan
+        run: huggingface-cli scan-cache
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
-          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
-          pytest -v --durations=10 tests/cli/
+          python -m pytest -v --durations=10 -n 8 --dist loadfile --cov=axolotl --cov-report=xml --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/
+          python -m pytest -v --durations=10 -n 8 --cov=axolotl --cov-append --cov-report=xml tests/monkeypatch/
+          python -m pytest -v --durations=10 -n 8 tests/cli/
 
   gate-skip-e2e:
     needs: [pre-commit, pytest, pytest-sdist]
@@ -243,13 +223,19 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
-            dockerfile: "Dockerfile-uv.jinja"
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            num_gpus: 1
+            axolotl_extras:
+            dockerfile: "Dockerfile.jinja"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -257,13 +243,17 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          pip install modal==1.0.2 jinja2 protobuf
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=${{ github.ref_name }}-base-uv-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}}" >> $GITHUB_ENV
@@ -295,15 +285,15 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
+            pytorch: 2.6.0
+            num_gpus: 1
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
             pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
-#          - cuda: 128
-#            cuda_version: 12.8.1
-#            python_version: "3.11"
-#            pytorch: 2.7.1
-#            num_gpus: 1
-#            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -311,12 +301,6 @@ jobs:
             num_gpus: 1
             gpu_type: "B200"
             axolotl_extras: fbgemm-gpu
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            num_gpus: 1
-            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -324,13 +308,17 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          pip install modal==1.0.2 jinja2 protobuf
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=${{ github.ref_name }}-base-uv-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}}" >> $GITHUB_ENV
@@ -367,13 +355,17 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
+          pip install modal==1.0.2 jinja2 protobuf
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=${{ github.ref_name }}-base-uv-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

.gitignore

@@ -191,5 +191,5 @@ out/
 # vim
 *.swp
 
-# scm auto-versioning
+# setuptools-scm generated version file
 src/axolotl/_version.py

.pre-commit-config.yaml

@@ -11,13 +11,13 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.7
+    rev: v0.12.12
     hooks:
     -   id: ruff
         args: [--fix]
     -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.19.0
+    rev: v1.17.1
     hooks:
     - id: mypy
       additional_dependencies:
@@ -26,7 +26,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.9.2
+    rev: 1.8.6
     hooks:
     -   id: bandit
         args: [

.runpod/Dockerfile

@@ -1,16 +1,14 @@
 FROM axolotlai/axolotl-cloud:main-py3.11-cu124-2.6.0
 
 COPY .runpod/requirements.txt /requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install --upgrade pip && \
-    python3 -m pip install --upgrade -r /requirements.txt
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    /root/.local/bin/uv pip install --system -r /requirements.txt
 
 # Environment settings
 ARG BASE_VOLUME="/runpod-volume"
 ENV BASE_VOLUME=$BASE_VOLUME
 ENV HF_DATASETS_CACHE="${BASE_VOLUME}/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
-ENV HF_HUB_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
 ENV TRANSFORMERS_CACHE="${BASE_VOLUME}/huggingface-cache/hub"
 
 COPY .runpod/src /src

MANIFEST.in

@@ -1,6 +1,5 @@
-include requirements.txt
+include pyproject.toml
 include README.md
 include LICENSE
-include src/setuptools_axolotl_dynamic_dependencies.py
 include src/axolotl/utils/chat_templates/templates/*.jinja
-recursive-include axolotl *.py
+recursive-include src/axolotl *.py

README.md

@@ -29,10 +29,6 @@
 
 ## 🎉 Latest Updates
 
-- 2025/12: Axolotl now includes support for [Olmo3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/olmo3), [Trinity](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/trinity), and [Ministral3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/ministral3).
-- 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/qwen3-next), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3), [Granite 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/granite4), [HunYuan](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/hunyuan), [Magistral 2509](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral#vision), [Apertus](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/apertus), and [Seed-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/seed-oss).
-- 2025/09: Axolotl now has text diffusion training. Read more [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion).
-- 2025/08: QAT has been updated to include NVFP4 support. See [PR](https://github.com/axolotl-ai-cloud/axolotl/pull/3107).
 - 2025/07:
   - ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the [blog post](https://huggingface.co/blog/accelerate-nd-parallel) for more info.
   - Axolotl adds more models: [GPT-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss), [Gemma 3n](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n), [Liquid Foundation Model 2 (LFM2)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2), and [Arcee Foundation Models (AFM)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm).
@@ -40,12 +36,12 @@
   - [Voxtral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral), [Magistral 1.1](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral), and [Devstral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral) with mistral-common tokenizer support has been integrated in Axolotl!
   - TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!
 - 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
+- 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.
 
 <details>
 
 <summary>Expand older updates</summary>
 
-- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 - 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!
 - 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: (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!
@@ -69,15 +65,9 @@ Features:
 - **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 - LLM Fine-tuning in Minutes
 
-**Requirements**:
-
-- NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
-- Python 3.11
-- PyTorch ≥2.7.1
+**Requirements**: NVIDIA GPU (Ampere+) or AMD GPU, Python 3.11+
 
 ### Google Colab
 
@@ -85,15 +75,35 @@ Features:
 
 ### Installation
 
-#### Using pip
+#### Project setup (uv add)
 
 ```bash
-pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
+# Install uv
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Initialize or enter your project
+uv init my-project && cd my-project
+uv add axolotl
+uv pip install flash-attn --no-build-isolation
+source .venv/bin/activate
 
 # Download example axolotl configs, deepspeed configs
 axolotl fetch examples
-axolotl fetch deepspeed_configs  # OPTIONAL
+axolotl fetch deepspeed_configs  # optional
+```
+
+#### Quick try (uv pip)
+
+```bash
+# Install uv if needed
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+uv pip install axolotl
+uv pip install flash-attn --no-build-isolation
+
+# Download example axolotl configs, deepspeed configs
+axolotl fetch examples
+axolotl fetch deepspeed_configs  # optional
 ```
 
 #### Using Docker
@@ -158,13 +168,6 @@ 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.
 
-## 📈 Telemetry
-
-Axolotl has opt-out telemetry that helps us understand how the project is being used
-and prioritize improvements. We collect basic system information, model types, and
-error rates—never personal data or file paths. Telemetry is enabled by default. To
-disable it, set AXOLOTL_DO_NOT_TRACK=1. For more details, see our [telemetry documentation](https://docs.axolotl.ai/docs/telemetry.html).
-
 ## ❤️ Sponsors
 
 Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)

_quarto.yml

@@ -241,7 +241,6 @@ website:
             - docs/installation.qmd
             - docs/inference.qmd
             - docs/cli.qmd
-            - docs/telemetry.qmd
             - docs/config-reference.qmd
             - text: "API Reference"
               href: docs/api

cicd/Dockerfile-uv.jinja

@@ -1,53 +0,0 @@
-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 ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
-
-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 uv pip install torchvision
-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/Dockerfile.jinja

@@ -1,6 +1,10 @@
-FROM axolotlai/axolotl-base:{{ BASE_TAG }}
+FROM axolotlai/axolotl-base-uv:{{ BASE_TAG }}
 
-ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+SHELL ["/bin/bash", "-euxo", "pipefail", "-c"]
+
+ARG VENV_PYTHON="/workspace/axolotl-venv/bin/python"
+
+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 }}"
@@ -9,7 +13,7 @@ ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
 ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 ENV HF_HOME="{{ HF_HOME }}"
-ENV AXOLOTL_DATASET_NUM_PROC="8"
+ENV VENV_PYTHON=$VENV_PYTHON
 
 RUN apt-get update && \
     apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
@@ -25,25 +29,27 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # 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; \
+        sed -i 's#"transformers[^"]*"#"transformers @ git+https://github.com/huggingface/transformers.git@main"#' pyproject.toml; \
+        sed -i 's#"peft[^"]*"#"peft @ git+https://github.com/huggingface/peft.git@main"#' pyproject.toml; \
+        sed -i 's#"accelerate[^"]*"#"accelerate @ git+https://github.com/huggingface/accelerate.git@main"#' pyproject.toml; \
+        sed -i 's#"trl[^"]*"#"trl @ git+https://github.com/huggingface/trl.git@main"#' pyproject.toml; \
+        sed -i 's#"datasets[^"]*"#"datasets @ git+https://github.com/huggingface/datasets.git@main"#' pyproject.toml; \
     fi
 
-RUN pip install packaging==23.2 setuptools==75.8.0 psutil
+RUN uv pip install --python "$VENV_PYTHON" packaging==23.2 setuptools==75.8.0 pip
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        uv pip install --python "$VENV_PYTHON" --no-build-isolation -e .[ring-flash-attn,optimizers,ray,${AXOLOTL_EXTRAS}] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        uv pip install --python "$VENV_PYTHON" --no-build-isolation -e .[ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py | sh
-RUN python scripts/cutcrossentropy_install.py | sh
+RUN uv pip install --python "$VENV_PYTHON" --no-build-isolation flash-attn $AXOLOTL_ARGS
+
+RUN "$VENV_PYTHON" scripts/unsloth_install.py | sh
+RUN "$VENV_PYTHON" scripts/cutcrossentropy_install.py | sh
 
 # So we can test the Docker image
-RUN pip install -r requirements-dev.txt -r requirements-tests.txt
+RUN uv pip install --python "$VENV_PYTHON" -e ".[dev]"
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

@@ -4,7 +4,7 @@ set -e
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
 # Run unit tests with initial coverage report
-pytest -v --durations=10 -n8 \
+uv run pytest -v --durations=10 -n8 \
   --ignore=tests/e2e/ \
   --ignore=tests/patched/ \
   --ignore=tests/cli \
@@ -12,36 +12,36 @@ pytest -v --durations=10 -n8 \
   --cov=axolotl
 
 # Run lora kernels tests with coverage append
-pytest -v --durations=10 \
+uv run pytest -v --durations=10 \
   /workspace/axolotl/tests/e2e/patched/lora_kernels \
   --cov=axolotl \
   --cov-append
 
 # Run patched tests excluding lora kernels with coverage append
-pytest --full-trace -vvv --durations=10 \
+uv run pytest --full-trace -vvv --durations=10 \
   --ignore=tests/e2e/patched/lora_kernels \
   /workspace/axolotl/tests/e2e/patched \
   --cov=axolotl \
   --cov-append
 
 # Run solo tests with coverage append
-pytest -v --durations=10 -n1 \
+uv run pytest -v --durations=10 -n1 \
   /workspace/axolotl/tests/e2e/solo/ \
   --cov=axolotl \
   --cov-append
 
 # Run integration tests with coverage append
-pytest -v --durations=10 \
+uv run pytest -v --durations=10 \
   /workspace/axolotl/tests/e2e/integrations/ \
   --cov=axolotl \
   --cov-append
 
-pytest -v --durations=10 /workspace/axolotl/tests/cli \
+uv run pytest -v --durations=10 /workspace/axolotl/tests/cli \
   --cov=axolotl \
   --cov-append
 
 # Run remaining e2e tests with coverage append and final report
-pytest -v --durations=10 \
+uv run pytest -v --durations=10 \
   --ignore=tests/e2e/solo/ \
   --ignore=tests/e2e/patched/ \
   --ignore=tests/e2e/multigpu/ \
@@ -52,4 +52,4 @@ pytest -v --durations=10 \
   --cov-append \
   --cov-report=xml:e2e-coverage.xml
 
-codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION} || true
+uv run codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION} || true

cicd/multigpu.py

@@ -23,7 +23,7 @@ df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
     "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
     "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-uv-py3.11-cu126-2.6.0"),
     "CUDA": os.environ.get("CUDA", "126"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),

cicd/single_gpu.py

@@ -23,7 +23,7 @@ df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
     "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
     "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-uv-py3.11-cu126-2.6.0"),
     "CUDA": os.environ.get("CUDA", "126"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
@@ -65,13 +65,8 @@ def run_cmd(cmd: str, run_folder: str):
     import subprocess  # nosec
 
     sp_env = os.environ.copy()
-    sp_env["AXOLOTL_DATASET_NUM_PROC"] = "8"
+    sp_env["AXOLOTL_DATASET_PROCESSES"] = "8"
 
     # Propagate errors from subprocess.
-    try:
-        exit_code = subprocess.call(cmd.split(), cwd=run_folder, env=sp_env)  # nosec
-        if exit_code:
-            print(f"Command '{cmd}' failed with exit code {exit_code}")
-            return exit_code
-    except Exception as e:  # pylint: disable=broad-except
-        print(f"Command '{cmd}' failed with exception {e}")
+    if exit_code := subprocess.call(cmd.split(), cwd=run_folder, env=sp_env):  # nosec
+        exit(exit_code)

devtools/dev_chat_template.yml

@@ -13,7 +13,7 @@ datasets:
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model
 dataset_prepared_path: temp_debug/axolotl_outputs/data
-dataset_num_proc: 1
+dataset_processes: 1
 
 sequence_len: 4096
 sample_packing: false

docker/Dockerfile

@@ -1,13 +1,19 @@
-ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base:$BASE_TAG
+ARG BASE_TAG=main-base-uv
+FROM axolotlai/axolotl-base-uv:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
 ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ARG PYTORCH_VERSION="2.1.2"
+ARG GIT_REF="refs/heads/main"
+ARG GIT_SHA="HEAD"
+ARG VENV_PYTHON="/workspace/axolotl-venv/bin/python"
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
+ENV GIT_REF=$GIT_REF
+ENV GIT_SHA=$GIT_SHA
+ENV VENV_PYTHON=$VENV_PYTHON
 
 RUN apt-get update && \
     apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs && \
@@ -20,16 +26,19 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 
 WORKDIR /workspace/axolotl
 
+# Ensure we are on the expected commit and break Docker cache between revisions
+RUN git fetch origin "$GIT_REF" && git checkout "$GIT_SHA"
+
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        uv pip install --python "$VENV_PYTHON" --no-build-isolation -e .[ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        uv pip install --python "$VENV_PYTHON" --no-build-isolation -e .[ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi && \
-    python scripts/unsloth_install.py | sh && \
-    python scripts/cutcrossentropy_install.py | sh && \
-    pip install pytest && \
-    pip cache purge
+    uv pip install --python "$VENV_PYTHON" --no-build-isolation flash-attn $AXOLOTL_ARGS && \
+    "$VENV_PYTHON" scripts/unsloth_install.py | sh && \
+    "$VENV_PYTHON" scripts/cutcrossentropy_install.py | sh && \
+    uv pip install --python "$VENV_PYTHON" pytest
 
 # fix so that git fetch/pull from remote works with shallow clone
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

docker/Dockerfile-base

@@ -35,24 +35,18 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel psutil && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
+    CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir causal_conv1d==1.5.2 && \
+    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
     python3 -m pip cache purge
 
-RUN if [ "$CUDA" != "130" ] ; then \
-        CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@v1.5.4"; \
-        python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
-        python3 -m pip cache purge; \
-    fi
-
 RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN if [ "$PYTORCH_VERSION" = "2.9.1" ] && [ "$CUDA" = "128" ] ; then \
-        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        pip3 install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
+        FLASH_ATTENTION_FORCE_BUILD="TRUE" uv pip install --no-build-isolation flash-attn==2.8.0.post2; \
     fi

docker/Dockerfile-cloud

@@ -12,8 +12,8 @@ EXPOSE 22
 COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
 COPY scripts/motd /etc/motd
 
-RUN pip install jupyterlab notebook ipywidgets && \
-    jupyter lab clean
+RUN uv pip install --python "$VENV_PYTHON" jupyterlab notebook ipywidgets && \
+    "$VENV_PYTHON" -m jupyter lab clean
 RUN apt update && \
     apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
     rm -rf /var/cache/apt/archives && \

docker/Dockerfile-cloud-no-tmux

@@ -12,8 +12,8 @@ EXPOSE 22
 COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
 COPY scripts/motd /etc/motd
 
-RUN pip install jupyterlab notebook ipywidgets && \
-    jupyter lab clean
+RUN uv pip install --python "$VENV_PYTHON" jupyterlab notebook ipywidgets && \
+    "$VENV_PYTHON" -m jupyter lab clean
 RUN apt update && \
     apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm && \
     rm -rf /var/cache/apt/archives && \

docker/Dockerfile-tests

@@ -24,13 +24,14 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        uv pip install --no-build-isolation -e .[deepspeed,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
-    fi
+        uv pip install --no-build-isolation -e .[deepspeed,mamba-ssm] $AXOLOTL_ARGS; \
+    fi && \
+    uv pip install --no-build-isolation flash-attn $AXOLOTL_ARGS
 
 # So we can test the Docker image
-RUN pip install pytest
+RUN uv pip install pytest
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

docker/Dockerfile-uv-base

@@ -13,6 +13,7 @@ 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}"
+ENV VENV_PYTHON=/workspace/axolotl-venv/bin/python
 
 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/* \
@@ -29,14 +30,8 @@ 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==${PYTORCH_VERSION} torchvision \
-    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
-    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
-    && uv pip install awscli pydantic
-
-RUN if [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$CUDA" = "128" ] ; then \
-        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-    fi
+RUN uv pip install --python "$VENV_PYTHON" packaging setuptools wheel psutil protobuf grpclib \
+    && uv pip install --python "$VENV_PYTHON" torch==${PYTORCH_VERSION} \
+    && uv pip install --python "$VENV_PYTHON" --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
+    && uv pip install --python "$VENV_PYTHON" "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
+    && uv pip install --python "$VENV_PYTHON" awscli pydantic

docs/dataset-formats/conversation.qmd

@@ -218,13 +218,6 @@ If you have tool arguments with same name but different dtypes (like `"time": st
 ```
 "arguments": "{\"...\": \"...\"}"
 ```
-
-The same is applicable for tool parameters.
-
-```
-"parameters": "{\"...\": \"...\"}"
-```
-
 :::
 
 Example config for Llama4:

docs/debugging.qmd

@@ -29,7 +29,7 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 1. **Make sure you are using the latest version of axolotl**:  This project changes often and bugs get fixed fast.  Check your git branch and make sure you have pulled the latest changes from `main`.
 1. **Eliminate concurrency**: Restrict the number of processes to 1 for both training and data preprocessing:
     - Set `CUDA_VISIBLE_DEVICES` to a single GPU, ex: `export CUDA_VISIBLE_DEVICES=0`.
-    - Set `dataset_num_proc: 1` in your axolotl config or run the training command with `--dataset_num_proc=1`.
+    - Set `dataset_processes: 1` in your axolotl config or run the training command with `--dataset_processes=1`.
 2. **Use a small dataset**: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure `sample_packing: False` and `eval_sample_packing: False` to avoid errors.  If you are in a pinch and don't have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset, but will only use a fraction of the data for training.  For example, to shard the dataset into 20 pieces, add the following to your axolotl config):
 
     ```yaml
@@ -72,8 +72,8 @@ datasets:
 Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) of Axolotl, which ensures that changes you make to the code are reflected at runtime.  Run the following commands from the root of this project:
 
 ```bash
-pip3 install packaging
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+uv sync --extra deepspeed
+uv pip install flash-attn --no-build-isolation
 ```
 
 #### Remote Hosts
@@ -101,7 +101,7 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
                 "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
-                "--dataset_num_proc=1",      // limits data preprocessing to one process
+                "--dataset_processes=1",      // limits data preprocessing to one process
                 "--max_steps=1",              // limits training to just one step
                 "--batch_size=1",             // minimizes batch size
                 "--micro_batch_size=1",       // minimizes batch size
@@ -213,8 +213,8 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 You will now be in the container.  Next, perform an editable install of Axolotl:
 
 ```bash
-pip3 install packaging
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+uv sync --extra deepspeed
+uv pip install flash-attn --no-build-isolation
 ```
 
 ### Attach To Container

docs/faq.qmd

@@ -63,14 +63,6 @@ description: Frequently asked questions
 
 > A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717.
 
-**Q: Can we mix text and text+image datasets for VLM training?**
-
-> A: Yes, you can for newer VLM arch. The ones that would not work are LLaVA / Pixtral arch. If you notice one not working, please let us know!
-
-**Q: Why is `memory/max_*` different from `nvidia-smi`?**
-
-> A: We use `torch` APIs to retrieve this information. You can see https://docs.pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management for more information.
-
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

docs/installation.qmd

@@ -29,19 +29,40 @@ Follow the instructions at: [https://pytorch.org/get-started/locally/](https://p
 For Blackwell GPUs, please use Pytorch 2.7.0 and CUDA 12.8.
 :::
 
-### PyPI Installation (Recommended) {#sec-pypi}
+### uv Installation (Recommended) {#sec-uv-quick}
 
 ```{.bash}
-pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
+# Install uv if not already installed
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Add Axolotl to a project (recommended)
+uv init my-project && cd my-project
+uv add axolotl
+uv pip install flash-attn --no-build-isolation
+source .venv/bin/activate
+```
+
+For a quick one-off install without creating a project:
+
+```{.bash}
+uv pip install axolotl
+uv pip install flash-attn --no-build-isolation
+```
+
+### pip Installation {#sec-pypi}
+
+```{.bash}
+pip install --no-build-isolation axolotl[deepspeed]
+pip install --no-build-isolation flash-attn
 ```
 
 We use `--no-build-isolation` in order to detect the installed PyTorch version (if
 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.
+co-dependencies. Flash Attention is resolved separately so it can be built against
+the environment configured by the previous step.
 
-### uv Installation {#sec-uv}
+### Advanced 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.
 
@@ -62,28 +83,38 @@ 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]
-
+uv pip install --no-build-isolation axolotl[deepspeed]
 # 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]
+# uv pip install --no-build-isolation axolotl[deepspeed,vllm]
+
+uv pip install flash-attn --no-build-isolation
 ```
 
 ### Edge/Development Build {#sec-edge-build}
 
 For the latest features between releases:
 
+#### Using uv (recommended)
 ```{.bash}
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+curl -LsSf https://astral.sh/uv/install.sh | sh  # If not already installed
+uv sync
+uv pip install flash-attn --no-build-isolation
+```
+
+#### Using pip
+```{.bash}
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+pip install --no-build-isolation -e '.[deepspeed]'
+pip install --no-build-isolation flash-attn
 ```
 
 ### Docker {#sec-docker}
@@ -141,7 +172,7 @@ For providers supporting Docker:
 ### macOS {#sec-macos}
 
 ```{.bash}
-pip3 install --no-build-isolation -e '.'
+uv pip install --no-build-isolation -e '.'
 ```
 
 See @sec-troubleshooting for Mac-specific issues.
@@ -159,10 +190,15 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
 1. Install Python ≥3.11
 2. Install PyTorch: https://pytorch.org/get-started/locally/
 3. Install Axolotl:
-   ```{.bash}
-   pip3 install -U packaging setuptools wheel ninja
-   pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
-   ```
+```{.bash}
+# Option A: add Axolotl to the environment
+uv add axolotl
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install axolotl
+uv pip install flash-attn --no-build-isolation
+```
 4. (Optional) Login to Hugging Face:
    ```{.bash}
    huggingface-cli login

docs/lr_groups.qmd

@@ -27,9 +27,3 @@ learning_rate: 2e-5
 In this example, we have a default learning rate of 2e-5 across the entire model, but we have a separate learning rate
 of 1e-6 for all the self attention `o_proj` modules across all layers, and a learning are of 1e-5 to the 3rd layer's
 self attention `q_proj` module.
-
-::: {.callout-note}
-
-We currently only support varying `lr` for now. If you're interested in adding support for others (`weight_decay`), we welcome PRs. See https://github.com/axolotl-ai-cloud/axolotl/blob/613bcf90e58f3ab81d3827e7fc572319908db9fb/src/axolotl/core/trainers/mixins/optimizer.py#L17
-
-:::

docs/multi-gpu.qmd

@@ -4,7 +4,7 @@ format:
   html:
     toc: true
     toc-depth: 3
-    # number-sections: true
+    number-sections: true
     code-tools: true
 execute:
   enabled: false
@@ -14,18 +14,12 @@ This guide covers advanced training configurations for multi-GPU setups using Ax
 
 ## Overview {#sec-overview}
 
-When training on multiple GPUs, Axolotl supports 3 sharding/parallelism strategies. Additionally, you can layer specific optimization features on top of that strategy.
+Axolotl supports several methods for multi-GPU training:
 
-You generally cannot combine these strategies; they are mutually exclusive.
-
-1.  **DeepSpeed**: Powerful optimization library, supports ZeRO stages 1-3.
-2.  **FSDP (Fully Sharded Data Parallel)**: PyTorch's native sharding implementation (Recommended).
-3.  **DDP (Distributed Data Parallel)**: PyTorch's native parallelism implementation (Default if neither of the above are selected).
-
-These features can often be combined with the strategies above:
-
-*   **Sequence Parallelism**: Splits long sequences across GPUs (Compatible with DDP, DeepSpeed, and FSDP).
-*   **FSDP + QLoRA**: Combines 4-bit quantization with FSDP (Specific to FSDP).
+- DeepSpeed (recommended)
+- FSDP (Fully Sharded Data Parallel)
+- Sequence parallelism
+- FSDP + QLoRA
 
 ## DeepSpeed {#sec-deepspeed}
 
@@ -71,18 +65,12 @@ Start from Stage 1 -> Stage 2 -> Stage 3.
 
 ## Fully Sharded Data Parallel (FSDP) {#sec-fsdp}
 
-FSDP allows you to shard model parameters, gradients, and optimizer states across data parallel workers.
-
 ::: {.callout-note}
 
 FSDP2 is recommended for new users. FSDP1 is deprecated and will be removed in an upcoming release of Axolotl.
 
 :::
 
-### FSDP + QLoRA {#sec-fsdp-qlora}
-
-For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
-
 ### Migrating from FSDP1 to FSDP2 {#sec-migrate-fsdp1-fsdp2}
 
 To migrate your config from FSDP1 to FSDP2, you must use the `fsdp_version` top-level config field to specify the FSDP version, and
@@ -100,7 +88,6 @@ fsdp_sync_module_states | **REMOVED**
 fsdp_cpu_ram_efficient_loading | cpu_ram_efficient_loading
 fsdp_state_dict_type | state_dict_type
 fsdp_use_orig_params | **REMOVED**
-fsdp_activation_checkpointing | activation_checkpointing
 
 For more details, please see the migration guide in the [torchtitan repo](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md). In Axolotl,
 if you were using the following FSDP1 config:
@@ -157,6 +144,10 @@ single sequence causes OOM errors during model training.
 
 See our [dedicated guide](sequence_parallelism.qmd) for more information.
 
+### FSDP + QLoRA {#sec-fsdp-qlora}
+
+For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
+
 ## Performance Optimization {#sec-performance}
 
 ### Liger Kernel Integration {#sec-liger}

docs/multimodal.qmd

@@ -56,14 +56,10 @@ image_resize_algorithm: bilinear
 
 Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
 
-::: {.callout-tip}
+::: {.callout-warning}
 Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
 :::
 
-::: {.callout-note}
-As of now, we do not truncate nor drop samples based on `sequence_len` as each arch has different ways to process non-text tokens. We are looking for help on this.
-:::
-
 ### Mllama {#sec-mllama}
 
 ```yaml
@@ -99,7 +95,7 @@ chat_template: llava
 ### Mistral-Small-3.1 {#sec-mistral-small-31}
 
 ::: {.callout-tip}
-Please make sure to install vision lib via `pip install 'mistral-common[opencv]==1.8.5'`
+Please make sure to install vision lib via `uv pip install 'mistral-common[opencv]==1.8.5'`
 :::
 
 ```yaml
@@ -109,7 +105,7 @@ base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 ### Magistral-Small-2509 {#sec-magistral-small-2509}
 
 ::: {.callout-tip}
-Please make sure to install vision lib via `pip install 'mistral-common[opencv]==1.8.5'`
+Please make sure to install vision lib via `uv pip install 'mistral-common[opencv]==1.8.5'`
 :::
 
 ```yaml
@@ -119,13 +115,11 @@ base_model: mistralai/Magistral-Small-2509
 ### Voxtral {#sec-voxtral}
 
 ::: {.callout-tip}
-Please make sure to install audio lib via `pip3 install librosa==0.11.0 'mistral_common[audio]==1.8.3'`
+Please make sure to install audio lib via `uv pip install librosa==0.11.0 'mistral_common[audio]==1.8.3'`
 :::
 
 ```yaml
 base_model: mistralai/Voxtral-Mini-3B-2507
-
-processor_type: VoxtralProcessor
 ```
 
 ### Gemma-3 {#sec-gemma-3}
@@ -149,7 +143,7 @@ The model's initial loss and grad norm will be very high. We suspect this to be
 :::
 
 ::: {.callout-tip}
-Please make sure to install `timm` via `pip3 install timm==1.0.17`
+Please make sure to install `timm` via `uv pip install timm==1.0.17`
 :::
 
 ```yaml
@@ -174,18 +168,10 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
-### Qwen3-VL {#sec-qwen3-vl}
-
-```yaml
-base_model: Qwen/Qwen3-VL-4B-Instruct
-
-chat_template: qwen2_vl  # same as qwen2-vl
-```
-
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}
-Please make sure to install `num2words` via `pip3 install num2words==0.5.14`
+Please make sure to install `num2words` via `uv pip install num2words==0.5.14`
 :::
 
 ```yaml
@@ -195,7 +181,7 @@ base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
 ### LFM2-VL {#sec-lfm2-vl}
 
 ::: {.callout-warning}
-Please uninstall `causal-conv1d` via `pip3 uninstall -y causal-conv1d`
+Please uninstall `causal-conv1d` via `uv pip uninstall -y causal-conv1d`
 :::
 
 ```yaml
@@ -236,7 +222,7 @@ For audio loading, you can use the following keys within `content` alongside `"t
 
 ::: {.callout-tip}
 
-You may need to install `librosa` via `pip3 install librosa==0.11.0`.
+You may need to install `librosa` via `uv pip install librosa==0.11.0`.
 
 :::
 

docs/rlhf.qmd

@@ -219,21 +219,6 @@ DPO supports the following types with the following dataset format:
 }
 ```
 
-#### chat_template.argilla_chat
-
-```json
-{
-    "chosen": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ],
-    "rejected": [
-        {"role": "user", "content": "..."},
-        {"role": "assistant", "content": "..."}
-    ]
-}
-```
-
 #### chat_template.default
 
 ```yaml
@@ -597,116 +582,6 @@ To see other examples of custom reward functions, please see [TRL GRPO Docs](htt
 
 To see all configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/src/axolotl/utils/schemas/trl.py).
 
-#### OpenEnv Rollout Functions
-
-GRPO supports custom rollout functions for OpenEnv-style environments, enabling interactive tasks like web browsing, code execution, or tool use. This allows you to implement custom generation logic that interacts with external environments.
-
-For example, to implement a simple math-solving environment with step-by-step verification:
-
-```python
-# math_env.py
-import re
-
-def math_solver_rollout(model, processing_class, prompts, generation_config=None):
-    """
-    Custom rollout function that generates step-by-step math solutions.
-
-    Args:
-        model: The language model
-        processing_class: The tokenizer/processing_class
-        prompts: List of prompt dicts (with 'messages' key for chat format)
-        generation_config: Optional generation configuration
-
-    Returns:
-        List of completion strings
-    """
-    completions = []
-
-    for prompt in prompts:
-        # Apply chat template to prompt
-        messages = prompt.get("messages", [])
-        formatted_prompt = processing_class.apply_chat_template(
-            messages, processing_class=False, add_generation_prompt=True
-        )
-
-        # Generate step-by-step solution
-        full_response = ""
-        for step in range(5):  # Max 5 reasoning steps
-            current_input = formatted_prompt + full_response + "\nNext step:"
-            inputs = processing_class(current_input, return_tensors="pt").to(model.device)
-
-            outputs = model.generate(
-                **inputs,
-                max_new_tokens=100,
-                generation_config=generation_config,
-            )
-            step_text = processing_class.decode(
-                outputs[0][inputs.input_ids.shape[1]:],
-                skip_special_tokens=True
-            )
-
-            # Check if solution is complete
-            if "FINAL ANSWER:" in step_text:
-                full_response += step_text
-                break
-            full_response += step_text + "\n"
-
-        completions.append(full_response)
-
-    return completions
-
-def math_reward(prompts, completions, answers, **kwargs):
-    """Reward function that checks mathematical correctness"""
-    rewards = []
-    for completion, correct_answer in zip(completions, answers):
-        # Extract predicted answer
-        match = re.search(r"FINAL ANSWER:\s*(.+)", completion)
-        predicted = match.group(1).strip() if match else ""
-
-        # Compare with correct answer
-        reward = 1.0 if predicted == str(correct_answer) else 0.0
-        rewards.append(reward)
-
-    return rewards
-
-def math_transform(cfg, *args, **kwargs):
-    """Transform dataset to GRPO format with answer field"""
-    def transform_fn(example, processing_class=None):
-        return {
-            "prompt": [{"role": "user", "content": example["question"]}],
-            "answer": str(example["answer"]),
-        }
-    return transform_fn, {"remove_columns": ["question"]}
-```
-
-```yaml
-rl: grpo
-
-trl:
-  beta: 0.001
-  max_completion_length: 512
-  num_generations: 4
-  rollout_func: "math_env.math_solver_rollout"  # Custom rollout function
-  reward_funcs: ["math_env.math_reward"]
-  reward_weights: [1.0]
-
-datasets:
-  - path: openai/gsm8k
-    name: main
-    type: math_env.math_transform
-```
-
-The `rollout_func` parameter accepts a fully qualified name (e.g., `module_name.function_name`) that points to a callable function in your local directory. The function receives:
-
-- `model`: The language model
-- `processing_class`: The tokenizer/processing class
-- `prompts`: List of prompt dictionaries
-- `generation_config` (optional): Generation configuration
-
-And should return a list of completion strings.
-
-For more OpenEnv examples, see [TRL OpenEnv Documentation](https://huggingface.co/docs/trl/main/en/openenv).
-
 #### 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.

docs/sequence_parallelism.qmd

@@ -49,9 +49,9 @@ When sequence parallelism is enabled:
 To use sequence parallelism, you need:
 
 - Multiple GPUs (at least 2)
-- The `ring-flash-attn` package. Install with:
-  - `pip install axolotl[ring-flash-attn]` (preferred)
-  - `pip install ring-flash-attn>=0.1.4`
+- The `ring-flash-attn` package. Install with either `uv sync --extra ring-flash-attn`
+  (from a cloned repository) or `uv pip install ring-flash-attn>=0.1.4`.
+- Flash Attention installed separately with `uv pip install flash-attn --no-build-isolation`.
 
 ## Limitations
 

docs/telemetry.qmd

@@ -1,61 +0,0 @@
----
-title: Telemetry
-description: A description of the telemetry implementation in Axolotl.
----
-
-# Telemetry in Axolotl
-
-Axolotl implements anonymous telemetry to help maintainers understand how the library
-is used and where users encounter issues. This data helps prioritize features, optimize
-performance, and fix bugs.
-
-## Data Collection
-
-We collect:
-
-- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
-version, etc.
-- Hardware info: CPU count, memory, GPU count and models
-- Runtime metrics: Training progress, memory usage, timing information
-- Usage patterns: Models (from a whitelist) and configurations used
-- Error tracking: Stack traces and error messages (sanitized to remove personal
-information)
-
-Personally identifiable information (PII) is not collected.
-
-## Implementation
-
-Telemetry is implemented using PostHog and consists of:
-
-- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
-telemetry system and provides methods for tracking events.
-- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
-sends sanitized stack traces.
-- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
-runtime metrics during training.
-- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
-runtime metrics telemetry.
-
-The telemetry system will block training startup for 10 seconds to ensure users are
-aware of data collection, unless telemetry is explicitly enabled or disabled.
-
-## Opt-Out Mechanism
-
-Telemetry is **enabled by default** on an opt-out basis. To disable it, set
-`AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1`.
-
-A warning message will be logged on start to clearly inform users about telemetry.
-We will remove this after some period.
-
-To hide the warning message about telemetry that is displayed on train, etc. startup,
-explicitly set: `AXOLOTL_DO_NOT_TRACK=0` (enable telemetry) or `AXOLOTL_DO_NOT_TRACK=1`
-(explicitly disable telemetry).
-
-## Privacy
-
-- All path-like config information is automatically redacted from telemetry data
-- Model information is only collected for whitelisted organizations
-    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
-- Each run generates a unique anonymous ID
-    - This allows us to link different telemetry events in a single same training run
-- Telemetry is only sent from the main process to avoid duplicate events

examples/LiquidAI/README.md

@@ -6,17 +6,20 @@ LFM2 features a new hybrid Liquid architecture with multiplicative gates, short-
 
 This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
 
-Thanks to the team at LiquidAI for giving us early access to prepare for these releases.
-
 ## Getting Started
 
 1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
 
     Here is an example of how to install from pip:
     ```bash
-    # Ensure you have a compatible version of Pytorch installed
-    pip3 install packaging setuptools wheel ninja
-    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    # Ensure you have a compatible version of PyTorch installed
+    # Option A: manage dependencies in your project
+    uv add 'axolotl>=0.12.0'
+    uv pip install flash-attn --no-build-isolation
+
+    # Option B: quick install
+    uv pip install 'axolotl>=0.12.0'
+    uv pip install flash-attn --no-build-isolation
     ```
 
 2.  Run one of the finetuning examples below.
@@ -33,19 +36,11 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
     axolotl train examples/LiquidAI/lfm2-vl-lora.yaml
     ```
 
-    **LFM2-MoE**
-    ```bash
-    pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
-
-    # LoRA SFT (1x48GB @ 16.2GiB)
-    axolotl train examples/LiquidAI/lfm2-8b-a1b-lora.yaml
-    ```
-
 ### TIPS
 
 - **Installation Error**: If you encounter `ImportError: ... undefined symbol ...` or `ModuleNotFoundError: No module named 'causal_conv1d_cuda'`, the `causal-conv1d` package may have been installed incorrectly. Try uninstalling it:
   ```bash
-  pip uninstall -y causal-conv1d
+  uv pip uninstall -y causal-conv1d
   ```
 
 - **Dataset Loading**: Read more on how to load your own dataset in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
@@ -55,13 +50,14 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
 
 ## Optimization Guides
 
-- [Optimizations Guide](https://docs.axolotl.ai/docs/optimizations.html)
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
 
 ## Related Resources
 
 - [LFM2 Blog](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models)
 - [LFM2-VL Blog](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models)
-- [LFM2-MoE Blog](https://www.liquid.ai/blog/lfm2-8b-a1b-an-efficient-on-device-mixture-of-experts)
 - [Axolotl Docs](https://docs.axolotl.ai)
 - [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
 - [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/LiquidAI/lfm2-350m-fft.yaml

@@ -1,7 +1,6 @@
 base_model: LiquidAI/LFM2-350M
 
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+chunked_cross_entropy: true
 
 eot_tokens:
   - "<|im_end|>"

examples/LiquidAI/lfm2-8b-a1b-lora.yaml

@@ -1,59 +0,0 @@
-base_model: LiquidAI/LFM2-8B-A1B
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: true
-
-eot_tokens:
-  - "<|im_end|>"
-datasets:
-  - path: mlabonne/FineTome-100k
-    type: chat_template
-    split: train[:20%]
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-output_dir: ./outputs/out
-
-sequence_len: 4096
-sample_packing: true
-
-adapter: lora
-lora_model_dir:
-
-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'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 4
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-bf16: true
-tf32: true
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 2
-saves_per_epoch: 1
-
-weight_decay: 0.0
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -3,9 +3,6 @@ trust_remote_code: true
 model_type: AutoModelForImageTextToText
 processor_type: AutoProcessor
 
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true
 remove_unused_columns: false

examples/apertus/README.md

@@ -15,8 +15,8 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 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]'
+uv sync
+uv pip install flash-attn --no-build-isolation
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh
@@ -31,7 +31,7 @@ python scripts/cutcrossentropy_install.py | sh
 # For those using our Docker image, use the below path.
 export CUDA_HOME=/usr/local/cuda
 
-pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+uv pip install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
 ```
 
 For any installation errors, see [XIELU Installation Issues](#xielu-installation-issues)
@@ -67,7 +67,7 @@ If those didn't help, please try the below solutions:
 1. Pass env for CMAKE and try install again:
 
     ```bash
-    Python_EXECUTABLE=$(which python) pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+    Python_EXECUTABLE=$(which python) uv pip install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
     ```
 
 2. Git clone the repo and manually hardcode python path:
@@ -92,7 +92,7 @@ If those didn't help, please try the below solutions:
     ```
 
     ```bash
-    pip3 install . --no-build-isolation --no-deps
+    uv pip install . --no-build-isolation --no-deps
     ```
 
 ## Optimization Guides

examples/arcee/README.md

@@ -17,8 +17,8 @@ Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the A
 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]'
+uv sync
+uv pip install flash-attn --no-build-isolation
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh

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

@@ -12,10 +12,10 @@
     "\n",
     "Axolotl is the most performant LLM post-training framework available, delivering faster training with efficient, consistent and stable performance. Train your workload and ship your product 30% faster; saving you both time and money.\n",
     "\n",
-    "- ⭐ us on [GitHub](https://github.com/axolotl-ai-cloud/axolotl)\n",
-    "- 📜 Read the [Docs](http://docs.axolotl.ai/)\n",
-    "- 💬 Chat with us on [Discord](https://discord.gg/mnpEYgRUmD)\n",
-    "- 📰 Get updates on [X/Twitter](https://x.com/axolotl_ai)\n"
+    "- \u2b50 us on [GitHub](https://github.com/axolotl-ai-cloud/axolotl)\n",
+    "- \ud83d\udcdc Read the [Docs](http://docs.axolotl.ai/)\n",
+    "- \ud83d\udcac Chat with us on [Discord](https://discord.gg/mnpEYgRUmD)\n",
+    "- \ud83d\udcf0 Get updates on [X/Twitter](https://x.com/axolotl_ai)\n"
    ]
   },
   {
@@ -39,8 +39,8 @@
    "source": [
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
-    "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88\""
+    "!uv pip install --no-build-isolation axolotl>=0.9.1\n!uv pip install flash-attn --no-build-isolation\n",
+    "!uv pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28\""
    ]
   },
   {
@@ -253,6 +253,7 @@
    "source": [
     "from axolotl.utils import set_pytorch_cuda_alloc_conf\n",
     "\n",
+    "# Set \"PYTORCH_CUDA_ALLOC_CONF\" env to save memory\n",
     "set_pytorch_cuda_alloc_conf()"
    ]
   },
@@ -1370,7 +1371,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv\u2026"
       ]
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       "description_tooltip": null,
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+      "placeholder": "\u200b",
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-      "value": " 11.4M/11.4M [00:00&lt;00:00, 21.8MB/s]"
+      "value": "\u200711.4M/11.4M\u2007[00:00&lt;00:00,\u200721.8MB/s]"
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-      "placeholder": "​",
+      "placeholder": "\u200b",
       "style": "IPY_MODEL_590eef89881545aa8bbef9a8bbe7fb00",
       "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
      }
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examples/devstral/README.md

@@ -16,8 +16,13 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Option A: manage dependencies in your project
+uv add 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
 ```
 
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,7 +1,7 @@
 base_model: google/gemma-3-1b-it
-
-model_type: Gemma3ForCausalLM
-
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,7 +1,7 @@
 base_model: google/gemma-3-270m-it
-
-model_type: Gemma3ForCausalLM
-
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 

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

@@ -1,8 +1,5 @@
 base_model: google/gemma-3-4b-it
 
-# Need to set else transformers tries to load vision too
-model_type: Gemma3ForCausalLM
-
 load_in_4bit: true
 
 # gemma3 doesn't seem to play nice with ddp

examples/gemma3n/README.md

@@ -10,17 +10,22 @@ Gemma-3n is a family of multimodal models from Google found on [HuggingFace](htt
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Option A: manage dependencies in your project
+uv add 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
 ```
 
 2. In addition to Axolotl's requirements, Gemma-3n requires:
 
 ```bash
-pip3 install timm==1.0.17
+uv pip install timm==1.0.17
 
 # for loading audio data
-pip3 install librosa==0.11.0
+uv pip install librosa==0.11.0
 ```
 
 3. Download sample dataset files

examples/gpt-oss/README.md

@@ -2,8 +2,6 @@
 
 [GPT-OSS](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) are a family of open-weight MoE models trained by OpenAI, released in August 2025. There are two variants: 20B and 120B.
 
-In October 2025, OpenAI released safeguard models built upon GPT-OSS called [GPT-OSS-Safeguard](https://huggingface.co/collections/openai/gpt-oss-safeguard). They use the same architecture, so the same examples below can be re-used.
-
 This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
 
 ## Getting started
@@ -14,8 +12,13 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Option A: manage dependencies in your project
+uv add 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
 ```
 
 2. Choose one of the following configs below for training the 20B model. (for 120B, see [below](#training-120b))
@@ -66,16 +69,6 @@ axolotl merge-sharded-fsdp-weights examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offlo
 mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
 ```
 
-### How to set reasoning_effort in template?
-
-The harmony template has a feature to set the `reasoning_effort` during prompt building. The default is `medium`. If you would like to adjust this, you can add the following to your config:
-
-```yaml
-chat_template_kwargs:
-  reasoning_effort: "high"  # low | medium | high
-```
-
-Currently, this applies globally. There is no method to apply per sample yet. If you are interested in adding this, please feel free to create an Issue to discuss.
 
 ### Inferencing your fine-tuned model
 
@@ -87,7 +80,7 @@ for more information about using a special vllm-openai docker image for inferenc
 Optionally, vLLM can be installed from nightly:
 
 ```bash
-pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+uv pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
 ```
 and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
 ```bash

examples/gpt-oss/gpt-oss-safeguard-20b-sft-lora-singlegpu.yaml

@@ -1,67 +0,0 @@
-base_model: openai/gpt-oss-safeguard-20b
-use_kernels: true
-model_quantization_config: Mxfp4Config
-model_quantization_config_kwargs:
-  dequantize: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-experimental_skip_move_to_device: true  # prevent OOM by not putting model to GPU before sharding
-
-datasets:
-  - path: HuggingFaceH4/Multilingual-Thinking
-    type: chat_template
-    field_thinking: thinking
-    template_thinking_key: thinking
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/gpt-oss-safeguard-out/
-
-sequence_len: 4096
-sample_packing: true
-
-adapter: lora
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.0  # dropout not supported when using LoRA over expert parameters
-lora_target_linear: true
-
-# TODO: not supported for now, see peft#2710
-#lora_target_parameters:  # target the experts in the last two layers
-#  - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
-#  - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
-#  - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
-#  - "23._checkpoint_wrapped_module.mlp.experts.down_proj"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-
-optimizer: adamw_torch_8bit
-lr_scheduler: constant_with_warmup
-learning_rate: 2e-4
-
-bf16: true
-tf32: true
-
-flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
-
-gradient_checkpointing: true
-activation_offloading: true
-
-logging_steps: 1
-saves_per_epoch: 1
-warmup_ratio: 0.1
-
-special_tokens:
-eot_tokens:
-  - "<|end|>"

examples/granite4/README.md

@@ -1,65 +0,0 @@
-# Finetune IBM's Granite 4.0 with Axolotl
-
-[Granite 4.0](https://huggingface.co/collections/ibm-granite/granite-40-language-models) are a family of open source models trained by IBM Research.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Granite4 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 2.7.1 min)
-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]'
-
-# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
-python scripts/cutcrossentropy_install.py | sh
-```
-
-2. Run the finetuning example:
-
-```bash
-axolotl train examples/granite4/granite-4.0-tiny-fft.yaml
-```
-
-This config uses about 40.8GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-### Limitation
-
-Adapter finetuning does not work at the moment. It would error with
-
-```bash
-RuntimeError: mat1 and mat2 shapes cannot be multiplied (4096x3072 and 1x1179648)
-```
-
-In addition, if adapter training works, `lora_target_linear: true` will not work due to:
-```bash
-ValueError: Target module GraniteMoeHybridParallelExperts() is not supported.
-```
-
-## 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)
-
-## Related Resources
-
-- [Granite Docs](https://www.ibm.com/granite/docs/models/granite)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/granite4/granite-4.0-tiny-fft.yaml

@@ -1,45 +0,0 @@
-base_model: ibm-granite/granite-4.0-tiny-preview
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/model-out
-
-sequence_len: 2048
-sample_packing: true
-
-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
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/hunyuan/README.md

@@ -13,8 +13,8 @@ Tencent released a family of opensource models called HunYuan with varying param
 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]'
+uv sync
+uv pip install flash-attn --no-build-isolation
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh

examples/llama-3/3b-fp8-fsdp2.yaml

@@ -29,7 +29,7 @@ flex_attention: true
 flex_attn_compile_kwargs:
   dynamic: false
   mode: max-autotune-no-cudagraphs
-save_strategy: no
+
 torch_compile: true
 
 wandb_project:

examples/llama-3/opentelemetry-qlora.yml

@@ -1,50 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_4bit: true
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-
-output_dir: ./outputs/opentelemetry-example
-
-adapter: qlora
-sequence_len: 512
-sample_packing: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-# OpenTelemetry Configuration
-use_otel_metrics: true
-otel_metrics_host: "localhost"
-otel_metrics_port: 8000
-
-# Disable WandB
-use_wandb: false
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-logging_steps: 1
-flash_attention: false
-
-warmup_ratio: 0.1
-evals_per_epoch: 2
-saves_per_epoch: 1
-weight_decay: 0.0
-
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/magistral/README.md

@@ -13,9 +13,14 @@ Thanks to the team at MistralAI for giving us early access to prepare for these
     Here is an example of how to install from pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.7.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Ensure you have PyTorch installed (PyTorch 2.6.0 min)
+# Option A: manage dependencies in your project
+uv add 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
 ```
 
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage

examples/magistral/think/README.md

@@ -12,7 +12,7 @@ Before starting, ensure you have:
 Run the thinking model fine-tuning:
 
 ```bash
-axolotl train examples/magistral/think/magistral-small-think-qlora.yaml
+axolotl train magistral-small-think-qlora.yaml
 ```
 
 This config uses about 19.1 GiB VRAM.

examples/magistral/vision/README.md

@@ -21,7 +21,7 @@ Before starting, ensure you have:
 
 3. Run the fine-tuning:
    ```bash
-   axolotl train examples/magistral/vision/magistral-small-vision-24B-qlora.yml
+   axolotl train magistral-small-vision-24B-qlora.yml
    ```
 
 This config uses about 17GiB VRAM.

examples/ministral/README.md

@@ -1,50 +0,0 @@
-# Finetune Ministral with Axolotl
-
-Ministral is a family of openweight models from MistralAI found on [HuggingFace](mistralai/Ministral-8B-Instruct-2410). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/ministral/ministral-small-qlora.yaml
-    ```
-
-This config uses about 8.76 GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### Tips
-
-- We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled `SYSTEM_PROMPT.txt`.
-- 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 text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
-
-In addition, we do not support overriding tokens yet.
-
-## Related Resources
-
-- [MistralAI Ministral Blog](https://mistral.ai/news/ministraux)
-- [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, etc.
-- Add parity to other tokenizer configs like overriding tokens.

examples/ministral/ministral-small-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-8B-Instruct-2410
-
-# 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
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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
-
-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
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/README.md

@@ -1,79 +0,0 @@
-# Finetune Ministral3 with Axolotl
-
-Ministral3 is a family of open-weight models from MistralAI found on [HuggingFace](https://huggingface.co/collections/mistralai/ministral-3). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-Please see [Thinking](#thinking) and [Vision](#vision) for their respective fine-tuning.
-
-Thanks to the team at MistralAI for giving us early access to prepare for these releases.
-
-Note: This is still experimental given it is based on transformers v5 RC.
-
-## Getting started
-
-1. Install Axolotl from source following the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Swap to the Axolotl transformers v5 branch
-
-    ```bash
-    cp examples/ministral3/ministral3-3b-qlora.yaml ministral3-3b-qlora.yaml
-
-    git fetch
-    git checkout transformers-v5
-
-    # Install packages for transformers v5
-    pip install -e .
-    ```
-
-4. Run the fine-tuning:
-
-    ```bash
-    axolotl train ministral3-3b-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-
-### Tips
-
-- We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled `SYSTEM_PROMPT.txt`.
-- 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 text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-### Thinking
-
-Ministral3 2512 model supports thinking capabilities, enabling Chain-of-Thought reasoning with explicit thinking steps.
-
-📚 **[See the Thinking fine-tuning guide →](./think/README.md)**
-
-### Vision
-
-Ministral3 2512 model also supports vision capabilities.
-
-📚 **[See the Vision fine-tuning guide →](./vision/README.md)**
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
-
-In addition, we do not support overriding tokens yet.
-
-## Related Resources
-
-- [MistralAI Mistral3 Blog](https://mistral.ai/news/mistral-3)
-- [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, etc.
-- Add parity to other tokenizer configs like overriding tokens.

examples/ministral3/ministral3-3b-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-
-# 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
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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
-
-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
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/think/README.md

@@ -1,73 +0,0 @@
-# Ministral3 2512 Thinking Fine-tuning
-
-This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collections/mistralai/ministral-3) with thinking capabilities using Axolotl. The thinking model enables explicit Chain-of-Thought reasoning with separate thinking and response sections.
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Installed Axolotl (see [main README](../README.md))
-
-## Getting Started
-
-Run the thinking model fine-tuning:
-
-```bash
-axolotl train examples/ministral3/think/ministral3-3b-think-qlora.yaml
-```
-
-This config uses about 4.76 GiB VRAM.
-
-### Tips
-
-- Dataset uses multi-content format with `type: thinking` support. See [Dataset Format](#dataset-format) below.
-- You cannot mix `content: str` and `content: list[dict]`, otherwise, dataset loading will fail. Keep it consistent.
-
-## Dataset Format
-
-The thinking model requires the multi-content dataset format with support for an extra `role: thinking` within system and assistant messages.
-
-Example format:
-
-```json
-{
-    "messages": [
-        {
-            "role": "system",
-            "content": [
-                { "type": "text", "text": "{SYSTEM_PROMPT}"}
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                { "type": "text", "text": "Solve this step by step: What is 15% of 240?"}
-            ]
-        },
-        {
-            "role": "assistant",
-            "content": [
-                {
-                    "type": "thinking",
-                    "thinking": "I need to calculate 15% of 240. First, I'll convert 15% to decimal: 0.15. Then multiply: 0.15 × 240 = 36."
-                },
-                {
-                    "type": "text",
-                    "text": "To find 15% of 240, I'll multiply 240 by 0.15:\n\n240 × 0.15 = 36\n\nTherefore, 15% of 240 is 36."
-                }
-            ]
-        }
-    ]
-}
-```
-
-### Advanced Options
-
-The `thinking` section supports an optional `closed` parameter:
-
-```json
-{
-    "type": "thinking",
-    "thinking": "Internal reasoning here...",
-    "closed": true  // Default: true, controls adding the closing [/THINK] tag
-}
-```

examples/ministral3/think/ministral3-3b-think-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-
-# 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
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: Nanobit/text-think-2k-test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: 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
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/vision/README.md

@@ -1,57 +0,0 @@
-# Ministral3 2512 Vision Fine-tuning
-
-This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collections/mistralai/ministral-3) with vision capabilities using Axolotl.
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Installed Axolotl from source (see [main README](../README.md#getting-started))
-
-## Getting started
-
-1. Install the required vision lib:
-    ```bash
-    pip install 'mistral-common[opencv]==1.8.6'
-    ```
-
-2. Download the example dataset image:
-   ```bash
-   wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
-   ```
-
-3. Run the fine-tuning:
-   ```bash
-   axolotl train examples/ministral3/vision/ministral3-3b-vision-qlora.yml
-   ```
-
-WARNING: The loss and grad norm will be much higher than normal at first. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
-
-### Tips
-
-Key differences from text-only model:
-- Multi-modal dataset format required
-- Sample packing not supported
-
-## Dataset Format
-
-The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
-
-Example:
-```json
-{
-    "messages": [
-        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
-        {"role": "user", "content": [
-            { "type": "text", "text": "What's in this image?"},
-            {"type": "image", "path": "path/to/image.jpg" }
-        ]},
-        {"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
-    ],
-}
-```
-
-## Limitations
-
-- Sample Packing is not supported for multi-modality training currently.

examples/ministral3/vision/ministral3-3b-vision-qlora.yml

@@ -1,64 +0,0 @@
-base_model: mistralai/Ministral-3-3B-Reasoning-2512
-processor_type: AutoProcessor
-
-# Enable to use mistral-common tokenizer
-tokenizer_use_mistral_common: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-# sample dataset below requires downloading image in advance
-# wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
-datasets:
-  - path: Nanobit/text-vision-2k-test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/mistral/mistral-small/README.md

@@ -1,51 +0,0 @@
-# Mistral Small 3.1/3.2 Fine-tuning
-
-This guide covers fine-tuning [Mistral Small 3.1](mistralai/Mistral-Small-3.1-24B-Instruct-2503) and [Mistral Small 3.2](mistralai/Mistral-Small-3.2-24B-Instruct-2506) with vision capabilities using Axolotl.
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Installed Axolotl (see [Installation docs](https://docs.axolotl.ai/docs/installation.html))
-
-## Getting Started
-
-1. Install the required vision lib:
-    ```bash
-    pip install 'mistral-common[opencv]==1.8.5'
-    ```
-
-2. Download the example dataset image:
-   ```bash
-   wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
-   ```
-
-3. Run the fine-tuning:
-   ```bash
-   axolotl train examples/mistral/mistral-small/mistral-small-3.1-24B-lora.yml
-   ```
-
-This config uses about 29.4 GiB VRAM.
-
-## Dataset Format
-
-The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
-
-Example:
-```json
-{
-    "messages": [
-        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
-        {"role": "user", "content": [
-            { "type": "text", "text": "What's in this image?"},
-            {"type": "image", "path": "path/to/image.jpg" }
-        ]},
-        {"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
-    ],
-}
-```
-
-## Limitations
-
-- Sample Packing is not supported for multi-modality training currently.

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

@@ -39,7 +39,7 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
-micro_batch_size: 2
+micro_batch_size: 1
 num_epochs: 1
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine

examples/olmo3/README.md

@@ -1,38 +0,0 @@
-# Finetune Allenai's Olmo 3 with Axolotl
-
-[Olmo 3](https://huggingface.co/collections/allenai/olmo-3) are a family of 7B and 32B models open source models trained by The Allen Institute for Artificial Intelligence.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/olmo3/olmo3-7b-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- The example config can be re-used for Olmo and Olmo 2.
-- 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 follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Olmo 3 Blog](https://allenai.org/blog/olmo3)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/olmo3/olmo3-7b-qlora.yaml

@@ -1,64 +0,0 @@
-base_model: allenai/Olmo-3-7B-Instruct-SFT
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 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
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Gemma3-12B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: google/gemma-3-12b-it
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/out_gemma/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 4e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Gemma3-12B_qat.yml

@@ -1,72 +0,0 @@
-base_model: google/gemma-3-12b-it
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/qat_out_gemma/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 4e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-12B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Math finetuning configuration for Gemma3-12B
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_gemma/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 3e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-12B_qat.yml

@@ -1,72 +0,0 @@
-base_model: google/gemma-3-12b-it
-# Math finetuning configuration for Gemma3-12B
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_gemma/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 3e-5
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-27B_baseline.yml

@@ -1,68 +0,0 @@
-base_model: google/gemma-3-27b-it
-# Math finetuning configuration for Gemma3-27B
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_gemma27/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Gemma3-27B_qat.yml

@@ -1,73 +0,0 @@
-base_model: google/gemma-3-27b-it
-# Math finetuning configuration for Gemma3-27B
-# 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
-seed: 42
-chat_template: gemma3
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_gemma27/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Qwen2.5-72B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Math finetuning configuration for Qwen2.5-72B (non-instruct)
-# 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
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/out_math_72b/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Math-Qwen2.5-72B_qat.yml

@@ -1,72 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Math finetuning configuration for Qwen2.5-72B (non-instruct)
-# 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
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: AI-MO/NuminaMath-CoT
-    type: chat_template
-
-output_dir: ./outputs/qat_out_math_72b/
-
-sequence_len: 4096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-num_epochs: 1
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-6
-eta_min: 7e-7
-
-bf16: true
-tf32: true
-
-resume_from_checkpoint:
-logging_steps: 1
-
-# evals_per_epoch: 1
-saves_per_epoch: 1
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Qwen2.5-72B_baseline.yml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Alpaca finetuning configuration for Qwen2.5-72B
-# 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
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/out_qwen72b/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-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_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qat_nvfp4/Qwen2.5-72B_qat.yml

@@ -1,72 +0,0 @@
-base_model: Qwen/Qwen2.5-72B
-# Alpaca finetuning configuration for Qwen2.5-72B
-# 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
-seed: 42
-chat_template: qwen_25
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-
-output_dir: ./outputs/qat_out_qwen72b/
-
-sequence_len: 8096
-sample_packing: true
-flash_attention: true
-
-qat:
-  activation_dtype: nvfp4
-  weight_dtype: nvfp4
-  group_size: 16 # only group_size of 16 is supported with nvfp4
-
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 16
-
-num_epochs: 1
-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_ratio: 0.1
-weight_decay: 0.0
-fsdp_version: 2
-
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  sharding_strategy: FULL_SHARD
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-special_tokens:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qwen3-next/README.md

@@ -15,8 +15,8 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 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]'
+uv sync
+uv pip install flash-attn --no-build-isolation
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh
@@ -24,12 +24,12 @@ python scripts/cutcrossentropy_install.py | sh
 
 2. Install Qwen3-Next transformers commit
 ```bash
-pip3 uninstall -y transformers && pip3 install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
+uv pip uninstall -y transformers && uv pip install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
 ```
 
 3. Install FLA for improved performance
 ```bash
-pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
+uv pip uninstall -y causal-conv1d && uv pip install flash-linear-attention==0.3.2
 ```
 
 4. Run the finetuning example:

examples/qwen3/README.md

@@ -1,46 +0,0 @@
-# Finetune Qwen3 with Axolotl
-
-[Qwen3](https://huggingface.co/collections/Qwen/qwen3) are a family of open source models trained by Alibaba.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/qwen3/32b-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### Chat template masking a few tokens off
-
-If you notice that the `chat_template` masking for assistant prompts are off by a few tokens, please ensure that you are adding the below to the yaml.
-
-```yaml
-chat_template: qwen3
-```
-
-### TIPS
-
-- For inference, please check the official model card as it depends on your reasoning mode.
-- 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 follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Qwen3 Blog](https://qwenlm.github.io/blog/qwen3/)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/seed-oss/README.md

@@ -6,17 +6,21 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ## Getting started
 
-1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Seed-OSS 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 pip:
-    ```bash
-    # Ensure you have a compatible version of Pytorch installed
-    pip3 install packaging setuptools wheel ninja
-    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    Here is an example of how to install from main for pip:
 
-    # Install Cut Cross Entropy
-    python scripts/cutcrossentropy_install.py | sh
-    ```
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+uv sync --extra deepspeed
+uv pip install flash-attn --no-build-isolation
+
+# Install Cut Cross Entropy
+python scripts/cutcrossentropy_install.py | sh
+```
 
 2. Run the finetuning example:
 
@@ -37,7 +41,9 @@ Let us know how it goes. Happy finetuning! 🚀
 
 ## Optimization Guides
 
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+- [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)
 
 ## Related Resources
 

examples/smolvlm2/README.md

@@ -13,14 +13,19 @@ This guide shows how to fine-tune SmolVLM2 models with Axolotl.
     Here is an example of how to install from pip:
     ```bash
     # Ensure you have a compatible version of Pytorch installed
-    pip3 install packaging setuptools wheel ninja
-    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    # Option A: manage dependencies in your project
+    uv add 'axolotl>=0.12.0'
+    uv pip install flash-attn --no-build-isolation
+
+    # Option B: quick install
+    uv pip install 'axolotl>=0.12.0'
+    uv pip install flash-attn --no-build-isolation
     ```
 
 2. Install an extra dependency:
 
     ```bash
-    pip3 install num2words==0.5.14
+    uv pip install num2words==0.5.14
     ```
 
 3.  Run the finetuning example:
@@ -37,7 +42,9 @@ This guide shows how to fine-tune SmolVLM2 models with Axolotl.
 
 ## Optimization Guides
 
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
 
 ## Related Resources
 

examples/trinity/README.md

@@ -1,38 +0,0 @@
-# Finetune ArceeAI's Trinity with Axolotl
-
-[Trinity](https://huggingface.co/collections/arcee-ai/trinity) is a family of open weight MoE models trained by Arcee.ai.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
-
-2. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
-    ```
-
-This config uses about 24.9 GiB VRAM.
-
-Let us know how it goes. Happy finetuning! 🚀
-
-### TIPS
-
-- For inference, the official Arcee.ai team recommends `top_p: 0.75`, `temperature: 0.15`, `top_k: 50`, and `min_p: 0.06`.
-- 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 follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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

@@ -1,67 +0,0 @@
-base_model: arcee-ai/Trinity-Nano-Preview
-trust_remote_code: true
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# CCE - N/A as of now
-# plugins:
-#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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
-
-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  # Not supported
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/voxtral/README.md

@@ -12,16 +12,21 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+# Option A: manage dependencies in your project
+uv add 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
+
+# Option B: quick install
+uv pip install 'axolotl>=0.12.0'
+uv pip install flash-attn --no-build-isolation
 ```
 
 2. Please install the below.
 
 ```bash
 # audio
-pip3 install librosa==0.11.0
-pip3 install 'mistral_common[audio]==1.8.3'
+uv pip install librosa==0.11.0
+uv pip install 'mistral_common[audio]==1.8.3'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
 python scripts/cutcrossentropy_install.py | sh

examples/voxtral/voxtral-mini-audio-qlora.yml

@@ -1,5 +1,5 @@
 base_model: mistralai/Voxtral-Mini-3B-2507
-processor_type: VoxtralProcessor
+processor_type: AutoProcessor
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name

pyproject.toml

@@ -1,14 +1,131 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
 build-backend = "setuptools.build_meta"
 
 [project]
 name = "axolotl"
-dynamic = ["version", "dependencies", "optional-dependencies"]
+dynamic = ["version"]
 description = "LLM Trainer"
 readme = "README.md"
-requires-python = ">=3.10"
-# license = "Apache-2.0"
+requires-python = ">=3.10,<3.13"
+license = {text = "Apache-2.0"}
+authors = [
+    {name = "Axolotl AI"},
+]
+maintainers = [
+    {name = "Axolotl AI"},
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+dependencies = [
+    "torch>=2.6.0",
+    "packaging>=23.2",
+    "huggingface_hub>=0.33.0",
+    "peft==0.17.0",
+    "transformers==4.56.1",
+    "tokenizers>=0.21.1",
+    "accelerate==1.10.1",
+    "datasets==4.0.0",
+    "trl==0.23.0",
+    "hf_xet==1.1.5",
+    "kernels==0.9.0",
+    "trackio",
+    "optimum==1.16.2",
+    "hf_transfer",
+    "sentencepiece",
+    "gradio==5.41.1",
+    "modal==1.0.2",
+    "pydantic>=2.10.6",
+    "addict",
+    "fire",
+    "PyYAML>=6.0",
+    "requests",
+    "wandb",
+    "einops",
+    "colorama",
+    "numba",
+    "numpy>=1.24.4,<3.0",
+    "evaluate==0.4.1",
+    "scipy",
+    "scikit-learn>=1.7.0",
+    "nvidia-ml-py==12.560.30",
+    "art",
+    "tensorboard",
+    "python-dotenv==1.0.1",
+    "s3fs>=2024.5.0",
+    "gcsfs>=2024.5.0",
+    "adlfs>=2024.5.0",
+    "ocifs==1.3.2",
+    "zstandard>=0.23.0",
+    "fastcore",
+    "lm_eval==0.4.7",
+    "langdetect==1.0.9",
+    "immutabledict==4.2.0",
+    "antlr4-python3-runtime==4.13.2",
+    "schedulefree==1.4.1",
+    "mistral-common==1.8.5",
+
+    # Axolotl contribs
+    "axolotl-contribs-lgpl @ git+https://github.com/axolotl-ai-cloud/axolotl-contribs-lgpl.git@numpy",
+    "axolotl-contribs-mit==0.0.5",
+
+    # Platform-specific dependencies (Linux by default, excluded on macOS)
+    "triton>=3.0.0 ; sys_platform != 'darwin'",
+    "xformers>=0.0.28 ; sys_platform != 'darwin'",
+    "autoawq==0.2.7.post3 ; sys_platform != 'darwin'",
+    "liger-kernel==0.6.1 ; sys_platform != 'darwin'",
+    "torchao==0.13.0 ; sys_platform != 'darwin'",
+    "bitsandbytes==0.47.0 ; sys_platform != 'darwin'",
+    "deepspeed>=0.17.5 ; sys_platform != 'darwin'",
+    "deepspeed-kernels ; sys_platform != 'darwin'",
+]
+
+[project.optional-dependencies]
+ring-flash-attn = [
+    "ring-flash-attn>=0.1.7",
+    "yunchang==0.6.0",
+]
+mamba-ssm = ["mamba-ssm>=2.2.0", "causal_conv1d>=1.4.0",]
+gptqmodel = ["gptqmodel>=4.0.0"]
+mlflow = ["mlflow"]
+galore = ["galore_torch"]
+apollo = ["apollo-torch"]
+optimizers = [
+    "galore_torch",
+    "apollo-torch",
+    "lomo-optim==0.1.1",
+    "torch-optimi==0.2.1",
+    "came_pytorch==0.1.3",
+]
+ray = ["ray[train]"]
+vllm = ["vllm>=0.10.0"]
+llmcompressor = ["llmcompressor>=0.5.1"]
+fbgemm-gpu = ["fbgemm-gpu-genai>=1.2.0"]
+dev = [
+    "pytest",
+    "pytest-cov",
+    "pytest-retry",
+    "pytest-sugar",
+    "pytest-xdist",
+    "codecov",
+    "codecov-cli",
+    "tbparse",
+    "ruff",
+    "mypy",
+    "pre-commit",
+    "types-requests",
+    "quartodoc",
+    "jupyter",
+    "blobfile",
+    "tiktoken",
+]
 
 [project.scripts]
 axolotl = "axolotl.cli.main:main"
@@ -17,15 +134,20 @@ axolotl = "axolotl.cli.main:main"
 Homepage = "https://axolotl.ai/"
 Documentation = "https://docs.axolotl.ai/"
 Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
-
-[tool.setuptools_scm]
+Issues = "https://github.com/axolotl-ai-cloud/axolotl/issues"
 
 [tool.setuptools]
-py-modules = ["setuptools_axolotl_dynamic_dependencies"]
+package-dir = {"" = "src"}
 include-package-data = true
 
-[tool.setuptools.cmdclass]
-build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.package-data]
+"*" = ["*.yaml", "*.yml", "*.json"]
+
+[tool.setuptools_scm]
+write_to = "src/axolotl/_version.py"
 
 [tool.ruff]
 line-length = 88
@@ -57,3 +179,60 @@ indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
 docstring-code-format = false
+
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = true
+warn_unused_configs = true
+ignore_missing_imports = true
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+addopts = "-v --tb=short"
+
+# UV specific configuration
+[tool.uv]
+prerelease = "allow"
+default-groups = ["default"]
+conflicts = [
+    [
+        { group = "default" },
+        { extra = "vllm" },
+    ],
+]
+
+[dependency-groups]
+default = ["torch>=2.6.0"]
+dev = [
+    "pytest",
+    "pytest-cov",
+    "pytest-retry",
+    "pytest-sugar",
+    "pytest-xdist",
+    "codecov",
+    "codecov-cli",
+    "tbparse",
+    "ruff",
+    "mypy",
+    "pre-commit",
+    "types-requests",
+    "quartodoc",
+    "jupyter",
+    "blobfile",
+    "tiktoken",
+]
+
+[[tool.uv.index]]
+name = "autogptq"
+url = "https://huggingface.github.io/autogptq-index/whl/"
+
+[tool.uv.extra-build-dependencies]
+mamba-ssm = ["torch", "causal_conv1d"]
+gptqmodel = [
+    { requirement = "torch", match-runtime = true },
+]
+autoawq = ["torch"]
+triton = ["torch"]
+bitsandbytes = ["torch"]
+grpclib = ["wheel"]

requirements-dev.txt

@@ -1,8 +0,0 @@
-black
-mypy
-pre-commit
-types-requests
-quartodoc
-jupyter
-blobfile
-tiktoken

requirements-tests.txt

@@ -1,8 +0,0 @@
-codecov
-codecov-cli
-pytest
-pytest-cov
-pytest-retry
-pytest-sugar
-pytest-xdist
-tbparse

requirements.txt

@@ -1,75 +0,0 @@
---extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-
-# START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.48.2
-triton>=3.0.0
-mamba-ssm==1.2.0.post1
-xformers>=0.0.23.post1
-liger-kernel==0.6.4
-# END section
-
-packaging==23.2
-
-huggingface_hub>=0.36.0
-peft>=0.18.0
-tokenizers>=0.22.1
-transformers==4.57.1
-accelerate==1.11.0
-datasets==4.4.1
-deepspeed>=0.17.0
-trl==0.25.0
-hf_xet==1.2.0
-kernels>=0.9.0
-trackio
-
-optimum==1.16.2
-hf_transfer
-sentencepiece
-gradio==5.49.1
-
-modal==1.0.2
-pydantic>=2.10.6
-addict
-fire
-PyYAML>=6.0
-requests
-wandb
-einops
-colorama
-numba>=0.61.2
-numpy>=2.2.6
-
-# qlora things
-evaluate==0.4.1
-scipy
-nvidia-ml-py==12.560.30
-art
-tensorboard
-python-dotenv==1.0.1
-
-# remote filesystems
-s3fs>=2024.5.0
-gcsfs>=2025.3.0
-adlfs>=2024.5.0
-ocifs==1.3.2
-
-zstandard==0.22.0
-fastcore
-
-# lm eval harness
-lm_eval==0.4.7
-langdetect==1.0.9
-immutabledict==4.2.0
-antlr4-python3-runtime==4.13.2
-
-torchao==0.13.0
-openenv-core==0.1.0
-schedulefree==1.4.1
-
-axolotl-contribs-lgpl==0.0.7
-axolotl-contribs-mit==0.0.5
-
-# telemetry
-posthog==6.7.11
-
-mistral-common==1.8.6

scripts/cutcrossentropy_install.py

@@ -1,33 +1,24 @@
-"""Script to output the correct installation command for cut-cross-entropy."""
+"""Print the pip command to install Axolotl's cut_cross_entropy fork."""
+
+from __future__ import annotations
 
-import importlib.util
 import sys
+from shlex import quote
 
 try:
     import torch
-except ImportError as exc:
+except ImportError as exc:  # pragma: no cover
     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
-if v < V("2.4.0"):
+if V(torch.__version__.split("+")[0]) < V("2.6.0"):
     print("")
     sys.exit(0)
 
-cce_spec = importlib.util.find_spec("cut_cross_entropy")
-
-UNINSTALL_PREFIX = ""
-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 ""
-
+python_exe = quote(sys.executable)
 print(
-    UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"'
+    f"{python_exe} -m pip install "
+    '"cut-cross-entropy[transformers] '
+    '@ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28"'
 )

scripts/unsloth_install.py

@@ -1,40 +1,48 @@
-# noqa
+"""Emit the install commands for Unsloth without altering torch."""
+
+from __future__ import annotations
+
+import shutil
 import sys
+from shlex import quote
 
 try:
     import torch
-except ImportError as error:
-    raise ImportError("Install torch via `pip install torch`") from error
+except ImportError as exc:  # pragma: no cover
+    raise ImportError("Install torch via `pip install torch`") from exc
+
 from packaging.version import Version as V
 
-use_uv = "--uv" in sys.argv[1:]
+MIN_TORCH = V("2.6.0")
 
-v = V(torch.__version__)
-cuda = str(torch.version.cuda)
-try:
-    is_ampere = torch.cuda.get_device_capability()[0] >= 8
-except RuntimeError:
-    is_ampere = False
-if cuda != "12.1" and cuda != "11.8" and cuda != "12.4":
-    raise RuntimeError(f"CUDA = {cuda} not supported!")
-if v <= V("2.1.0"):
-    raise RuntimeError(f"Torch = {v} too old!")
-elif v <= V("2.1.1"):
-    x = "cu{}{}-torch211"
-elif v <= V("2.1.2"):
-    x = "cu{}{}-torch212"
-elif v < V("2.3.0"):
-    x = "cu{}{}-torch220"
-elif v < V("2.4.0"):
-    x = "cu{}{}-torch230"
-elif v < V("2.5.0"):
-    x = "cu{}{}-torch240"
-elif v < V("2.6.0"):
-    x = "cu{}{}-torch250"
+if V(torch.__version__.split("+")[0]) < MIN_TORCH:
+    raise RuntimeError(
+        f"Torch {torch.__version__} detected, but Unsloth requires >= {MIN_TORCH}."
+    )
+
+USE_UV_FLAG = "--uv" in sys.argv[1:]
+USE_PIP_FLAG = "--pip" in sys.argv[1:]
+
+if USE_UV_FLAG and USE_PIP_FLAG:
+    raise SystemExit("Specify only one of --uv or --pip")
+
+if USE_PIP_FLAG:
+    use_uv = False
+elif USE_UV_FLAG:
+    use_uv = True
 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'{uv_prefix}pip install unsloth-zoo==2024.12.1 && {uv_prefix}pip install --no-deps "unsloth[{x}]==2024.12.4"'
-)
+    use_uv = shutil.which("uv") is not None
+
+python_exe = quote(sys.executable or shutil.which("python3") or "python")
+
+if use_uv:
+    installer = "uv pip install --system --no-deps"
+else:
+    installer = f"{python_exe} -m pip install --no-deps"
+
+commands = [
+    f"{installer} unsloth-zoo==2025.9.12",
+    f'{installer} "unsloth[huggingface]==2025.9.9"',
+]
+
+print(" && ".join(commands))