drop valueerror as this was from when 4bit required gptq

.github/workflows/base.yml

@@ -1,16 +1,6 @@
 name: ci-cd-base
 
 on:
-  push:
-    branches:
-      - "main"
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
-  pull_request:
-    paths:
-      - 'Dockerfile-base'
-      - '.github/workflows/base.yml'
   workflow_dispatch:
 
 jobs:
@@ -34,41 +24,27 @@ jobs:
             python_version: "3.11"
             pytorch: 2.3.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.10"
-            pytorch: 2.4.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.4.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Docker metadata
         id: metadata
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v3
         with:
-          images: |
+          images: winglian/axolotl-base
-            winglian/axolotl-base
-            axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v4
         with:

.github/workflows/docs.yml

@@ -17,7 +17,7 @@ jobs:
         - name: Set up Quarto
           uses: quarto-dev/quarto-actions/setup@v2
         - name: Setup Python
-          uses: actions/setup-python@v5
+          uses: actions/setup-python@v3
           with:
             python-version: '3.10'
         - name: install dependencies

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - "*.[q]md"
+       - "*.md"
        - "examples/**/*.y[a]?ml"
   workflow_dispatch:
 
@@ -15,9 +15,9 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0

.github/workflows/main.yml

@@ -4,13 +4,11 @@ on:
   push:
     branches:
       - "main"
-    tags:
-      - "v*"
   workflow_dispatch:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -29,12 +27,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.4.0
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -44,12 +37,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
+          images: winglian/axolotl
-            winglian/axolotl
-            axolotlai/axolotl
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
       - name: Login to Docker Hub
@@ -63,7 +51,7 @@ jobs:
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             CUDA=${{ matrix.cuda }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             AXOLOTL_ARGS=${{ matrix.axolotl_args }}
@@ -77,7 +65,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -96,12 +84,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.4.0
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -111,25 +94,20 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
+          images: winglian/axolotl-cloud
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud
           push: ${{ github.event_name != 'pull_request' }}
@@ -140,7 +118,7 @@ jobs:
 
   build-axolotl-cloud-no-tmux:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -158,25 +136,20 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
+          images: winglian/axolotl-cloud-term
-            winglian/axolotl-cloud-term
-            axolotlai/axolotl-cloud-term
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:
           context: .
           build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
           file: ./docker/Dockerfile-cloud-no-tmux
           push: ${{ github.event_name != 'pull_request' }}

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

@@ -1,21 +1,13 @@
 name: docker-multigpu-tests-biweekly
 
 on:
-  pull_request:
-    paths:
-      - 'tests/e2e/multigpu/*.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
 
-# Cancel jobs on the same ref if a new one is triggered
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
-
 jobs:
   test-axolotl-multigpu:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -26,17 +18,10 @@ jobs:
             pytorch: 2.3.1
             axolotl_extras:
             num_gpus: 2
-          - cuda: 124
+          - cuda: 121
-            cuda_version: 12.4.1
+            cuda_version: 12.1.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.3.1
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
             axolotl_extras:
             num_gpus: 2
             nightly_build: "true"

.github/workflows/nightlies.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -26,12 +26,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.4.0
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -41,9 +36,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
+          images: winglian/axolotl
-            winglian/axolotl
-            axolotlai/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Set up Docker Buildx
@@ -71,7 +64,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -90,12 +83,7 @@ jobs:
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.4.0
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -105,9 +93,7 @@ jobs:
         id: metadata
         uses: docker/metadata-action@v5
         with:
-          images: |
+          images: winglian/axolotl-cloud
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
       - name: Login to Docker Hub
@@ -116,7 +102,7 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
       - name: Build
         uses: docker/build-push-action@v5
         with:

.github/workflows/pypi.yml

@@ -3,27 +3,12 @@ name: publish pypi
 on:
   push:
     tags:
-      - 'v*'
+      - '*'
-  workflow_dispatch:
 
 jobs:
-  setup_release:
-    name: Create Release
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Create release
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: gh release create "$GITHUB_REF_NAME" --generate-notes
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
-    needs: [setup_release]
     environment:
       name: pypi
       url: https://pypi.org/p/axolotl
@@ -31,18 +16,18 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
       - name: Install dependencies
         run: |
           pip3 install wheel packaging
-          pip3 install --no-build-isolation -e .
+          pip3 install -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          pip3 install -r requirements-tests.txt
 
       - name: Extract tag name
         id: tag
@@ -52,9 +37,9 @@ jobs:
         run: |
           sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
-      - name: Build a source dist
+      - name: Build a binary wheel
         run: |
-          python setup.py sdist
+          python setup.py sdist bdist_wheel
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests-nightly.yml

@@ -9,12 +9,12 @@ jobs:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -23,65 +23,37 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
-        exclude:
-          - python_version: "3.10"
-            pytorch_version: "2.4.1"
-          - python_version: "3.10"
-            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
-
       - name: Update requirements.txt
         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#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt
-          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
-          pip3 install --no-build-isolation -U -e .
+          pip3 install -U -e .
-          python scripts/unsloth_install.py | sh
+          pip3 install -r requirements-tests.txt
-          python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
 
       - name: Run tests
         run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest --ignore=tests/e2e/ tests/
-          pytest tests/patched/
 
       - name: cleanup pip cache
         run: |
@@ -105,17 +77,17 @@ jobs:
             num_gpus: 1
             axolotl_extras: mamba-ssm
             nightly_build: "true"
-          - cuda: 124
+          - cuda: 121
-            cuda_version: 12.4.1
+            cuda_version: 12.1.1
             python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.3.1
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: mamba-ssm
             nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.4.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -8,35 +8,24 @@ on:
       - '**.py'
       - 'requirements.txt'
       - '.github/workflows/*.yml'
-      - 'requirements-tests.txt'
-      - 'cicd/cicd.sh'
-      - 'cicd/Dockerfile.jinja'
   pull_request:
       paths:
        - '**.py'
        - 'requirements.txt'
        - '.github/workflows/*.yml'
-       - 'requirements-tests.txt'
-       - 'cicd/cicd.sh'
-       - 'cicd/Dockerfile.jinja'
   workflow_dispatch:
 
-# Cancel jobs on the same ref if a new one is triggered
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
-
 jobs:
   pre-commit:
     name: pre-commit
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
           cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         env:
           SKIP: no-commit-to-branch
 
@@ -45,163 +34,41 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 2
       matrix:
         python_version: ["3.10", "3.11"]
-        pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
-        exclude:
-          - python_version: "3.10"
-            pytorch_version: "2.4.1"
-          - python_version: "3.10"
-            pytorch_version: "2.5.1"
     timeout-minutes: 20
 
     steps:
       - name: Check out repository code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Setup Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
-
       - name: Install dependencies
         run: |
-          pip3 show torch
+          pip3 install --upgrade pip
-          pip3 install --no-build-isolation -U -e .
+          pip3 install --upgrade packaging
-          python scripts/unsloth_install.py | sh
+          pip3 install -U -e .
-          python scripts/cutcrossentropy_install.py | sh
+          pip3 install -r requirements-tests.txt
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest --ignore=tests/e2e/ tests/
-          pytest -v tests/patched/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
-  pytest-sdist:
-    name: PyTest from Source Dist
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      max-parallel: 1
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1"]
-    timeout-minutes: 20
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools setuptools_scm build wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
-
-      - name: Install dependencies
-        run: |
-          pip3 show torch
-          python -m build --no-isolation --sdist
-          pip3 install --no-build-isolation dist/axolotl*.tar.gz
-          python scripts/unsloth_install.py | sh
-          python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
-      - name: Run tests
-        run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest -v tests/patched/
-
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
-  docker-e2e-tests-1st:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, pytest-sdist]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.63.64 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.tests
-
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
-    timeout-minutes: 90
+    timeout-minutes: 60
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    needs: [pre-commit, pytest]
 
     strategy:
       fail-fast: false
@@ -213,10 +80,16 @@ jobs:
             pytorch: 2.3.1
             num_gpus: 1
             axolotl_extras: mamba-ssm
+          - cuda: 121
+            cuda_version: 12.1.1
+            python_version: "3.11"
+            pytorch: 2.3.1
+            num_gpus: 1
+            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.4.0
             num_gpus: 1
             axolotl_extras:
     steps:

.gitignore

@@ -182,6 +182,3 @@ submit.sh
 
 typings/
 out/
-
-# vim
-*.swp

.isort.cfg

@@ -1,3 +1,3 @@
 [settings]
 profile=black
-known_third_party=wandb,comet_ml
+known_third_party=wandb

.mypy.ini

@@ -11,9 +11,6 @@ ignore_errors = True
 [mypy-axolotl.models.mixtral.*]
 ignore_errors = True
 
-[mypy-axolotl.integrations.liger.models.*]
-ignore_errors = True
-
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

MANIFEST.in

@@ -1,5 +0,0 @@
-include requirements.txt
-include README.md
-include LICENSE
-include src/setuptools_axolotl_dynamic_dependencies.py
-recursive-include axolotl *.py

README.md

@@ -1,25 +1,8 @@
-<p align="center">
+# Axolotl
-    <picture>
-        <source media="(prefers-color-scheme: dark)" srcset="image/axolotl_logo_digital_white.svg">
-        <source media="(prefers-color-scheme: light)" srcset="image/axolotl_logo_digital_black.svg">
-        <img alt="Axolotl" src="image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
-    </picture>
-</p>
 
-<p align="center">
+![tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg)
-    <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
+![tests-nightly](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg)
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
+![multigpu-semi-weekly tests](https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg)
-    <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
-    <br/>
-    <a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors"><img src="https://img.shields.io/github/contributors-anon/axolotl-ai-cloud/axolotl?color=yellow&style=flat-square" alt="contributors" style="height: 20px;"></a>
-    <img src="https://img.shields.io/github/stars/axolotl-ai-cloud/axolotl" alt="GitHub Repo stars">
-    <br/>
-    <a href="https://discord.com/invite/HhrNrHJPRb"><img src="https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord" alt="discord" style="height: 20px;"></a>
-    <a href="https://twitter.com/axolotl_ai"><img src="https://img.shields.io/twitter/follow/axolotl_ai?style=social" alt="twitter" style="height: 20px;"></a>
-    <br/>
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
-</p>
 
 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
 
@@ -28,10 +11,10 @@ Features:
 - Supports fullfinetune, lora, qlora, relora, and gptq
 - Customize configurations using a simple yaml file or CLI overwrite
 - Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, [liger kernel](https://github.com/linkedin/Liger-Kernel), rope scaling, and multipacking
+- Integrated with xformer, flash attention, rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb, mlflow or Comet
+- Log results and optionally checkpoints to wandb or mlflow
 - And more!
 
 <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
@@ -45,13 +28,9 @@ Features:
 ## Table of Contents
 - [Axolotl](#axolotl)
   - [Table of Contents](#table-of-contents)
-  - [Quickstart ⚡](#quickstart-)
-    - [Edge Builds](#edge-builds-)
-    - [Axolotl CLI Usage](#axolotl-cli-usage)
-  - [Badge ❤🏷️](#badge-️)
-  - [Contributing 🤝](#contributing-)
-  - [Sponsors 🤝❤](#sponsors-)
   - [Axolotl supports](#axolotl-supports)
+  - [Quickstart ⚡](#quickstart-)
+    - [Usage](#usage)
   - [Advanced Setup](#advanced-setup)
     - [Environment](#environment)
       - [Docker](#docker)
@@ -76,19 +55,26 @@ Features:
         - [FSDP + QLoRA](#fsdp--qlora)
         - [Weights \& Biases Logging](#weights--biases-logging)
         - [Special Tokens](#special-tokens)
-      - [Liger Kernel](#liger-kernel)
     - [Inference Playground](#inference-playground)
     - [Merge LORA to base](#merge-lora-to-base)
   - [Common Errors 🧰](#common-errors-)
     - [Tokenization Mismatch b/w Inference \& Training](#tokenization-mismatch-bw-inference--training)
   - [Debugging Axolotl](#debugging-axolotl)
   - [Need help? 🙋](#need-help-)
+  - [Badge ❤🏷️](#badge-️)
+  - [Community Showcase](#community-showcase)
+  - [Contributing 🤝](#contributing-)
+  - [Sponsors 🤝❤](#sponsors-)
+      - [💎 Diamond Sponsors - Contact directly](#-diamond-sponsors---contact-directly)
+      - [🥇 Gold Sponsors - $5000/mo](#-gold-sponsors---5000mo)
+      - [🥈 Silver Sponsors - $1000/mo](#-silver-sponsors---1000mo)
+      - [🥉 Bronze Sponsors - $500/mo](#-bronze-sponsors---500mo)
 
 </td>
 <td>
 
 <div align="center">
-  <img src="image/axolotl_symbol_digital_white.svg" alt="axolotl" width="160">
+  <img src="image/axolotl.png" alt="axolotl" width="160">
   <div>
     <p>
       <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
@@ -105,148 +91,6 @@ Features:
 </tr>
 </table>
 
-## Quickstart ⚡
-
-Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
-
-**Requirements**: *Nvidia* GPU (Ampere architecture or newer for `bf16` and Flash Attention) or *AMD* GPU, Python >=3.10 and PyTorch >=2.3.1.
-
-```bash
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
-
-# download examples and optionally deepspeed configs to the local path
-axolotl fetch examples
-axolotl fetch deepspeed_configs  # OPTIONAL
-
-# finetune using lora
-axolotl train examples/llama-3/lora-1b.yml
-```
-
-### Edge Builds 🏎️
-
-If you're looking for the latest features and updates between releases, you'll need to install
-from source.
-
-```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-pip3 install packaging ninja
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
-```
-
-### Axolotl CLI Usage
-We now support a new, more streamlined CLI using [click](https://click.palletsprojects.com/en/stable/).
-
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/llama-3/lora-1b.yml
-
-# finetune lora
-axolotl train examples/llama-3/lora-1b.yml
-
-# inference
-axolotl inference examples/llama-3/lora-1b.yml \
-    --lora-model-dir="./outputs/lora-out"
-
-# gradio
-axolotl inference examples/llama-3/lora-1b.yml \
-    --lora-model-dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
-```
-
-We've also added a new command for fetching `examples` and `deepspeed_configs` to your
-local machine. This will come in handy when installing `axolotl` from PyPI.
-
-```bash
-# Fetch example YAML files (stores in "examples/" folder)
-axolotl fetch examples
-
-# Fetch deepspeed config files (stores in "deepspeed_configs/" folder)
-axolotl fetch deepspeed_configs
-
-# Optionally, specify a destination folder
-axolotl fetch examples --dest path/to/folder
-```
-
-### Legacy Usage
-<details>
-
-<summary>Click to Expand</summary>
-
-While the Axolotl CLI is the preferred method for interacting with axolotl, we
-still support the legacy `-m axolotl.cli.*` usage.
-
-```bash
-# preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/llama-3/lora-1b.yml
-
-# finetune lora
-accelerate launch -m axolotl.cli.train examples/llama-3/lora-1b.yml
-
-# inference
-accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
-    --lora_model_dir="./outputs/lora-out"
-
-# gradio
-accelerate launch -m axolotl.cli.inference examples/llama-3/lora-1b.yml \
-    --lora_model_dir="./outputs/lora-out" --gradio
-
-# remote yaml files - the yaml config can be hosted on a public URL
-# Note: the yaml config must directly link to the **raw** yaml
-accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
-```
-
-</details>
-
-## Badge ❤🏷️
-
-Building something cool with Axolotl? Consider adding a badge to your model card.
-
-```markdown
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
-```
-
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
-
-## Sponsors 🤝❤
-
-If you love axolotl, consider sponsoring the project by reaching out directly to [wing@axolotl.ai](mailto:wing@axolotl.ai).
-
----
-
-- [Modal](https://modal.com/) Modal lets you run data/AI jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale, fine-tune LLM models, run protein folding simulations, and much more.
-
----
-
-## Contributing 🤝
-
-Please read the [contributing guide](./.github/CONTRIBUTING.md)
-
-Bugs? Please check the [open issues](https://github.com/axolotl-ai-cloud/axolotl/issues/bug) else create a new Issue.
-
-PRs are **greatly welcome**!
-
-Please run the quickstart instructions followed by the below to setup env:
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
-
-# test
-pytest tests/
-
-# optional: run against all files
-pre-commit run --all-files
-```
-
-Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
-
-<a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
-</a>
-
 ## Axolotl supports
 
 |             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
@@ -272,6 +116,41 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 ❌: not supported
 ❓: untested
 
+## Quickstart ⚡
+
+Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
+
+**Requirements**: Python >=3.10 and Pytorch >=2.1.1.
+
+```bash
+git clone https://github.com/axolotl-ai-cloud/axolotl
+cd axolotl
+
+pip3 install packaging ninja
+pip3 install -e '.[flash-attn,deepspeed]'
+```
+
+### Usage
+```bash
+# preprocess datasets - optional but recommended
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
+
+# finetune lora
+accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
+
+# inference
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out" --gradio
+
+# remote yaml files - the yaml config can be hosted on a public URL
+# Note: the yaml config must directly link to the **raw** yaml
+accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
+```
+
 ## Advanced Setup
 
 ### Environment
@@ -279,7 +158,7 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 #### Docker
 
   ```bash
-  docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-latest
   ```
 
   Or run on the current files for development:
@@ -298,7 +177,7 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
   A more powerful Docker command to run would be this:
 
   ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-latest
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-latest
   ```
 
   It additionally:
@@ -320,7 +199,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
   3. Install Axolotl along with python dependencies
         ```bash
         pip3 install packaging
-        pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+        pip3 install -e '.[flash-attn,deepspeed]'
         ```
   4. (Optional) Login to Huggingface to use gated models/datasets.
         ```bash
@@ -330,7 +209,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 
 #### Cloud GPU
 
-For cloud GPU providers that support docker images, use [`axolotlai/axolotl-cloud:main-latest`](https://hub.docker.com/r/axolotlai/axolotl-cloud/tags)
+For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud:main-latest`](https://hub.docker.com/r/winglian/axolotl-cloud/tags)
 
 - on Latitude.sh use this [direct link](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
 - on JarvisLabs.ai use this [direct link](https://jarvislabs.ai/templates/axolotl)
@@ -399,7 +278,7 @@ Please use WSL or Docker!
 
 Use the below instead of the install method in QuickStart.
 ```
-pip3 install --no-build-isolation -e '.'
+pip3 install -e '.'
 ```
 More info: [mac.md](/docs/mac.qmd)
 
@@ -439,7 +318,7 @@ Write a job description in YAML as below:
 # dstack.yaml
 type: task
 
-image: axolotlai/axolotl-cloud:main-latest
+image: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
 
 env:
   - HUGGING_FACE_HUB_TOKEN
@@ -478,7 +357,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
 - model
   ```yaml
-  base_model: ./llama-7b-hf/ # local or huggingface repo
+  base_model: ./llama-7b-hf # local or huggingface repo
   ```
   Note: The code will load the right architecture.
 
@@ -503,10 +382,11 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
         - typescript
       type: ... # unimplemented custom format
 
-      # chat_template https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/conversation.html#chat_template
+      # fastchat conversation
+      # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     - path: ...
-      type: chat_template
+      type: sharegpt
-      chat_template: chatml # defaults to tokenizer's chat_template
+      conversation: chatml # default: vicuna_v1.1
 
       # local
     - path: data.jsonl # or json
@@ -634,22 +514,6 @@ wandb_name:
 wandb_log_model:
 ```
 
-##### Comet Logging
-
-Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to wandb with `comet login`.
-
-- wandb options
-```yaml
-use_comet:
-comet_api_key:
-comet_workspace:
-comet_project_name:
-comet_experiment_key:
-comet_mode:
-comet_online:
-comet_experiment_config:
-```
-
 ##### Special Tokens
 
 It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
@@ -666,26 +530,6 @@ tokens: # these are delimiters
 
 When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
 
-##### Liger Kernel
-
-Liger Kernel: Efficient Triton Kernels for LLM Training
-
-https://github.com/linkedin/Liger-Kernel
-
-Liger (LinkedIn GPU Efficient Runtime) Kernel is a collection of Triton kernels designed specifically for LLM training.
-It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The Liger Kernel
-composes well and is compatible with both FSDP and Deepspeed.
-
-```yaml
-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
-```
-
 ### Inference Playground
 
 Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.
@@ -789,6 +633,86 @@ See [this debugging guide](docs/debugging.qmd) for tips on debugging Axolotl, al
 
 ## Need help? 🙋
 
-Join our [Discord server](https://discord.gg/HhrNrHJPRb) where our community members can help you.
+Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we our community members can help you.
 
-Need dedicated support? Please contact us at [✉️wing@axolotl.ai](ailto:wing@axolotl.ai) for dedicated support options.
+Need dedicated support? Please contact us at [✉️wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org) for dedicated support options.
+
+## Badge ❤🏷️
+
+Building something cool with Axolotl? Consider adding a badge to your model card.
+
+```markdown
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+```
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+
+## Community Showcase
+
+Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.
+
+Open Access AI Collective
+- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed)
+- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
+- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
+
+PocketDoc Labs
+- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
+
+## Contributing 🤝
+
+Please read the [contributing guide](./.github/CONTRIBUTING.md)
+
+Bugs? Please check the [open issues](https://github.com/axolotl-ai-cloud/axolotl/issues/bug) else create a new Issue.
+
+PRs are **greatly welcome**!
+
+Please run the quickstart instructions followed by the below to setup env:
+```bash
+pip3 install -r requirements-dev.txt -r requirements-tests.txt
+pre-commit install
+
+# test
+pytest tests/
+
+# optional: run against all files
+pre-commit run --all-files
+```
+
+Thanks to all of our contributors to date. Help drive open source AI progress forward by contributing to Axolotl.
+
+<a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=openaccess-ai-collective/axolotl" alt="contributor chart by https://contrib.rocks"/>
+</a>
+
+## Sponsors 🤝❤
+
+OpenAccess AI Collective is run by volunteer contributors such as [winglian](https://github.com/winglian),
+[NanoCode012](https://github.com/NanoCode012), [tmm1](https://github.com/tmm1),
+[mhenrichsen](https://github.com/mhenrichsen), [casper-hansen](https://github.com/casper-hansen),
+[hamelsmu](https://github.com/hamelsmu) and many more who help us accelerate forward by fixing bugs, answering
+community questions and implementing new features. Axolotl needs donations from sponsors for the compute needed to
+run our unit & integration tests, troubleshooting community issues, and providing bounties. If you love axolotl,
+consider sponsoring the project via [GitHub Sponsors](https://github.com/sponsors/OpenAccess-AI-Collective),
+[Ko-fi](https://ko-fi.com/axolotl_ai) or reach out directly to
+[wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org).
+
+---
+
+#### 💎 Diamond Sponsors - [Contact directly](mailto:wing@openaccessaicollective.org)
+
+---
+
+#### 🥇 Gold Sponsors - $5000/mo
+
+---
+
+#### 🥈 Silver Sponsors - $1000/mo
+
+---
+
+#### 🥉 Bronze Sponsors - $500/mo
+
+ - [JarvisLabs.ai](https://jarvislabs.ai)
+
+---

_quarto.yml

@@ -37,7 +37,6 @@ website:
             - docs/mac.qmd
             - docs/multi-node.qmd
             - docs/unsloth.qmd
-            - docs/amd_hpc.qmd
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
         - section: "Reference"

cicd/Dockerfile.jinja

@@ -1,9 +1,10 @@
-FROM axolotlai/axolotl-base:{{ BASE_TAG }}
+FROM winglian/axolotl-base:{{ BASE_TAG }}
 
 ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
 ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
 ENV CUDA="{{ CUDA }}"
+ENV BNB_CUDA_VERSION="{{ CUDA }}"
 ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
 ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
@@ -22,25 +23,22 @@ RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
+RUN pip install causal_conv1d
 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#^bitsandbytes.*#bitsandbytes @ git+https://github.com/bitsandbytes-foundation/bitsandbytes.git@main#' requirements.txt; \
-        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py | sh
-RUN 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 pip install -r requirements-tests.txt
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/cicd.sh

@@ -1,10 +1,6 @@
 #!/bin/bash
 set -e
 
-python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
+pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-
+pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
+pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ /workspace/axolotl/tests/e2e/
-# pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import App, Image
+from modal import Image, Stub
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -46,7 +46,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-app = App("Axolotl CI/CD", secrets=[])
+stub = Stub("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 2))
@@ -61,10 +61,10 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@app.function(
+@stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=45 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
 )
@@ -72,6 +72,6 @@ def cicd_pytest():
     run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
 
 
-@app.local_entrypoint()
+@stub.local_entrypoint()
 def main():
     cicd_pytest.remote()

cicd/multigpu.sh

@@ -2,4 +2,4 @@
 set -e
 
 # only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/
+pytest -n1 /workspace/axolotl/tests/e2e/multigpu/

cicd/tests.py

@@ -10,7 +10,7 @@ import tempfile
 import jinja2
 import modal
 from jinja2 import select_autoescape
-from modal import App, Image
+from modal import Image, Stub
 
 cicd_path = pathlib.Path(__file__).parent.resolve()
 
@@ -40,7 +40,6 @@ with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
 cicd_image = (
     Image.from_dockerfile(
         pathlib.Path(temp_dir) / "Dockerfile",
-        context_mount=None,
         force_build=True,
         gpu="A10G",
     )
@@ -48,7 +47,7 @@ cicd_image = (
     .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )
 
-app = App("Axolotl CI/CD", secrets=[])
+stub = Stub("Axolotl CI/CD", secrets=[])
 
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
@@ -63,10 +62,10 @@ def run_cmd(cmd: str, run_folder: str):
         exit(exit_code)  # pylint: disable=consider-using-sys-exit
 
 
-@app.function(
+@stub.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=45 * 60,
     cpu=8.0,
     memory=131072,
 )
@@ -74,6 +73,6 @@ def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
 
 
-@app.local_entrypoint()
+@stub.local_entrypoint()
 def main():
     cicd_pytest.remote()

deepspeed_configs/zero3_bf16.json

@@ -14,6 +14,15 @@
   "bf16": {
     "enabled": true
   },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -24,6 +24,15 @@
   "bf16": {
     "enabled": true
   },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -20,6 +20,15 @@
   "bf16": {
     "enabled": true
   },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

devtools/dev_sharegpt.yml

@@ -1,4 +1,4 @@
-# Example config for debugging the chat_template prompt format
+# Example config for debugging the sharegpt prompt format
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
@@ -7,8 +7,8 @@ load_in_8bit: true
 load_in_4bit: false
 
 datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
+  - path: philschmid/guanaco-sharegpt-style
-    type: chat_template
+    type: sharegpt
     shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model

docker/Dockerfile

@@ -1,10 +1,11 @@
 ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base:$BASE_TAG
+FROM winglian/axolotl-base:$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"
+ENV BNB_CUDA_VERSION=$CUDA
 ARG PYTORCH_VERSION="2.1.2"
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
@@ -19,15 +20,13 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
+RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py | sh
-RUN python scripts/cutcrossentropy_install.py | sh
-
 # So we can test the Docker image
 RUN pip install pytest
 

docker/Dockerfile-base

@@ -16,7 +16,7 @@ ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 
 RUN apt-get update \
-    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/* \
     && wget \
     https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \
@@ -29,9 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
 
 RUN git lfs install --skip-repo && \
     pip3 install awscli && \

docker/Dockerfile-cloud

@@ -1,8 +1,8 @@
 ARG BASE_TAG=main
-FROM axolotlai/axolotl:$BASE_TAG
+FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docker/Dockerfile-cloud-no-tmux

@@ -1,8 +1,8 @@
 ARG BASE_TAG=main
-FROM axolotlai/axolotl:$BASE_TAG
+FROM winglian/axolotl:$BASE_TAG
 
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 

docker/Dockerfile-tests

@@ -1,10 +1,11 @@
 ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base:$BASE_TAG
+FROM winglian/axolotl-base:$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"
+ENV BNB_CUDA_VERSION=$CUDA
 ARG PYTORCH_VERSION="2.1.2"
 ARG GITHUB_REF="main"
 
@@ -24,9 +25,9 @@ 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] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docs/amd_hpc.qmd

@@ -1,108 +0,0 @@
----
-title: Training with AMD GPUs on HPC Systems
-description: A comprehensive guide for using Axolotl on distributed systems with AMD GPUs
----
-
-This guide provides step-by-step instructions for installing and configuring Axolotl on a High-Performance Computing (HPC) environment equipped with AMD GPUs.
-
-## Setup
-
-### 1. Install Python
-
-We recommend using Miniforge, a minimal conda-based Python distribution:
-
-```bash
-curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
-bash Miniforge3-$(uname)-$(uname -m).sh
-```
-
-### 2. Configure Python Environment
-Add Python to your PATH and ensure it's available at login:
-
-```bash
-echo 'export PATH=~/miniforge3/bin:$PATH' >> ~/.bashrc
-echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' >> ~/.bash_profile
-```
-
-### 3. Load AMD GPU Software
-
-Load the ROCm module:
-
-```bash
-module load rocm/5.7.1
-```
-
-Note: The specific module name and version may vary depending on your HPC system. Consult your system documentation for the correct module name.
-
-### 4. Install PyTorch
-
-Install PyTorch with ROCm support:
-
-```bash
-pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7 --force-reinstall
-```
-
-### 5. Install Flash Attention
-
-Clone and install the Flash Attention repository:
-
-```bash
-git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.git
-export GPU_ARCHS="gfx90a"
-cd flash-attention
-export PYTHON_SITE_PACKAGES=$(python -c 'import site; print(site.getsitepackages()[0])')
-patch "${PYTHON_SITE_PACKAGES}/torch/utils/hipify/hipify_python.py" hipify_patch.patch
-pip install --no-build-isolation .
-```
-
-### 6. Install Axolotl
-
-Clone and install Axolotl:
-
-```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl
-cd axolotl
-pip install packaging ninja
-pip install --no-build-isolation -e .
-```
-
-### 7. Apply xformers Workaround
-
-xformers appears to be incompatible with ROCm. Apply the following workarounds:
- - Edit $HOME/packages/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py modifying the code to always return `False` for SwiGLU availability from xformers.
- - Edit $HOME/miniforge3/lib/python3.10/site-packages/xformers/ops/swiglu_op.py replacing the "SwiGLU" function with a pass statement.
-
-### 8. Prepare Job Submission Script
-
-Create a script for job submission using your HPC's particular software (e.g. Slurm, PBS). Include necessary environment setup and the command to run Axolotl training. If the compute node(s) do(es) not have internet access, it is recommended to include
-
-```bash
-export TRANSFORMERS_OFFLINE=1
-export HF_DATASETS_OFFLINE=1
-```
-
-### 9. Download Base Model
-
-Download a base model using the Hugging Face CLI:
-
-```bash
-huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
-```
-
-### 10. Create Axolotl Configuration
-
-Create an Axolotl configuration file (YAML format) tailored to your specific training requirements and dataset. Use FSDP for multi-node training.
-
-Note: Deepspeed did not work at the time of testing. However, if anyone managed to get it working, please let us know.
-
-### 11. Preprocess Data
-
-Run preprocessing on the login node:
-
-```bash
-CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess /path/to/your/config.yaml
-```
-
-### 12. Train
-
-You are now ready to submit your previously prepared job script. 🚂

docs/config.qmd

@@ -83,15 +83,22 @@ lora_on_cpu: true
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
   - path: vicgalle/alpaca-gpt4
-    # The type of prompt to use for training. [alpaca, gpteacher, oasst, reflection]
+  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
     type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
     ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
     train_on_split: train # Optional[str] name of dataset split to load from
-    revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
+
-    trust_remote_code: # Optional[bool] Trust remote code for untrusted source
+    # Optional[str] fastchat conversation type, only used with type: sharegpt
+    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+    field_human: # Optional[str]. Human key to use for conversation.
+    field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
 
   # Custom user instruction prompt
   - path: repo
@@ -116,55 +123,10 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
-  # Using chat template
-  - path: ...
-    # Set type to `chat_template` to use this strategy
-    type: chat_template
-    # Specify the name of the chat template to use
-    # The name of the chat template to use for training, following values are supported:
-    # - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default.
-    # - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
-    # - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
-    # - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
-    chat_template: tokenizer_default
-    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
-    chat_template_jinja:
-    # The key in the data example that contains the messages. Default is "messages".
-    field_messages: messages
-    # The key in the message turn that contains the role. Default is "role".
-    message_field_role: role
-    # The key in the message turn that contains the content. Default is "content".
-    message_field_content: content
-    # Optional[Dict[str, List]]. Roles mapping for the messages.
-    roles:
-      user: ["human", "user"]
-      assistant: ["gpt", "assistant", "ai"]
-      system: ["system"]
-
-    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
-
-    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
-    roles_to_train: ["gpt", "assistant"]
-    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
-    # - all: train on all EOS tokens
-    # - turn: train on the EOS token at the end of each trainable turn
-    # - last: train on the last EOS token in the conversation
-    train_on_eos: last
-    # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
-    message_field_training: training
-    # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
-    # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
-    # See example at `docs/dataset-formats/conversation.qmd`
-    message_field_training_detail: train_detail
-
-
 # If false, the datasets will not be shuffled and will keep their original order in `datasets`.
 # The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
 shuffle_merged_datasets: true
 
-Deduplicates datasets and test_datasets with identical entries.
-dataset_exact_deduplication: true
-
 # A list of one or more datasets to eval the model with.
 # You can use either test_datasets, or val_set_size, but not both.
 test_datasets:
@@ -178,19 +140,10 @@ test_datasets:
 
 # use RL training: 'dpo', 'ipo', 'kto'
 rl:
-# whether to perform weighting if doing DPO training. Boolean.
-dpo_use_weighting:
 
-# The name of the chat template to use for training, following values are supported:
+# Saves the desired chat template to the tokenizer_config.json for easier inferencing
-# - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
+# Currently supports chatml and inst (mistral/mixtral)
-# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
+chat_template: chatml
-# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
-# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
-# The selected chat template will be saved to the tokenizer_config.json for easier inferencing
-# Note: It is recommended to set train_on_inputs to true when using a chat template that is different from the model's default chat template.
-chat_template: tokenizer_default
-# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
-chat_template_jinja: null
 # Changes the default system message
 default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
@@ -312,21 +265,8 @@ wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_step
 # mlflow configuration if you're using it
 mlflow_tracking_uri: # URI to mlflow
 mlflow_experiment_name: # Your experiment name
-mlflow_run_name: # Your run name
 hf_mlflow_log_artifacts:  # set to true to copy each saved checkpoint on each save to mlflow artifact registry
 
-# Comet configuration if you're using it
-# Make sure your `COMET_API_KEY` environment variable is set (recommended) or you login to Comet with `comet login`.
-# Check out our documentation for more details https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/Experiment-Creation/#comet_ml.start
-use_comet: # Enable or disable Comet integration.
-comet_api_key: # API key for Comet. Recommended to set via `comet login`.
-comet_workspace: # Workspace name in Comet. Defaults to the user's default workspace.
-comet_project_name: # Project name in Comet. Defaults to Uncategorized.
-comet_experiment_key: # Identifier for the experiment. Used to append data to an existing experiment or control the key of new experiments. Default to a random key.
-comet_mode: # Create a new experiment ("create") or log to an existing one ("get"). Default ("get_or_create") auto-selects based on configuration.
-comet_online: # Set to True to log data to Comet server, or False for offline storage. Default is True.
-comet_experiment_config: # Dictionary for additional configuration settings, see the doc for more details.
-
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
@@ -361,7 +301,7 @@ max_steps:
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
+eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
 
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
@@ -409,7 +349,6 @@ lr_div_factor: # Learning rate div factor
 # - adamw_torch_fused
 # - adamw_torch_xla
 # - adamw_apex_fused
-# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
 # - sgd

docs/dataset-formats/conversation.qmd

@@ -6,8 +6,31 @@ order: 3
 
 ## sharegpt
 
-IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
+conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
 
+```{.json filename="data.jsonl"}
+{"conversations": [{"from": "...", "value": "..."}]}
+```
+
+Note: `type: sharegpt` opens special configs:
+- `conversation`: enables conversions to many Conversation types. Refer to the 'name' [here](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) for options.
+- `roles`: allows you to specify the roles for input and output. This is useful for datasets with custom roles such as `tool` etc to support masking.
+- `field_human`: specify the key to use instead of `human` in the conversation.
+- `field_model`: specify the key to use instead of `gpt` in the conversation.
+
+```yaml
+datasets:
+    path: ...
+    type: sharegpt
+
+    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+    field_human: # Optional[str]. Human key to use for conversation.
+    field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
+```
 
 ## pygmalion
 
@@ -15,137 +38,34 @@ IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 {"conversations": [{"role": "...", "value": "..."}]}
 ```
 
+## sharegpt.load_role
 
-## chat_template
+conversations where `role` is used instead of `from`
-
-Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.
 
 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "content": "..."}]}
+{"conversations": [{"role": "...", "value": "..."}]}
 ```
 
-See `config.qmd` for full configs and supported templates.
+## sharegpt.load_guanaco
 
-### Migrating from sharegpt
+conversations where `from` is `prompter` `assistant` instead of default sharegpt
-
-Most configs can be adapted as follows:
-
-```yaml
-# old
-chat_template: chatml
-datasets:
-  - path: ...
-    type: sharegpt
-    conversation: chatml
-
-# new (if using tokenizer's chat_template)
-datasets:
-  - path: ...
-    type: chat_template
-
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
-# new (if setting a new chat_template like chatml, gemma, etc)
-chat_template: chatml
-datasets:
-  - path: ...
-    type: chat_template
-
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-```
-
-We recommend checking the below examples for other usecases.
-
-### Examples
-
-1. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
-
-```yaml
-datasets:
-  - path: ...
-    type: chat_template
-```
-
-2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
-
-```yaml
-chat_template: gemma # this overwrites the tokenizer's chat_template
-datasets:
-  - path: ...
-    type: chat_template
-    roles_to_train: ["assistant"]
-```
-
-3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
-
-```yaml
-chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
-datasets:
-  - path: ...
-    type: chat_template
-    roles_to_train: ["assistant"]
-```
-
-4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
-
-```yaml
-# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
-chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"
-
-datasets:
-  - path: ...
-    type: chat_template
-    roles_to_train: ["assistant"]
-```
-
-5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
-
-For a data sample that looks like:
 
 ```{.json filename="data.jsonl"}
-{
+{"conversations": [{"from": "...", "value": "..."}]}
-  "conversations": [
-    {"from": "system", "value": "You are an AI assistant.", "train": false},
-    {"from": "human", "value": "Hello", "train": false},
-    {"from": "assistant", "value": "Hello", "train": true},
-    {"from": "human", "value": "How are you?", "train": true},
-    {
-      "from": "assistant",
-      "value": "I'm doing very well, thank you!",
-      "train_detail": [
-        {"begin_offset": 0, "end_offset": 8, "train": false},
-        {"begin_offset": 9, "end_offset": 18, "train": true},
-        {"begin_offset": 19, "end_offset": 30, "train": false},
-      ],
-    },
-    {
-        "from": "human",
-        "value": "I'm doing very well, thank you!",
-        "train": true,
-    },
-    {"from": "assistant", "value": "Hi there!", "train": true}
-  ]
-}
 ```
 
-The configuration would look like:
+## sharegpt.load_ultrachat
 
-```yaml
+conversations where the turns field is 'messages', human is 'user' and gpt is 'assistant'.
-datasets:
+
-  - path: ...
+```{.json filename="data.jsonl"}
-    type: chat_template
+{"messages": [{"user": "...", "assistant": "..."}]}
-    chat_template: tokenizer_default
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-    roles_to_train: []
-    train_on_eos: turn
-    message_field_training: train
-    message_field_training_detail: train_detail
 ```
 
-Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.
+## sharegpt_jokes
+
+creates a chat where bot is asked to tell a joke, then explain why the joke is funny
+
+```{.json filename="data.jsonl"}
+{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
+```

docs/dataset-formats/tokenized.qmd

@@ -7,7 +7,7 @@ order: 5
 - Pass an empty `type:` in your axolotl config.
 - Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
 - To indicate that a token should be ignored during training, set its corresponding label to `-100`.
-- You must add BOS and EOS, and make sure that you are training on EOS by not setting its label to -100.
+- Do not add BOS/EOS. Axolotl will add them for you based on the default tokenizer for the model you're using.
 - For pretraining, do not truncate/pad documents to the context window length.
 - For instruction training, documents must be truncated/padded as desired.
 

docs/debugging.qmd

@@ -51,12 +51,12 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 
 ### Background
 
-The below example shows how to configure VSCode to debug data preprocessing of the `chat_template` format.  This is the format used when you have the following in your axolotl config:
+The below example shows how to configure VSCode to debug data preprocessing of the `sharegpt` format.  This is the format used when you have the following in your axolotl config:
 
 ```yaml
 datasets:
-  - path: <path to your chat_template formatted dataset> # example on HF Hub: fozziethebeat/alpaca_messages_2k_test
+  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
-    type: chat_template
+    type: sharegpt
 ```
 
 >[!Important]
@@ -71,7 +71,7 @@ Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/us
 
 ```bash
 pip3 install packaging
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install -e '.[flash-attn,deepspeed]'
 ```
 
 #### Remote Hosts
@@ -83,7 +83,7 @@ If you developing on a remote host, you can easily use VSCode to debug remotely.
 
 The easiest way to get started is to modify the [.vscode/launch.json](../.vscode/launch.json) file in this project.  This is just an example configuration, so you may need to modify or copy it to suit your needs.
 
-For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_chat_template.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
+For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 accelerate launch -m axolotl.cli.train dev_sharegpt.yml`, you would use the below configuration[^1].  Note that we add additional flags that override the axolotl config and incorporate the tips above (see the comments). We also set the working directory to `devtools` and set the `env` variable `HF_HOME` to a temporary folder that is later partially deleted.  This is because we want to delete the HF dataset cache before each run in order to ensure that the data preprocessing code is run from scratch.
 
 ```jsonc
 // .vscode/launch.json
@@ -91,12 +91,12 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Debug axolotl prompt - chat_template",
+            "name": "Debug axolotl prompt - sharegpt",
             "type": "python",
             "module": "accelerate.commands.launch",
             "request": "launch",
             "args": [
-                "-m", "axolotl.cli.train", "dev_chat_template.yml",
+                "-m", "axolotl.cli.train", "dev_sharegpt.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
                 "--dataset_processes=1",      // limits data preprocessing to one process
@@ -185,7 +185,7 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 
 ## Debugging With Docker
 
-Using [official Axolotl Docker images](https://hub.docker.com/r/axolotlai/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
+Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/axolotl/tags) is a great way to debug your code, and is a very popular way to use Axolotl.  Attaching VSCode to Docker takes a few more steps.
 
 ### Setup
 
@@ -202,17 +202,17 @@ cd axolotl
 Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:[^2]
 
 ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
 ```
 
 >[!Tip]
-> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/axolotlai/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
+> To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/winglian/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
 
 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]'
+pip3 install -e '.[flash-attn,deepspeed]'
 ```
 
 ### Attach To Container
@@ -240,6 +240,6 @@ style="border-radius: 10px; display: block; margin: auto;" width="560" height="3
 </div>
 <br>
 
-[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/chat_template.yml`, but this is the same thing.
+[^1]: The config actually mimics the command `CUDA_VISIBLE_DEVICES=0 python -m accelerate.commands.launch -m axolotl.cli.train devtools/sharegpt.yml`, but this is the same thing.
 
 [^2]: Many of the below flags are recommended best practices by Nvidia when using nvidia-container-toolkit.  You can read more about these flags [here](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html).

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ignored by comparing the labels
+We can check that the right tokens are ingored by comparing the labels
 to each token:
 
 ```python

docs/multimodal.qmd

@@ -1,28 +0,0 @@
-# MultiModal / Vision Language Models (BETA)
-
-### Supported Models
-
-- Mllama, i.e. llama with vision models
-
-### Usage
-
-Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
-you'll need to use the following in YAML in combination with the rest of the required hyperparams.
-
-```yaml
-base_model: alpindale/Llama-3.2-11B-Vision-Instruct
-processor_type: AutoProcessor
-skip_prepare_dataset: true
-
-chat_template: llama3_2_vision
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-remove_unused_columns: false
-sample_packing: false
-
-# only finetune the Language model, leave the vision model and vision tower frozen
-lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-```

docs/rlhf.qmd

@@ -52,26 +52,6 @@ datasets:
     type: chat_template.argilla
 ```
 
-
-#### KTO
-
-```yaml
-rl: kto
-rl_beta: 0.5
-kto_desirable_weight: 0.2
-
-remove_unused_columns: false
-
-datasets:
-  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
-    type: llama3.ultra
-    split: train
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-```
-
 #### Using local dataset files
 ```yaml
 datasets:

docs/unsloth.qmd

@@ -11,10 +11,12 @@ standard industry baselines.
 
 ### Installation
 
-The following will install the correct unsloth and extras from source.
+The following will install unsloth from source and downgrade xformers as unsloth is incompatible with the most up
+to date libraries.
 
 ```bash
-python scripts/unsloth_install.py | sh
+pip install --no-deps "unsloth @ git+https://github.com/unslothai/unsloth.git"
+pip install --no-deps --force-reinstall xformers==0.0.26.post1
 ```
 
 ### Using unsloth w Axolotl

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

@@ -2,15 +2,19 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "AKjdG7tbTb-n"
+   },
    "source": [
-    "## Setting up"
+    "# Example notebook for running Axolotl on google colab"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "RcbNpOgWRcii"
+   },
    "outputs": [],
    "source": [
     "import torch\n",
@@ -18,76 +22,82 @@
     "assert (torch.cuda.is_available()==True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h3nLav8oTRA5"
+   },
+   "source": [
+    "## Install Axolotl and dependencies"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3c3yGAwnOIdi",
+    "outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
+   },
    "outputs": [],
    "source": [
-    "!pip install --no-build-isolation axolotl[deepspeed]"
+    "!pip install -e git+https://github.com/axolotl-ai-cloud/axolotl#egg=axolotl\n",
+    "!pip install flash-attn==\"2.5.0\"\n",
+    "!pip install deepspeed==\"0.13.1\"!pip install mlflow==\"2.13.0\""
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "BW2MFr7HTjub"
+   },
    "source": [
-    "## Hugging Face login (optional)"
+    "## Create an yaml config file"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
-   "outputs": [],
+    "id": "9pkF2dSoQEUN"
-   "source": [
+   },
-    "from huggingface_hub import notebook_login\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
    "outputs": [],
    "source": [
     "import yaml\n",
     "\n",
+    "# Your YAML string\n",
     "yaml_string = \"\"\"\n",
-    "base_model: NousResearch/Meta-Llama-3.1-8B\n",
+    "base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
+    "model_type: LlamaForCausalLM\n",
+    "tokenizer_type: LlamaTokenizer\n",
     "\n",
     "load_in_8bit: false\n",
     "load_in_4bit: true\n",
     "strict: false\n",
     "\n",
     "datasets:\n",
-    "  - path: tatsu-lab/alpaca\n",
+    "  - path: mhenrichsen/alpaca_2k_test\n",
     "    type: alpaca\n",
-    "dataset_prepared_path: last_run_prepared\n",
+    "dataset_prepared_path:\n",
     "val_set_size: 0.05\n",
-    "output_dir: ./outputs/lora-out\n",
+    "output_dir: ./outputs/qlora-out\n",
-    "\n",
-    "sequence_len: 2048\n",
-    "sample_packing: true\n",
-    "eval_sample_packing: true\n",
-    "pad_to_sequence_len: true\n",
     "\n",
     "adapter: qlora\n",
     "lora_model_dir:\n",
+    "\n",
+    "sequence_len: 4096\n",
+    "sample_packing: true\n",
+    "eval_sample_packing: false\n",
+    "pad_to_sequence_len: true\n",
+    "\n",
     "lora_r: 32\n",
     "lora_alpha: 16\n",
     "lora_dropout: 0.05\n",
+    "lora_target_modules:\n",
     "lora_target_linear: true\n",
     "lora_fan_in_fan_out:\n",
-    "lora_modules_to_save:\n",
-    "  - embed_tokens\n",
-    "  - lm_head\n",
     "\n",
     "wandb_project:\n",
     "wandb_entity:\n",
@@ -95,12 +105,12 @@
     "wandb_name:\n",
     "wandb_log_model:\n",
     "\n",
-    "gradient_accumulation_steps: 2\n",
+    "gradient_accumulation_steps: 4\n",
-    "micro_batch_size: 1\n",
+    "micro_batch_size: 2\n",
-    "num_epochs: 1\n",
+    "num_epochs: 4\n",
-    "optimizer: paged_adamw_8bit\n",
+    "optimizer: paged_adamw_32bit\n",
     "lr_scheduler: cosine\n",
-    "learning_rate: 2e-5\n",
+    "learning_rate: 0.0002\n",
     "\n",
     "train_on_inputs: false\n",
     "group_by_length: false\n",
@@ -111,15 +121,13 @@
     "gradient_checkpointing: true\n",
     "early_stopping_patience:\n",
     "resume_from_checkpoint:\n",
+    "local_rank:\n",
     "logging_steps: 1\n",
     "xformers_attention:\n",
-    "flash_attention: false\n",
+    "flash_attention: true\n",
-    "sdp_attention: true\n",
     "\n",
-    "warmup_steps: 1\n",
+    "warmup_steps: 10\n",
-    "max_steps: 25\n",
+    "evals_per_epoch: 4\n",
-    "evals_per_epoch: 1\n",
-    "eval_table_size:\n",
     "saves_per_epoch: 1\n",
     "debug:\n",
     "deepspeed:\n",
@@ -127,9 +135,8 @@
     "fsdp:\n",
     "fsdp_config:\n",
     "special_tokens:\n",
-    "  pad_token: <|end_of_text|>\n",
-    "\"\"\"\n",
     "\n",
+    "\"\"\"\n",
     "\n",
     "# Convert the YAML string to a Python dictionary\n",
     "yaml_dict = yaml.safe_load(yaml_string)\n",
@@ -139,124 +146,31 @@
     "\n",
     "# Write the YAML file\n",
     "with open(file_path, 'w') as file:\n",
-    "    yaml.dump(yaml_dict, file)"
+    "    yaml.dump(yaml_dict, file)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "bidoj8YLTusD"
+   },
    "source": [
-    "Above we have a configuration file with base LLM model and datasets specified, among many other things. Axolotl can automatically detect whether the specified datasets are on HuggingFace repo or local machine.\n",
+    "## Launch the training"
-    "\n",
-    "The Axolotl configuration options encompass model and dataset selection, data pre-processing, and training. Let's go through them line by line:\n",
-    "\n",
-    "*   \"base model\": String value, specifies the underlying pre-trained LLM that will be used for finetuning\n",
-    "\n",
-    "Next we have options for model weights quantization. Quantization allows for reduction in occupied memory on GPUs.\n",
-    "\n",
-    "*   \"load_in_8bit\": Boolean value, whether to quantize the model weights into 8-bit integer.\n",
-    "\n",
-    "*   \"load_in_4bit\": Boolean value, whether to quantize the model weights into 4-bit integer.\n",
-    "\n",
-    "*   \"strict\": Boolean value. If false, it allows for overriding established configuration options in the yaml file when executing in command-line interface.\n",
-    "\n",
-    "*   \"datasets\": a list of dicts that contain path and type of data sets as well as other optional configurations where datasets are concerned. Supports multiple datasets.\n",
-    "\n",
-    "*   \"val_set_size\": Either a float value less than one or an integer less than the total size of dataset. Sets the size of validation set from the whole dataset. If float, sets the proportion of the dataset assigned for validation. If integer, sets the direct size of validation set.\n",
-    "\n",
-    "*   \"output_dir\": String value. Path of trained model.\n",
-    "\n",
-    "For data preprocessing:\n",
-    "\n",
-    "*   \"sequence_len\": Integer. Specifies the maximum sequence length of the input. Typically 2048 or less.\n",
-    "\n",
-    "*   \"pad_to_sequence_len\": Boolean. Padding input to maximum sequence length.\n",
-    "\n",
-    "*   \"sample_packing\": Boolean. Specifies whether to use multi-packing with block diagonal attention.\n",
-    "\n",
-    "*   \"special_tokens\": Python dict, optional. Allows users to specify the additional special tokens to be ignored by the tokenizer.\n",
-    "\n",
-    "For LoRA configuration and its hyperparamters:\n",
-    "\n",
-    "*   \"adapter\": String. Either \"lora\" or \"qlora\", depending on user's choice.\n",
-    "\n",
-    "*   \"lora_model_dir\": String, Optional. Path to directory that contains LoRA model, if there is already a trained LoRA model the user would like to use.\n",
-    "\n",
-    "*   \"lora_r\": Integer. Refers to the rank of LoRA decomposition matrices. Higher value will reduce LoRA efficiency. Recommended to be set to 8.\n",
-    "\n",
-    "*   \"lora_alpha\": Integer. Scale the weight matrices by $\\frac{\\text{lora_alpha}}{\\text{lora_r}}$Recommended to be fixed at 16.\n",
-    "\n",
-    "*   \"lora_dropout\": Float that is 1 or less. The dropout probability of a lora layer.\n",
-    "\n",
-    "*   \"lora_target_linear\": Boolean. If true, lora will target all linear modules in the transformers architecture.\n",
-    "\n",
-    "*   \"lora_modules_to_save\": If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.\n",
-    "\n",
-    "See [LoRA](https://arxiv.org/abs/2106.09685) for detailed explanation of LoRA implementation.\n",
-    "\n",
-    "For the training configurations:\n",
-    "\n",
-    "*   \"gradient_accumulation_steps\": Integer. The number of steps over which to accumulate gradient for batch training. E.g. if 2, backprop is performed every two steps.\n",
-    "\n",
-    "*   \"micro_batch_size\": Integer. Batch size per gpu / gradient_accumulation_steps\n",
-    "\n",
-    "*   \"num_epochs\": Integer. Number of epochs. One epoch is when training has looped over every batch in the whole data set once.\n",
-    "\n",
-    "*   \"optimizer\": The optimizer to use for the training.\n",
-    "\n",
-    "*   \"learning_rate\": The learning rate.\n",
-    "\n",
-    "*   \"lr_scheduler\": The learning rate scheduler to use for adjusting learning rate during training.\n",
-    "\n",
-    "*   \"train_on_inputs\": Boolean. Whether to ignore or include the user's prompt from the training labels.\n",
-    "\n",
-    "*   \"group_by_length\": Boolean. Whether to group similarly sized data to minimize padding.\n",
-    "\n",
-    "*   \"bf16\": Either \"auto\", \"true\", or \"false\". Whether to use CUDA bf16 floating point format. If set to \"auto\", will automatically apply bf16 should the gpu supports it.\n",
-    "\n",
-    "*   \"fp16\": Optional. Specifies whether to use CUDA fp16. Automatically set to true if \"bf16\" is set to true. Otherwise false.\n",
-    "\n",
-    "*   \"tf32\": Boolean. Whether to use CUDA tf32. Will override bf16.\n",
-    "\n",
-    "*   \"gradient_checkpointing\": Boolean. Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing\n",
-    "\n",
-    "*   \"gradient_checkpointing_kwargs\": Python Dict. Fed into the trainer.\n",
-    "\n",
-    "*   \"logging_steps\": Integer. Log training information over every specified number of steps.\n",
-    "\n",
-    "*   \"flash_attention\": Boolean. Whether to use the [flash attention](https://github.com/Dao-AILab/flash-attention) mechanism.\n",
-    "\n",
-    "*   \"sdp_attention\": Boolean. Whether to use the Scaled Dot Product attention mechanism (the attention mechanism in the [original implementation](https://arxiv.org/abs/1706.03762) of transformers.)\n",
-    "\n",
-    "*   \"warmup_steps\": Integer. The number of pre-training steps where a very low learning rate is used.\n",
-    "\n",
-    "*   \"evals_per_epoch\": Integer. Number of evaluations to be performed within one training epoch.\n",
-    "\n",
-    "*   \"saves_per_epoch\": Integer. Number of times the model is saved in one training epoch.\n",
-    "\n",
-    "*   \"weight_decay\": Positive Float. Sets the \"strength\" of weight decay (i.e. setting the coefficient of L2 regularization)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above is but a snippet aiming to get users familiarized with the types of streamlined configuration options axolotl provides. For a full list of configuration options, see [here](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ydTI2Jk2RStU",
+    "outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
+   },
    "outputs": [],
    "source": [
+    "# By using the ! the comand will be executed as a bash command\n",
     "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
    ]
   },
@@ -264,7 +178,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Predict with trained model"
+    "## Play with inference"
    ]
   },
   {
@@ -273,85 +187,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# By using the ! the comand will be executed as a bash command\n",
     "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
-    "    --lora_model_dir=\"./outputs/lora-out\" --gradio"
+    "    --qlora_model_dir=\"./qlora-out\" --gradio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deeper Dive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is also helpful to gain some familiarity over some of the core inner workings of axolotl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuration Normalization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Axolotl uses a custom Dict class, called ```DictDefault```\n",
-    "to store configurations specified in the yaml configuration file (into a Python variable named ```cfg```). The definition for this custom Dict can be found in the [utils/dict.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/dict.py)\n",
-    "\n",
-    "```DictDefault``` is amended such that calling a missing key from it will result in a ```None``` return type. This is important because if some configuration options aren't specified by the user, the ```None``` type allows Axolotl to perform boolean operations to determine the default settings for missing configurations. For more examples on how this is done, check out [utils/config/__init__.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/__init__.py)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading Models, Tokenizers, and Trainer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we inspect [cli.train.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/cli/train.py), we will find that most of the heavy lifting were done by the function ```train()``` which is itself imported from [src/axolotl/train.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/train.py).\n",
-    "\n",
-    "```train()``` takes care of loading the appropriate tokenizer and pre-trained model through ```load_model()``` and ```load_tokenizer()``` from [src/axolotl/utils/models.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/models.py) respectively.\n",
-    "\n",
-    "```load_tokenizer()``` loads in the appropriate tokenizer given the desired model, as well as chat templates.\n",
-    "\n",
-    "```ModelLoader``` class follows after tokenizer has been selected. It will automatically discern the base model type, load in the desired model, as well as applying model-appropriate attention mechanism modifications (e.g. flash attention). Depending on which base model the user chooses in the configuration, ```ModelLoader``` will utilize the corresponding \"attention hijacking\" script. For example, if the user specified the base model to be ```NousResearch/Meta-Llama-3.1-8B```, which is of llama type, and set ```flash_attn``` to ```True```, ```ModelLoader``` will load in [llama_attn_hijack_flash.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/monkeypatch/llama_attn_hijack_flash.py). For a list of supported attention hijacking, please refer to the directory [/src/axolotl/monkeypatch/](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/monkeypatch)\n",
-    "\n",
-    "Another important operation encompassed in ```train()``` is setting up the training that takes into account of user-specified traning configurations (e.g. num_epochs, optimizer) through the use of ```setup_trainer()``` from [/src/axolotl/utils/trainer.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/trainer.py), which in turn relies on modules from [/src/axolotl/core/trainer_builder.py](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/core/trainer_builder.py).\n",
-    "```trainer_builder.py``` provides a list of trainer object options bespoke for the task type (Causal or Reinforcement learning ('dpo', 'ipo', 'kto') )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Monkey patch\n",
-    "\n",
-    "The [Monkey patch directory](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/monkeypatch) is where model architecture/optimization patching scripts are stored (these are modifications that are not implemented in the official releases, hence the name monkey patch). It includes attention jacking, ReLoRA, and unsloth optimization."
    ]
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.9.6"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -1,67 +0,0 @@
-base_model: deepseek-ai/DeepSeek-V2-Lite
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-special_tokens:
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: true
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -1,86 +0,0 @@
-base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-liger_rms_norm: true
-liger_glu_activation: true
-liger_fused_linear_cross_entropy: true
-
-chat_template: deepseek_v2
-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: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-adapter: qlora
-lora_r: 256
-lora_alpha: 256
-lora_target_linear: true
-peft_use_rslora: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 8
-num_epochs: 1
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-special_tokens:
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: true
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: DeepseekV2DecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD

examples/gemma2/qlora.yml

@@ -11,11 +11,8 @@ chat_template: gemma
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
+    chat_template: gemma
     drop_system_message: true
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
 val_set_size: 0.0
 output_dir: ./outputs/out
 

examples/gemma2/reward-model.yaml

@@ -1,63 +0,0 @@
-base_model: google/gemma-2-2b
-model_type: AutoModelForSequenceClassification
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-reward_model: true
-chat_template: gemma
-datasets:
-  - path: argilla/distilabel-intel-orca-dpo-pairs
-    type: bradley_terry.chat_template
-val_set_size: 0.0
-output_dir: ./outputs/out
-remove_unused_columns: false
-
-sequence_len: 2048
-sample_packing: false
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/jamba/qlora_fsdp_large.yaml

@@ -4,15 +4,11 @@ tokenizer_type: AutoTokenizer
 load_in_4bit: true
 strict: false
 use_tensorboard: true
-chat_template: jamba
 datasets:
   - path: cgato/SlimOrcaDedupCleaned
     type: chat_template
+    chat_template: jamba
     drop_system_message: true
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft

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

@@ -1,63 +0,0 @@
-base_model: alpindale/Llama-3.2-11B-Vision-Instruct
-processor_type: AutoProcessor
-strict: false
-
-# 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
-
-chat_template: llama3_2_vision
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.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: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -1,80 +0,0 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_fused_linear_cross_entropy: true
-
-strict: false
-
-chat_template: llama3
-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: 0.02
-output_dir: ./outputs/out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: 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_torch
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: true
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_backward_prefetch: BACKWARD_PRE
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>

examples/llama-3/fft-8b.yaml

@@ -1,4 +1,6 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
+base_model: NousResearch/Meta-Llama-3-8B
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: false

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

@@ -11,6 +11,7 @@ rl: dpo
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_dpo_test
     type: chat_template.default
+    chat_template: llama3
     field_messages: conversation
     field_chosen: chosen
     field_rejected: rejected

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

@@ -10,6 +10,7 @@ chat_template: llama3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
+    chat_template: llama3
     field_messages: messages
     message_field_role: role
     message_field_content: content

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

@@ -1,95 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: llama3
-rl: dpo
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_exact_deduplication: true
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/lora-out
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

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

@@ -1,76 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/lora-out
-
-dataset_exact_deduplication: true
-test_value: true
-
-sequence_len: 4096
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-   pad_token: <|end_of_text|>

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

@@ -1,74 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_fan_in_fan_out:
-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: 2
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/llama-3/qlora-1b-kto.yaml

@@ -1,75 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-rl: kto
-rl_beta: 0.5
-kto_desirable_weight: 0.2
-
-datasets:
-  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
-    type: llama3.ultra
-    split: train
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/qlora-out
-
-remove_unused_columns: false
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: false  # not supported with kto
-eval_sample_packing: false
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 64
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 20
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

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

@@ -1,76 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_fan_in_fan_out:
-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
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"

examples/mistral/mistral-dpo-qlora.yml

@@ -1,93 +0,0 @@
-#Note that we are switching from the regular chat template to chatml.
-#If you experience problems with the special tokens, training for more epochs can help.
-#After training, merge the model before inference otherwise you might
-#face problems with the special tokens.
-
-base_model: mistralai/Mistral-7B-Instruct-v0.2
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-chat_template: chatml
-rl: dpo
-datasets:
-  - path: olivermolenschot/alpaca_messages_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-qlora
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: true
-
-adapter: qlora
-lora_model_dir:
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.2
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-lora_modules_to_save:
- - embed_tokens
- - lm_head
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 16
-num_epochs: 6
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0001
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: false
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<|im_start|>"
-  eos_token: "<|im_end|>"

examples/phi/lora-3.5.yaml

@@ -1,75 +0,0 @@
-base_model: microsoft/Phi-3.5-mini-instruct
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: phi_3
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-    field_messages: messages
-    message_field_role: role
-    message_field_content: content
-    roles:
-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/lora-out
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 4
-num_epochs: 2
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bfloat16: true
-bf16: true
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 4
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:

examples/qwen2/dpo.yaml

@@ -1,67 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-
-strict: false
-
-chat_template: qwen_25
-rl: dpo
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_field_role: role
-    message_field_content: content
-    roles:
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-      user:
-        - user
-      assistant:
-        - assistant
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/dpo-out
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
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-fsdp:
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image/axolotl_symbol_digital_black.svg

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image/axolotl_wordmark_digital_black.svg

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image/axolotl_wordmark_digital_white.svg

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pyproject.toml

@@ -1,26 +0,0 @@
-[build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "axolotl"
-dynamic = ["version", "dependencies", "optional-dependencies"]
-description = "LLM Trainer"
-readme = "README.md"
-requires-python = ">=3.10"
-
-[project.scripts]
-axolotl = "axolotl.cli.main:main"
-
-[project.urls]
-Homepage = "https://axolotl-ai-cloud.github.io/axolotl/"
-Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
-
-[tool.setuptools_scm]
-
-[tool.setuptools]
-py-modules = ["setuptools_axolotl_dynamic_dependencies"]
-include-package-data = true
-
-[tool.setuptools.cmdclass]
-build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"

requirements-tests.txt

@@ -1,5 +1,2 @@
 pytest
 pytest-xdist
-pytest-retry
-pytest-sugar
-tbparse

requirements.txt

@@ -1,30 +1,22 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-
-# START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.0
-triton>=2.3.0
-mamba-ssm==1.2.0.post1
-flash-attn==2.7.0.post2
-xformers>=0.0.23.post1
-autoawq==0.2.7.post3
-liger-kernel==0.4.2
-# END section
-
 packaging==23.2
-peft==0.14.0
+peft==0.12.0
-transformers>=4.46.3
+transformers==4.44.0
-tokenizers>=0.20.1
+tokenizers>=0.19.1
-accelerate==1.2.0
+bitsandbytes==0.43.3
-datasets==3.1.0
+accelerate==0.33.0
-deepspeed==0.16.1
+datasets==2.20.0
+deepspeed==0.14.4
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
+flash-attn==2.6.3
 sentencepiece
 wandb
 einops
+xformers==0.0.27
 optimum==1.16.2
 hf_transfer
 colorama
@@ -34,26 +26,21 @@ numpy>=1.24.4,<=2.0.1
 evaluate==0.4.1
 scipy
 scikit-learn==1.4.2
-nvidia-ml-py==12.560.30
+pynvml
 art
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
 gradio==3.50.2
 tensorboard
 python-dotenv==1.0.1
+autoawq>=0.2.5
+
+mamba-ssm==1.2.0.post1
 
 # remote filesystems
 s3fs>=2024.5.0
 gcsfs>=2024.5.0
 # adlfs
 
-trl==0.12.1
+trl==0.9.6
 zstandard==0.22.0
 fastcore
-
-# lm eval harness
-lm_eval==0.4.4
-langdetect==1.0.9
-immutabledict==4.2.0
-antlr4-python3-runtime==4.13.2
-
-torchao==0.5.0
-schedulefree==1.3.0

requirements_env.txt

@@ -1,315 +0,0 @@
-accelerate==0.34.1
-addict==2.4.0
-aiofiles==23.2.1
-aiohttp==3.9.0
-aiosignal==1.3.1
-aiostream==0.5.2
-alembic==1.13.1
-annotated-types==0.6.0
-annoy==1.17.3
-ansible==6.7.0
-ansible-core==2.13.13
-ansible-vault==2.1.0
-anyio==3.7.1
-appdirs==1.4.4
-art==6.0
-asgiref==3.7.2
-async-timeout==4.0.2
-attrdict==2.0.1
-attrs==22.2.0
-awscli==1.32.75
--e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
-backoff==2.2.1
-base58==2.1.1
-beartype==0.17.2
-bitnet==0.2.1
-bitsandbytes==0.42.0
-bittensor==6.7.0
-black==23.7.0
-blinker==1.7.0
-boto3==1.34.75
-botocore==1.34.75
-cachetools==5.3.3
-cachy==0.1.1
-certifi==2023.7.22
-cffi==1.16.0
-cfgv==3.3.1
-chai-guanaco==1.2.4
-charset-normalizer==3.2.0
-cleo==0.6.8
-click==8.1.7
-cloudpickle==2.0.0
-cohere==4.11.2
-colorama==0.4.4
-coloredlogs==15.0.1
-CoLT5-attention==0.10.20
-contextlib2==21.6.0
-contourpy==1.2.0
-cryptography==41.0.3
-cycler==0.12.1
-cytoolz==0.12.3
-databricks-cli==0.18.0
-dataclasses-json==0.5.7
-datasets==2.11.0
-ddt==1.6.0
-decorator==5.1.1
-deepspeed==0.15.0
-# Editable Git install with no remote (dialogpt==0.1)
--e /Users/wing/Projects/ml/dialogpt/src
-dill==0.3.6
-distlib==0.3.6
-docker==7.0.0
-docker-pycreds==0.4.0
-docstring-parser==0.15
-docutils==0.16
-ecdsa==0.18.0
-einops==0.7.0
-einops-exts==0.0.4
-einx==0.1.3
-entrypoints==0.4
-eth-hash==0.6.0
-eth-keys==0.5.0
-eth-typing==4.0.0
-eth-utils==2.3.1
-evaluate==0.4.0
-exceptiongroup==1.1.1
-fastapi==0.109.2
-fastcore==1.5.29
-ffmpy==0.4.0
-filelock==3.12.2
--e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
-fire==0.5.0
-first==2.0.2
-flake8==7.0.0
-Flask==3.0.1
-fonttools==4.47.2
-frozendict==2.4.1
-frozenlist==1.3.3
-fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
-fsspec==2023.6.0
-fuzzywuzzy==0.18.0
-gitdb==4.0.10
-GitPython==3.1.31
-google-pasta==0.2.0
-gradio==4.42.0
-gradio_client==1.3.0
-greenlet==2.0.2
-grpclib==0.4.7
-gunicorn==21.2.0
-h11==0.14.0
-h2==4.1.0
-hpack==4.0.0
-httpcore==0.17.3
-httpx==0.24.1
-huggingface-hub==0.23.4
-humanfriendly==10.0
-hyperframe==6.0.1
-identify==2.5.24
-idna==3.4
-immutables==0.20
-importlib-metadata==6.7.0
-importlib-resources==6.1.1
-inflection==0.5.1
-iniconfig==2.0.0
-itsdangerous==2.1.2
-Jinja2==3.1.2
-jmespath==1.0.1
-joblib==1.3.2
-jsonlines==3.1.0
-jsonschema==2.6.0
-kiwisolver==1.4.5
-langchain==0.0.144
-Levenshtein==0.24.0
-libcst==1.1.0
-liger-kernel==0.0.0
-lion-pytorch==0.1.2
-llama-cpp-python==0.1.36
-llvmlite==0.40.1
-local-attention==1.9.0
-loguru==0.7.0
-Mako==1.3.2
-Markdown==3.5.2
-markdown-it-py==3.0.0
-markdown2==2.4.10
-MarkupSafe==2.1.2
-marshmallow==3.19.0
-marshmallow-enum==1.5.1
-matplotlib==3.8.2
-mccabe==0.7.0
-mdurl==0.1.2
-MEGABYTE-pytorch==0.0.7
--e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
-mlflow==2.10.0
-modal==0.62.77
-more-itertools==10.2.0
-mpmath==1.2.1
-msgpack==1.0.7
-msgpack-numpy-opentensor==0.5.0
-multidict==6.0.4
-multiprocess==0.70.14
-munch==2.5.0
-mypy==1.3.0
-mypy-extensions==1.0.0
-nest-asyncio==1.6.0
-netaddr==0.10.1
-networkx==3.0rc1
-nh3==0.2.14
-nodeenv==1.8.0
-nomic==2.0.2
-numba==0.57.1
-numexpr==2.8.4
-numpy==1.24.4
-oauthlib==3.2.2
-openai==0.27.4
-openapi==1.1.0
-openapi-schema-pydantic==1.2.4
-optimum==1.8.6
-orjson==3.10.7
-packaging==23.1
-pandas==2.0.0
-parameterized==0.9.0
-password-strength==0.0.3.post2
-pastel==0.1.1
-pathos==0.3.0
-pathspec==0.11.1
-pathtools==0.1.2
-peft==0.11.1
-pendulum==3.0.0
-Pillow==9.5.0
-pip-tools==1.11.0
-platformdirs==3.2.0
-pluggy==1.4.0
-poetry==0.7.1
-pox==0.3.2
-ppft==1.7.6.6
-pre-commit==3.3.2
-prettytable==3.10.0
-prompt-toolkit==3.0.39
-protobuf==3.20.2
-protobuf3-to-dict==0.1.5
-psutil==5.9.5
-psycopg==3.1.18
-PuLP==2.8.0
-py==1.11.0
-py-bip39-bindings==0.1.11
-py-cpuinfo==9.0.0
-py-ed25519-zebra-bindings==1.0.1
-py-sr25519-bindings==0.2.0
-pyarrow==11.0.0
-pyasn1==0.6.0
-pycodestyle==2.11.1
-pycparser==2.21
-pycryptodome==3.20.0
-pydantic==2.5.3
-pydantic_core==2.14.6
-pydub==0.25.1
-pyfiglet==0.8.post1
-pyflakes==3.2.0
-Pygments==2.15.1
-PyJWT==2.8.0
-pylev==1.4.0
-PyNaCl==1.5.0
-pynvml==11.5.0
-pyparsing==2.4.7
-pyrsistent==0.14.11
-pytest==8.0.2
-pytest-asyncio==0.23.4
-python-dateutil==2.8.2
-python-dotenv==1.0.1
-python-Levenshtein==0.24.0
-python-multipart==0.0.9
-pytz==2023.3
-PyYAML==6.0.1
-querystring-parser==1.2.4
-rapidfuzz==3.6.1
-regex==2023.6.3
-requests==2.31.0
-requests-toolbelt==0.8.0
-resolvelib==0.8.1
-responses==0.18.0
-retry==0.9.2
-rich==13.7.0
-rsa==4.7.2
-ruff==0.6.3
-s3transfer==0.10.1
-safetensors==0.4.5
-sagemaker==2.148.0
-scalecodec==1.2.7
-schedulefree==1.2.1
-schema==0.7.5
-scikit-learn==1.4.0
-scipy==1.9.3
-seaborn==0.13.2
-semantic-version==2.10.0
-sentencepiece==0.2.0
-sentry-sdk==1.19.1
-setproctitle==1.3.2
-shellingham==1.5.4
-shortuuid==1.0.11
-shtab==1.6.5
-sigtools==4.0.1
-six==1.16.0
-skypilot==0.4.1
-smdebug-rulesconfig==1.0.1
-smmap==5.0.0
-sniffio==1.3.0
-SQLAlchemy==1.4.47
-sqlparse==0.4.4
-starlette==0.36.3
-substrate-interface==1.5.2
-svgwrite==1.4.3
-sympy==1.11.1
-synchronicity==0.6.7
-tabulate==0.9.0
-tblib==1.7.0
-tenacity==8.2.2
-tensor-parallel==2.0.0
-termcolor==2.2.0
-text2art==0.2.0
-threadpoolctl==3.2.0
-tiktoken==0.6.0
-time-machine==2.14.1
-timm==0.9.16
-tokenizers==0.19.1
-tokenmonster==1.1.12
-toml==0.9.6
-tomli==2.0.1
-tomlkit==0.12.0
-toolz==0.12.1
-torch==2.2.0
-torchdata==0.6.1
-torchdiffeq==0.2.3
-TorchFix==0.4.0
-torchtext==0.15.2
-torchvision==0.17.0
-tqdm==4.66.2
-transformers==4.44.2
-trl==0.9.6
-typer==0.12.5
-types-certifi==2021.10.8.3
-types-requests==2.31.0.20240125
-types-setuptools==69.0.0.20240125
-types-toml==0.10.8.7
-typing==3.7.4.3
-typing-inspect==0.8.0
-typing_extensions==4.9.0
-tyro==0.5.18
-tzdata==2023.3
-unique-names-generator==1.0.2
-urllib3==2.2.2
-uvicorn==0.22.0
-vector_quantize_pytorch==1.14.1
-virtualenv==20.23.0
-voyager==2.0.2
-wandb==0.16.2
-watchfiles==0.21.0
-wavedrom==2.0.3.post3
-wcwidth==0.2.6
-websocket-client==1.7.0
-websockets==12.0
-Werkzeug==3.0.1
-wonderwords==2.2.0
-xxhash==3.2.0
-yarl==1.8.2
-zetascale==2.2.7
-zipp==3.15.0

scripts/chat_datasets.py

@@ -1,60 +0,0 @@
-"""
-helper script to parse chat datasets into a usable yaml
-"""
-import click
-import yaml
-from datasets import load_dataset
-
-
-@click.command()
-@click.argument("dataset", type=str)
-@click.option("--split", type=str, default="train")
-def parse_dataset(dataset=None, split="train"):
-    ds_cfg = {}
-    ds_cfg["path"] = dataset
-    ds_cfg["split"] = split
-    ds_cfg["type"] = "chat_template"
-    ds_cfg["chat_template"] = "<<<Replace based on your model>>>"
-
-    dataset = load_dataset(dataset, split=split)
-    features = dataset.features
-    feature_keys = features.keys()
-    field_messages = None
-    for key in ["conversation", "conversations", "messages"]:
-        if key in feature_keys:
-            field_messages = key
-            break
-    if not field_messages:
-        raise ValueError(
-            f'No conversation field found in dataset: {", ".join(feature_keys)}'
-        )
-    ds_cfg["field_messages"] = field_messages
-
-    message_fields = features["conversations"][0].keys()
-    message_field_role = None
-    for key in ["from", "role"]:
-        if key in message_fields:
-            message_field_role = key
-            break
-    if not message_field_role:
-        raise ValueError(
-            f'No role field found in messages: {", ".join(message_fields)}'
-        )
-    ds_cfg["message_field_role"] = message_field_role
-
-    message_field_content = None
-    for key in ["content", "text", "value"]:
-        if key in message_fields:
-            message_field_content = key
-            break
-    if not message_field_content:
-        raise ValueError(
-            f'No content field found in messages: {", ".join(message_fields)}'
-        )
-    ds_cfg["message_field_content"] = message_field_content
-
-    print(yaml.dump({"datasets": [ds_cfg]}))
-
-
-if __name__ == "__main__":
-    parse_dataset()

scripts/cloud-entrypoint.sh

@@ -2,7 +2,7 @@
 
 # Export specific ENV variables to /etc/rp_environment
 echo "Exporting environment variables..."
-printenv | grep -E '^HF_|^BNB_|^CUDA_|^NCCL_|^NV|^RUNPOD_|^PATH=|^_=' | sed 's/^\([^=]*\)=\(.*\)$/export \1="\2"/' | grep -v 'printenv' >> /etc/rp_environment
+printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
 echo 'source /etc/rp_environment' >> ~/.bashrc
 
 add_keys_to_authorized() {

scripts/cutcrossentropy_install.py

@@ -1,28 +0,0 @@
-"""Script to output the correct installation command for cut-cross-entropy."""
-import importlib.util
-import sys
-
-try:
-    import torch
-except ImportError as exc:
-    raise ImportError("Install torch via `pip install torch`") from exc
-from packaging.version import Version as V
-
-v = V(torch.__version__)
-
-# no cut-cross-entropy support for torch < 2.4.0
-if v < V("2.4.0"):
-    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 && "
-
-print(
-    UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
-)

scripts/motd

@@ -13,5 +13,5 @@ cd /workspace
 rm -rf /workspace/axolotl
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip install --no-build-isolation --no-deps -e .
+pip install --no-deps -e .
 ```

scripts/unsloth_install.py

@@ -1,36 +0,0 @@
-# noqa
-# pylint: skip-file
-try:
-    import torch
-except ImportError:
-    raise ImportError("Install torch via `pip install torch`")
-from packaging.version import Version as V
-
-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"
-else:
-    raise RuntimeError(f"Torch = {v} too new!")
-x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
-print(
-    f'pip install unsloth-zoo==2024.11.7 && pip install --no-deps "unsloth[{x}]==2024.11.9"'
-)

setup.py

@@ -1,10 +1,8 @@
 """setup.py for axolotl"""
-import ast
+
-import os
 import platform
 import re
 from importlib.metadata import PackageNotFoundError, version
-from pathlib import Path
 
 from setuptools import find_packages, setup
 
@@ -32,19 +30,13 @@ def parse_requirements():
 
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
-        torchao_version = [req for req in _install_requires if "torchao" in req][0]
-        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
-
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
             _install_requires.pop(_install_requires.index(xformers_version))
         else:
             # detect the version of torch already installed
             # and set it so dependencies don't clobber the torch version
-            try:
+            torch_version = version("torch")
-                torch_version = version("torch")
-            except PackageNotFoundError:
-                torch_version = "2.5.1"
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -57,80 +49,48 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 5):
+            if (major, minor) >= (2, 3):
-                _install_requires.pop(_install_requires.index(xformers_version))
-                if patch == 0:
-                    _install_requires.append("xformers==0.0.28.post2")
-                else:
-                    _install_requires.append("xformers==0.0.28.post3")
-                _install_requires.pop(_install_requires.index(autoawq_version))
-            elif (major, minor) >= (2, 4):
-                if patch == 0:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                    _install_requires.append("xformers>=0.0.27")
-                else:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                    _install_requires.append("xformers==0.0.28.post1")
-            elif (major, minor) >= (2, 3):
-                _install_requires.pop(_install_requires.index(torchao_version))
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))
                     _install_requires.append("xformers>=0.0.26.post1")
-                else:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                    _install_requires.append("xformers>=0.0.27")
             elif (major, minor) >= (2, 2):
-                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.25.post1")
             else:
-                _install_requires.pop(_install_requires.index(torchao_version))
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers>=0.0.23.post1")
 
     except PackageNotFoundError:
         pass
+
     return _install_requires, _dependency_links
 
 
-def get_package_version():
-    with open(
-        Path(os.path.dirname(os.path.abspath(__file__)))
-        / "src"
-        / "axolotl"
-        / "__init__.py",
-        "r",
-        encoding="utf-8",
-    ) as fin:
-        version_match = re.search(r"^__version__\s*=\s*(.*)$", fin.read(), re.MULTILINE)
-    version_ = ast.literal_eval(version_match.group(1))
-    return version_
-
-
 install_requires, dependency_links = parse_requirements()
 
+
 setup(
-    version=get_package_version(),
+    name="axolotl",
+    version="0.4.1",
+    description="LLM Trainer",
+    long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
-    packages=find_packages("src"),
+    packages=find_packages(),
     install_requires=install_requires,
     dependency_links=dependency_links,
-    entry_points={
-        "console_scripts": [
-            "axolotl=axolotl.cli.main:main",
-        ],
-    },
     extras_require={
         "flash-attn": [
-            "flash-attn==2.7.0.post2",
+            "flash-attn==2.6.3",
+        ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.6.2#subdirectory=csrc/fused_dense_lib",
         ],
         "deepspeed": [
-            "deepspeed==0.16.1",
+            "deepspeed==0.14.4",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [
             "mamba-ssm==1.2.0.post1",
-            "causal_conv1d",
         ],
         "auto-gptq": [
             "auto-gptq==0.5.1",

src/axolotl/__init__.py

@@ -1,3 +0,0 @@
-"""Axolotl - Train and fine-tune large language models"""
-
-__version__ = "0.6.0"

src/axolotl/cli/__init__.py

@@ -29,22 +29,16 @@ from transformers.utils.import_utils import _is_package_available
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.chat_templates import (
-    get_chat_template,
-    get_chat_template_from_config,
-)
-from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
-    prepare_plugins,
     validate_config,
 )
 from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.models import load_processor, load_tokenizer
+from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
@@ -58,22 +52,8 @@ LOG = logging.getLogger("axolotl.scripts")
 
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
-AXOLOTL_LOGO = """
-     #@@ #@@      @@# @@#
-    @@  @@          @@  @@           =@@#                               @@                 #@    =@@#.
-    @@    #@@@@@@@@@    @@           #@#@=                              @@                 #@     .=@@
-      #@@@@@@@@@@@@@@@@@            =@# @#     ##=     ##    =####=+    @@      =#####+  =#@@###.   @@
-    @@@@@@@@@@/  +@@/  +@@          #@  =@=     #@=   @@   =@#+  +#@#   @@    =@#+  +#@#   #@.      @@
-    @@@@@@@@@@  ##@@  ##@@         =@#   @#      =@# @#    @@      @@   @@    @@      #@   #@       @@
-     @@@@@@@@@@@@@@@@@@@@          #@=+++#@=      =@@#     @@      @@   @@    @@      #@   #@       @@
-                                  =@#=====@@     =@# @#    @@      @@   @@    @@      #@   #@       @@
-    @@@@@@@@@@@@@@@@  @@@@        #@      #@=   #@=  +@@   #@#    =@#   @@.   =@#    =@#   #@.      @@
-                                 =@#       @#  #@=     #@   =#@@@@#=    +#@@=  +#@@@@#=    .##@@+   @@
-    @@@@  @@@@@@@@@@@@@@@@
-"""
 
-
+def print_axolotl_text_art(suffix=None):
-def print_legacy_axolotl_text_art(suffix=None):
     font = "nancyj"
     ascii_text = "  axolotl"
     if suffix:
@@ -86,13 +66,6 @@ def print_legacy_axolotl_text_art(suffix=None):
     print_dep_versions()
 
 
-def print_axolotl_text_art(
-    **kwargs,  # pylint: disable=unused-argument
-):
-    if is_main_process():
-        print(AXOLOTL_LOGO)
-
-
 def print_dep_versions():
     packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
     max_len = max(len(pkg) for pkg in packages)
@@ -100,8 +73,8 @@ def print_dep_versions():
         print("*" * 40)
         print("**** Axolotl Dependency Versions *****")
         for pkg in packages:
-            pkg_version = _is_package_available(pkg, return_version=True)
+            version = _is_package_available(pkg, return_version=True)
-            print(f"{pkg: >{max_len}}: {pkg_version[1]: <15}")
+            print(f"{pkg: >{max_len}}: {version[1]: <15}")
         print("*" * 40)
 
 
@@ -139,7 +112,7 @@ def check_remote_config(config: Union[str, Path]):
         with open(output_path, "wb") as file:
             file.write(content)
         LOG.info(
-            f"Using the following config obtained from {config}: \n\n{content.decode('utf-8')}\n"
+            f"Using the following config obtained from {config}:\n\n{content.decode('utf-8')}\n"
         )
         return output_path
 
@@ -193,19 +166,18 @@ def do_inference(
 ):
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
-    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
-    elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
-    elif cfg.datasets[0].type == "chat_template":
-        chat_template_str = get_chat_template_from_config(
-            cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
-        )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -215,31 +187,13 @@ def do_inference(
         instruction = get_multi_line_input()
         if not instruction:
             return
-
         if prompter_module:
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
         else:
             prompt = instruction.strip()
-
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
-        if chat_template_str:
-            batch = tokenizer.apply_chat_template(
-                [
-                    {
-                        "role": "user",
-                        "content": prompt,
-                    }
-                ],
-                return_tensors="pt",
-                add_special_tokens=True,
-                add_generation_prompt=True,
-                chat_template=chat_template_str,
-                tokenize=True,
-                return_dict=True,
-            )
-        else:
-            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         print("=" * 40)
         model.eval()
@@ -279,15 +233,18 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
-    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
-    elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -301,24 +258,7 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
-        if chat_template_str:
-            batch = tokenizer.apply_chat_template(
-                [
-                    {
-                        "role": "user",
-                        "content": prompt,
-                    }
-                ],
-                return_tensors="pt",
-                add_special_tokens=True,
-                add_generation_prompt=True,
-                chat_template=chat_template_str,
-                tokenize=True,
-                return_dict=True,
-            )
-        else:
-            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -341,7 +281,6 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
-                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }
@@ -380,7 +319,7 @@ def choose_config(path: Path):
 
     if len(yaml_files) == 1:
         print(f"Using default YAML file '{yaml_files[0]}'")
-        return str(yaml_files[0])
+        return yaml_files[0]
 
     print("Choose a YAML file:")
     for idx, file in enumerate(yaml_files):
@@ -391,7 +330,7 @@ def choose_config(path: Path):
         try:
             choice = int(input("Enter the number of your choice: "))
             if 1 <= choice <= len(yaml_files):
-                chosen_file = str(yaml_files[choice - 1])
+                chosen_file = yaml_files[choice - 1]
             else:
                 print("Invalid choice. Please choose a number from the list.")
         except ValueError:
@@ -432,8 +371,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
     except:  # pylint: disable=bare-except # noqa: E722
         gpu_version = None
 
-    prepare_plugins(cfg)
-
     cfg = validate_config(
         cfg,
         capabilities={
@@ -441,9 +378,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
-        env_capabilities={
-            "torch_version": str(torch.__version__).split("+", maxsplit=1)[0],
-        },
     )
 
     prepare_optim_env(cfg)
@@ -458,8 +392,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
 
     setup_mlflow_env_vars(cfg)
 
-    setup_comet_env_vars(cfg)
-
     return cfg
 
 
@@ -469,20 +401,12 @@ def load_datasets(
     cli_args: TrainerCliArgs,
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
-    processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg,
+        cfg, tokenizer
-        tokenizer,
-        processor=processor,
     )
 
-    if (
+    if cli_args.debug or cfg.debug:
-        cli_args.debug
-        or cfg.debug
-        or cli_args.debug_text_only
-        or int(cli_args.debug_num_examples) > 0
-    ):
         LOG.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(

src/axolotl/cli/inference.py

@@ -2,7 +2,6 @@
 CLI to run inference on a trained model
 """
 from pathlib import Path
-from typing import Union
 
 import fire
 import transformers
@@ -17,10 +16,10 @@ from axolotl.cli import (
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), gradio=False, **kwargs):
+def do_cli(config: Path = Path("examples/"), gradio=False, **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, inference=True, **kwargs)
+    parsed_cfg = load_cfg(config, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/main.py

@@ -1,233 +0,0 @@
-"""CLI definition for various axolotl commands."""
-# pylint: disable=redefined-outer-name
-import subprocess  # nosec B404
-from typing import Optional
-
-import click
-
-import axolotl
-from axolotl.cli.utils import (
-    add_options_from_config,
-    add_options_from_dataclass,
-    build_command,
-    fetch_from_github,
-)
-from axolotl.common.cli import PreprocessCliArgs, TrainerCliArgs
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
-
-
-@click.group()
-@click.version_option(version=axolotl.__version__, prog_name="axolotl")
-def cli():
-    """Axolotl CLI - Train and fine-tune large language models"""
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@add_options_from_dataclass(PreprocessCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def preprocess(config: str, **kwargs):
-    """Preprocess datasets before training."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    from axolotl.cli.preprocess import do_cli
-
-    do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for multi-GPU training",
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def train(config: str, accelerate: bool, **kwargs):
-    """Train or fine-tune a model."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.train"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.train import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for multi-GPU inference",
-)
-@click.option(
-    "--lora-model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing LoRA model",
-)
-@click.option(
-    "--base-model",
-    type=click.Path(exists=True, path_type=str),
-    help="Path to base model for non-LoRA models",
-)
-@click.option("--gradio", is_flag=True, help="Launch Gradio interface")
-@click.option("--load-in-8bit", is_flag=True, help="Load model in 8-bit mode")
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def inference(
-    config: str,
-    accelerate: bool,
-    lora_model_dir: Optional[str] = None,
-    base_model: Optional[str] = None,
-    **kwargs,
-):
-    """Run inference with a trained model."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-    del kwargs["inference"]  # interferes with inference.do_cli
-
-    if lora_model_dir:
-        kwargs["lora_model_dir"] = lora_model_dir
-    if base_model:
-        kwargs["output_dir"] = base_model
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.inference"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.inference import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=False,
-    help="Use accelerate launch for multi-GPU operations",
-)
-@click.option(
-    "--model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing model weights to shard",
-)
-@click.option(
-    "--save-dir",
-    type=click.Path(path_type=str),
-    help="Directory to save sharded weights",
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def shard(config: str, accelerate: bool, **kwargs):
-    """Shard model weights."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = ["accelerate", "launch", "-m", "axolotl.cli.shard"]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.shard import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--accelerate/--no-accelerate",
-    default=True,
-    help="Use accelerate launch for weight merging",
-)
-@click.option(
-    "--model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing sharded weights",
-)
-@click.option(
-    "--save-path", type=click.Path(path_type=str), help="Path to save merged weights"
-)
-@add_options_from_dataclass(TrainerCliArgs)
-@add_options_from_config(AxolotlInputConfig)
-def merge_sharded_fsdp_weights(config: str, accelerate: bool, **kwargs):
-    """Merge sharded FSDP model weights."""
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    if accelerate:
-        base_cmd = [
-            "accelerate",
-            "launch",
-            "-m",
-            "axolotl.cli.merge_sharded_fsdp_weights",
-        ]
-        if config:
-            base_cmd.append(config)
-        cmd = build_command(base_cmd, kwargs)
-        subprocess.run(cmd, check=True)  # nosec B603
-    else:
-        from axolotl.cli.merge_sharded_fsdp_weights import do_cli
-
-        do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("config", type=click.Path(exists=True, path_type=str))
-@click.option(
-    "--lora-model-dir",
-    type=click.Path(exists=True, path_type=str),
-    help="Directory containing the LoRA model to merge",
-)
-@click.option(
-    "--output-dir",
-    type=click.Path(path_type=str),
-    help="Directory to save the merged model",
-)
-def merge_lora(
-    config: str,
-    lora_model_dir: Optional[str] = None,
-    output_dir: Optional[str] = None,
-):
-    """Merge a trained LoRA into a base model"""
-    kwargs = {}
-    if lora_model_dir:
-        kwargs["lora_model_dir"] = lora_model_dir
-    if output_dir:
-        kwargs["output_dir"] = output_dir
-
-    from axolotl.cli.merge_lora import do_cli
-
-    do_cli(config=config, **kwargs)
-
-
-@cli.command()
-@click.argument("directory", type=click.Choice(["examples", "deepspeed_configs"]))
-@click.option("--dest", help="Destination directory")
-def fetch(directory: str, dest: Optional[str]):
-    """
-    Fetch example configs or other resources.
-
-    Available directories:
-    - examples: Example configuration files
-    - deepspeed_configs: DeepSpeed configuration files
-    """
-    fetch_from_github(f"{directory}/", dest)
-
-
-def main():
-    cli()
-
-
-if __name__ == "__main__":
-    main()

src/axolotl/cli/merge_lora.py

@@ -2,7 +2,6 @@
 CLI to run merge a trained LoRA into a base model
 """
 from pathlib import Path
-from typing import Union
 
 import fire
 import transformers
@@ -12,7 +11,7 @@ from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
 from axolotl.common.cli import TrainerCliArgs
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parser = transformers.HfArgumentParser((TrainerCliArgs))

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -177,7 +177,7 @@ def merge_fsdp_weights(
     state.wait_for_everyone()
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
     parser = transformers.HfArgumentParser((TrainerCliArgs))

src/axolotl/cli/preprocess.py

@@ -23,7 +23,10 @@ from axolotl.cli import (
 )
 from axolotl.common.cli import PreprocessCliArgs
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
-from axolotl.utils.trainer import disable_datasets_caching
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 
 LOG = logging.getLogger("axolotl.cli.preprocess")
 
@@ -40,6 +43,23 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         return_remaining_strings=True
     )
 
+    if parsed_cfg.chat_template == "chatml":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"ChatML set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_chatml_template(parsed_cfg.default_system_message)
+        else:
+            register_chatml_template()
+    elif parsed_cfg.chat_template == "llama3":
+        if parsed_cfg.default_system_message:
+            LOG.info(
+                f"LLaMA-3 set. Adding default system message: {parsed_cfg.default_system_message}"
+            )
+            register_llama3_template(parsed_cfg.default_system_message)
+        else:
+            register_llama3_template()
+
     if not parsed_cfg.dataset_prepared_path:
         msg = (
             Fore.RED
@@ -50,11 +70,10 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    with disable_datasets_caching():
+    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
-        if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-            load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
-        else:
+        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
-            load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
     if parsed_cli_args.download:
         model_name = parsed_cfg.base_model

src/axolotl/cli/train.py

@@ -3,11 +3,13 @@ CLI to run training on a model
 """
 import logging
 from pathlib import Path
-from typing import Union
+from typing import Tuple, Union
 
 import fire
 from dotenv import load_dotenv
 from transformers.hf_argparser import HfArgumentParser
+from transformers.modeling_utils import PreTrainedModel
+from transformers.tokenization_utils import PreTrainedTokenizer
 
 from axolotl.cli import (
     check_accelerate_default_config,
@@ -18,7 +20,10 @@ from axolotl.cli import (
     print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.integrations.base import PluginManager
+from axolotl.prompt_strategies.sharegpt import (
+    register_chatml_template,
+    register_llama3_template,
+)
 from axolotl.train import train
 
 LOG = logging.getLogger("axolotl.cli.train")
@@ -34,23 +39,32 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     return do_train(parsed_cfg, parsed_cli_args)
 
 
-def do_train(cfg, cli_args) -> None:
+def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
     print_axolotl_text_art()
     check_accelerate_default_config()
     check_user_token()
+    if cfg.chat_template == "chatml" and cfg.default_system_message:
+        LOG.info(
+            f"ChatML set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_chatml_template(cfg.default_system_message)
+    else:
+        register_chatml_template()
+
+    if cfg.chat_template == "llama3" and cfg.default_system_message:
+        LOG.info(
+            f"LLaMA-3 set. Adding default system message: {cfg.default_system_message}"
+        )
+        register_llama3_template(cfg.default_system_message)
+    else:
+        register_llama3_template()
 
     if cfg.rl:  # and cfg.rl != "orpo":
         dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    model, tokenizer = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-    plugin_manager = PluginManager.get_instance()
-
-    del model
-    del tokenizer
-
-    plugin_manager.post_train_unload(cfg)
 
 
 if __name__ == "__main__":

src/axolotl/cli/utils.py

@@ -1,218 +0,0 @@
-"""Utility methods for axoltl CLI."""
-import concurrent.futures
-import dataclasses
-import hashlib
-import json
-import logging
-from pathlib import Path
-from types import NoneType
-from typing import Any, Dict, List, Optional, Tuple, Type, Union, get_args, get_origin
-
-import click
-import requests
-from pydantic import BaseModel
-
-LOG = logging.getLogger("axolotl.cli.utils")
-
-
-def add_options_from_dataclass(config_class: Type[Any]):
-    """Create Click options from the fields of a dataclass."""
-
-    def decorator(function):
-        # Process dataclass fields in reverse order for correct option ordering
-        for field in reversed(dataclasses.fields(config_class)):
-            field_type = field.type
-
-            if get_origin(field_type) is Union and type(None) in get_args(field_type):
-                field_type = next(
-                    t for t in get_args(field_type) if not isinstance(t, NoneType)
-                )
-
-            if field_type == bool:
-                field_name = field.name.replace("_", "-")
-                option_name = f"--{field_name}/--no-{field_name}"
-                function = click.option(
-                    option_name,
-                    default=field.default,
-                    help=field.metadata.get("description"),
-                )(function)
-            else:
-                option_name = f"--{field.name.replace('_', '-')}"
-                function = click.option(
-                    option_name,
-                    type=field_type,
-                    default=field.default,
-                    help=field.metadata.get("description"),
-                )(function)
-        return function
-
-    return decorator
-
-
-def add_options_from_config(config_class: Type[BaseModel]):
-    """Create Click options from the fields of a Pydantic model."""
-
-    def decorator(function):
-        # Process model fields in reverse order for correct option ordering
-        for name, field in reversed(config_class.model_fields.items()):
-            if field.annotation == bool:
-                field_name = name.replace("_", "-")
-                option_name = f"--{field_name}/--no-{field_name}"
-                function = click.option(
-                    option_name, default=None, help=field.description
-                )(function)
-            else:
-                option_name = f"--{name.replace('_', '-')}"
-                function = click.option(
-                    option_name, default=None, help=field.description
-                )(function)
-        return function
-
-    return decorator
-
-
-def build_command(base_cmd: List[str], options: Dict[str, Any]) -> List[str]:
-    """Build command list from base command and options."""
-    cmd = base_cmd.copy()
-
-    for key, value in options.items():
-        if value is None:
-            continue
-
-        key = key.replace("_", "-")
-
-        if isinstance(value, bool):
-            if value:
-                cmd.append(f"--{key}")
-        else:
-            cmd.extend([f"--{key}", str(value)])
-
-    return cmd
-
-
-def download_file(
-    file_info: tuple, raw_base_url: str, dest_path: Path, dir_prefix: str
-) -> Tuple[str, str]:
-    """
-    Download a single file and return its processing status.
-
-    Args:
-        file_info: Tuple of (file_path, remote_sha)
-        raw_base_url: Base URL for raw GitHub content
-        dest_path: Local destination directory
-        dir_prefix: Directory prefix to filter files
-
-    Returns:
-        Tuple of (file_path, status) where status is 'new', 'updated', or 'unchanged'
-    """
-    file_path, remote_sha = file_info
-    raw_url = f"{raw_base_url}/{file_path}"
-    dest_file = dest_path / file_path.split(dir_prefix)[-1]
-
-    # Check if file exists and needs updating
-    if dest_file.exists():
-        with open(dest_file, "rb") as file:
-            content = file.read()
-            # Calculate git blob SHA
-            blob = b"blob " + str(len(content)).encode() + b"\0" + content
-            local_sha = hashlib.sha1(blob, usedforsecurity=False).hexdigest()
-
-        if local_sha == remote_sha:
-            print(f"Skipping {file_path} (unchanged)")
-            return file_path, "unchanged"
-
-        print(f"Updating {file_path}")
-        status = "new"
-    else:
-        print(f"Downloading {file_path}")
-        status = "new"
-
-    # Create directories if needed
-    dest_file.parent.mkdir(parents=True, exist_ok=True)
-
-    # Download and save file
-    try:
-        response = requests.get(raw_url, timeout=30)
-        response.raise_for_status()
-
-        with open(dest_file, "wb") as file:
-            file.write(response.content)
-
-        return file_path, status
-    except (requests.RequestException, IOError) as request_error:
-        print(f"Error downloading {file_path}: {str(request_error)}")
-        return file_path, "error"
-
-
-def fetch_from_github(
-    dir_prefix: str, dest_dir: Optional[str] = None, max_workers: int = 5
-) -> None:
-    """
-    Sync files from a specific directory in the GitHub repository.
-    Only downloads files that don't exist locally or have changed.
-
-    Args:
-        dir_prefix: Directory prefix to filter files (e.g., 'examples/', 'deepspeed_configs/')
-        dest_dir: Local destination directory
-        max_workers: Maximum number of concurrent downloads
-    """
-    api_url = "https://api.github.com/repos/axolotl-ai-cloud/axolotl/git/trees/main?recursive=1"
-    raw_base_url = "https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main"
-
-    # Get repository tree with timeout
-    response = requests.get(api_url, timeout=30)
-    response.raise_for_status()
-    tree = json.loads(response.text)
-
-    # Filter for files and get their SHA
-    files = {
-        item["path"]: item["sha"]
-        for item in tree["tree"]
-        if item["type"] == "blob" and item["path"].startswith(dir_prefix)
-    }
-
-    if not files:
-        raise click.ClickException(f"No files found in {dir_prefix}")
-
-    # Default destination directory is the last part of dir_prefix
-    default_dest = Path(dir_prefix.rstrip("/"))
-    dest_path = Path(dest_dir) if dest_dir else default_dest
-
-    # Keep track of processed files for summary
-    files_processed: Dict[str, List[str]] = {
-        "new": [],
-        "updated": [],
-        "unchanged": [],
-        "error": [],
-    }
-
-    # Process files in parallel using ThreadPoolExecutor
-    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-        future_to_file = {
-            executor.submit(
-                download_file,
-                (file_path, remote_sha),
-                raw_base_url,
-                dest_path,
-                dir_prefix,
-            ): file_path
-            for file_path, remote_sha in files.items()
-        }
-
-        # Process completed tasks as they finish
-        for future in concurrent.futures.as_completed(future_to_file):
-            file_path = future_to_file[future]
-            try:
-                file_path, status = future.result()
-                files_processed[status].append(file_path)
-            except (requests.RequestException, IOError) as request_error:
-                print(f"Error processing {file_path}: {str(request_error)}")
-                files_processed["error"].append(file_path)
-
-    # Log summary
-    LOG.info("\nSync Summary:")
-    LOG.info(f"New files: {len(files_processed['new'])}")
-    LOG.info(f"Updated files: {len(files_processed['updated'])}")
-    LOG.info(f"Unchanged files: {len(files_processed['unchanged'])}")
-    if files_processed["error"]:
-        LOG.info(f"Failed files: {len(files_processed['error'])}")

src/axolotl/common/cli.py

@@ -23,7 +23,7 @@ class TrainerCliArgs:
 
     debug: bool = field(default=False)
     debug_text_only: bool = field(default=False)
-    debug_num_examples: int = field(default=0)
+    debug_num_examples: int = field(default=5)
     inference: bool = field(default=False)
     merge_lora: bool = field(default=False)
     prompter: Optional[str] = field(default=None)

src/axolotl/core/chat/format/chatml.py

@@ -1,34 +0,0 @@
-"""
-ChatML transformation functions for MessageContents
-"""
-from typing import Optional
-
-from ..messages import MessageContents, Messages
-from .shared import wrap_tools
-
-
-def format_message(
-    message: Messages,
-    message_index: Optional[int] = None,  # pylint: disable=unused-argument
-) -> Messages:
-    if message.is_chat_formatted:
-        return message
-
-    # prepend the role prefix within a MessageContents to message.content
-    message.content.insert(
-        0,
-        MessageContents(
-            type="text",
-            value=f"<|im_start|>{message.role}\n",
-            weight=0,
-        ),
-    )
-    message.content.append(
-        MessageContents(type="text", value="<|im_end|>", weight=message.weight)
-    )
-    message.content.append(MessageContents(type="text", value="\n", weight=0))
-
-    message = wrap_tools(message)
-
-    message.is_chat_formatted = True
-    return message

src/axolotl/core/chat/format/llama3x.py

@@ -1,45 +0,0 @@
-"""
-Llama 3.x chat formatting functions for MessageContents
-"""
-from typing import Optional
-
-from ..messages import MessageContents, Messages
-from .shared import wrap_tools
-
-
-def format_message(message: Messages, message_index: Optional[int] = None) -> Messages:
-    if message.is_chat_formatted:
-        return message
-
-    message_role = message.role
-    if message.role == "tool":
-        message_role = "ipython"
-
-    # prepend the role prefix within a MessageContents to message.content
-    message.content.insert(
-        0,
-        MessageContents(
-            type="text",
-            value=f"<|start_header_id|>{message_role}<|end_header_id|>\n\n",
-            weight=0,
-        ),
-    )
-
-    message.content.append(
-        MessageContents(type="text", value="<|eot_id|>", weight=message.weight)
-    )
-
-    message = wrap_tools(message)
-
-    if message_index == 0:
-        message.content.insert(
-            0,
-            MessageContents(
-                type="text",
-                value="<|begin_of_text|>",
-                weight=0,
-            ),
-        )
-
-    message.is_chat_formatted = True
-    return message

src/axolotl/core/chat/format/shared.py

@@ -1,47 +0,0 @@
-"""
-shared functions for format transforms
-"""
-from axolotl.core.chat.messages import MessageContents, Messages
-
-
-def wrap_tools(message: Messages):
-    # loop over message.content by index to find tool calls, we need to wrap each with tags,
-    # so be wary of indexing issues when changing the list while iterating.
-    # iterate over the range in reverse order to avoid index shifting
-    for i in range(len(message.content) - 1, -1, -1):
-        if message.content[i].type == "tool_call":
-            # append a </tool_call> MessageContents text tag after
-            message.content.insert(
-                i + 1,
-                MessageContents(
-                    type="text", value="</tool_call>\n", weight=message.weight
-                ),
-            )
-            # make sure the actual tool call content ends with a newline
-            message.content[i].has_newline = True
-            # prepend a <tool_call> MessageContents text tag before
-            message.content.insert(
-                i,
-                MessageContents(
-                    type="text", value="<tool_call>\n", weight=message.weight
-                ),
-            )
-        elif message.content[i].type == "tool_response":
-            # append a </tool_call> MessageContents text tag after
-            message.content.insert(
-                i + 1,
-                MessageContents(
-                    type="text", value="</tool_response>\n", weight=message.weight
-                ),
-            )
-            # make sure the actual tool response content ends with a newline
-            message.content[i].has_newline = True
-            # prepend a <tool_call> MessageContents text tag before
-            message.content.insert(
-                i,
-                MessageContents(
-                    type="text", value="<tool_response>\n", weight=message.weight
-                ),
-            )
-
-    return message

src/axolotl/core/chat/messages.py

@@ -1,230 +0,0 @@
-"""
-internal message representations of chat messages
-"""
-import json
-from enum import Enum
-from typing import Any, Callable, List, Optional, Union
-
-from pydantic import BaseModel
-from transformers import PreTrainedTokenizer
-
-
-class MessageRoles(str, Enum):
-    """
-    Message roles for the system, user, assistant, and tools
-    """
-
-    system = "system"  # pylint: disable=invalid-name
-    user = "user"  # pylint: disable=invalid-name
-    assistant = "assistant"  # pylint: disable=invalid-name
-    tool = "tool"  # pylint: disable=invalid-name
-    ipython = (  # pylint: disable=invalid-name
-        # for responses from builtin tools
-        "ipython"
-    )
-
-
-class MessageContentTypes(str, Enum):
-    """
-    Message content types for text, image, audio, tool calls, and tool responses
-    """
-
-    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
-    text = "text"  # pylint: disable=invalid-name
-    image = "image"  # pylint: disable=invalid-name
-    audio = "audio"  # pylint: disable=invalid-name
-    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
-    tool_response = "tool_response"  # pylint: disable=invalid-name
-
-
-class SpecialToken(str, Enum):
-    """
-    Special tokens for beginning of string and end of string
-    """
-
-    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
-    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
-
-
-class ToolCallFunction(BaseModel):
-    """
-    Tool call function with name and arguments
-    """
-
-    name: str
-    arguments: dict[str, str]
-
-
-class Tool(BaseModel):
-    """
-    Tool with description, function, and parameters
-    """
-
-    description: str
-    function: ToolCallFunction
-    parameters: dict[str, str]  # .properties
-
-
-class ToolCallContents(BaseModel):
-    """
-    Tool call contents with name, arguments, and optional id
-    """
-
-    name: str
-    arguments: dict[str, Union[str, int]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
-
-    def __str__(self) -> str:
-        data = {"name": self.name, "arguments": self.arguments}
-        if self.id is not None:
-            data["id"] = self.id
-        return json.dumps(data)
-
-
-class ToolResponseContents(BaseModel):
-    """
-    Tool response contents with name, content, and optional id
-    """
-
-    name: str
-    content: Union[str, dict[str, Union[str, int, float]]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
-
-    def __str__(self) -> str:
-        data = {"name": self.name, "content": self.content}
-        if self.id is not None:
-            data["id"] = self.id
-        return json.dumps(data)
-
-
-class MessageContents(BaseModel):
-    """
-    Message contents with type, value, metadata, weight, newline, and end of contents
-    """
-
-    type: Union[str, MessageContentTypes]
-    value: Union[str, ToolCallContents, ToolResponseContents, SpecialToken]
-    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
-    weight: Optional[Union[int, float]] = None
-    has_newline: bool = False
-    eoc: bool = False  # end of contents
-
-    def __str__(self) -> str:
-        str_val = str(self.value)
-        if self.has_newline and not str_val.endswith("\n"):
-            str_val += "\n"
-        return str_val
-
-
-class Messages(BaseModel):
-    """
-    Messages with role, content, metadata, weight, and chat formatting
-    """
-
-    role: Union[MessageRoles, str]  # allows for arbitrary roles
-    content: List["MessageContents"]
-    meta: Optional[dict[str, Any]] = None  # support additional arbitrary metadata
-    weight: Optional[Union[int, float]] = None
-    is_chat_formatted: bool = False
-
-    def __str__(self) -> str:
-        return "".join(str(c) for c in self.content)
-
-    def tokenized(
-        self, tokenizer: PreTrainedTokenizer, ignore_index=-100
-    ) -> dict[str, List[int]]:
-        # iterate over the contents, tokenizing the concatenated string values up to the current MessageContents
-        # returns a dictionary mapping w input_ids, attention_mask, and labels
-        input_ids: List[int] = []
-        labels: List[int] = []
-        pending_input_ids: List[int] = []
-        pending_weight = self.weight
-        running_content = ""
-        for _, msg_content in enumerate(self.content):
-            # TODO also handle non-text content types
-            if msg_content.type in [
-                MessageContentTypes.text.value,
-                MessageContentTypes.tool_call.value,
-                MessageContentTypes.tool_response.value,
-            ]:
-                running_content += str(msg_content)
-                tok_results = tokenizer(running_content, add_special_tokens=False)
-                tok_input_ids = tok_results["input_ids"]
-                if pending_input_ids:
-                    new_pending_inputs = tok_input_ids[
-                        len(input_ids) : len(input_ids) + len(pending_input_ids)
-                    ]
-                    if new_pending_inputs != pending_input_ids:
-                        # logging.warning("tokenization mismatch from concatenation.")
-                        pending_input_ids = new_pending_inputs
-                    input_ids.extend(pending_input_ids)
-                    if pending_weight:
-                        labels.extend(pending_input_ids)
-                    else:
-                        labels.extend([ignore_index] * len(pending_input_ids))
-                pending_input_ids = tok_results["input_ids"][len(input_ids) :]
-                pending_weight = self.weight and msg_content.weight not in [0, 0.0]
-        input_ids.extend(pending_input_ids)
-        if pending_weight:
-            labels.extend(pending_input_ids)
-        else:
-            labels.extend([ignore_index] * len(pending_input_ids))
-        attention_mask = [1] * len(input_ids)
-        return {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "labels": labels,
-        }
-
-
-class Chats(BaseModel):
-    """
-    top level data structure for chat conversations
-    """
-
-    conversation: List[Messages]
-
-    def __str__(self) -> str:
-        return "".join(str(c) for c in self.conversation)
-
-    def tokenized(
-        self, tokenizer: Callable[[str], dict[str, List[int]]], ignore_index=-100
-    ) -> dict[str, List[int]]:
-        input_ids = []
-        attention_mask = []
-        labels = []
-        for msg in self.conversation:
-            msg_results = msg.tokenized(tokenizer, ignore_index)
-            input_ids.extend(msg_results["input_ids"])
-            attention_mask.extend(msg_results["attention_mask"])
-            labels.extend(msg_results["labels"])
-        return {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "labels": labels,
-        }
-
-
-class ChatFormattedChats(Chats):
-    """
-    Chat formatted chats with formatter and optional train on inputs
-    """
-
-    formatter: Callable  # [[Union[dict, Chats]], Chats]
-    train_on_inputs: bool = False
-
-    def model_post_init(self, __context):
-        for i, msg in enumerate(self.conversation):
-            self.conversation[i] = self.formatter(msg, message_index=i)
-            if self.train_on_inputs:
-                self.conversation[i].weight = 1
-
-
-class PreferenceChats(BaseModel):
-    """
-    representation for preference data for chat
-    """
-
-    prompt: List[Messages]
-    chosen: Messages
-    rejected: Messages

src/axolotl/core/datasets/chat.py

@@ -1,55 +0,0 @@
-"""
-chat dataset module
-"""
-import os
-from typing import Callable, Optional, Union
-
-from datasets import Dataset
-from transformers import PreTrainedTokenizer
-
-from axolotl.core.chat.messages import ChatFormattedChats
-
-
-class TokenizedChatDataset(Dataset):
-    """
-    Tokenized chat dataset
-    """
-
-    def __init__(
-        self,
-        data: Dataset,
-        model_transform: Union[PreTrainedTokenizer, Callable],
-        *args,
-        message_transform: Optional[Callable] = None,
-        formatter=None,
-        process_count: Optional[int] = None,
-        keep_in_memory: Optional[bool] = False,
-        **kwargs,
-    ):
-        def map_fn(ex):
-            if message_transform is not None:
-                ex = message_transform(ex)
-            if formatter is not None:
-                ex = ChatFormattedChats(
-                    formatter=formatter,
-                    **ex,
-                )
-            else:
-                ex = ChatFormattedChats(
-                    **ex,
-                )
-            return ex.tokenized(model_transform)
-
-        process_or_cpu_count: int = (
-            process_count or os.cpu_count()  # type: ignore[assignment]
-        )
-        num_proc = min(64, process_or_cpu_count)
-        features = data.features.keys()
-        tokenized_data = data.map(
-            map_fn,
-            num_proc=num_proc,
-            keep_in_memory=keep_in_memory,
-            remove_columns=features,
-            desc="Tokenizing Chats",
-        )
-        super().__init__(tokenized_data.data, *args, **kwargs)

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -1,150 +0,0 @@
-"""
-This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
-"""
-from typing import Any, Mapping, Union
-
-
-def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
-    train_on_inputs=False,
-    conversations_field: str = "conversations",
-    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
-    message_field_content: Union[str, list[str]] = [
-        "value",
-        "text",
-        "content",
-    ],  # commonly "content"
-    message_field_training: Union[str, list[str]] = [
-        "train",
-        "weight",
-    ],  # commonly "weight"
-):
-    """Builds a transform that takes a row from the dataset and converts it to a Chat
-
-    Args:
-        train_on_inputs (bool, optional):
-            If True, the transform will train on the inputs. If False, the transform will train on the targets.
-            Defaults to False.
-        conversations_field (str, optional):
-            The field name of the conversations. Defaults to "conversations".
-        message_field_role (str | list[str], optional):
-            The field name of the role. Defaults to "role".
-        message_field_content (str | list[str], optional):
-            The field name of the message content. Defaults to "content".
-        message_field_training (str | list[str], optional):
-            The field name of the train/weight. Defaults to "weight".
-
-    Returns:
-        Callable:
-            A function that takes a list of conversations and returns a list of messages.
-    """
-
-    message_field_role = (
-        [message_field_role]
-        if isinstance(message_field_role, str)
-        else message_field_role
-    )
-    message_field_content = (
-        [message_field_content]
-        if isinstance(message_field_content, str)
-        else message_field_content
-    )
-    message_weight_fields = (
-        [message_field_training]
-        if isinstance(message_field_training, str)
-        else message_field_training
-    )
-
-    role_value_mappings = {
-        "system": "system",
-        "user": "user",
-        "human": "user",
-        "assistant": "assistant",
-        "gpt": "assistant",
-        "tool": "tool",
-        "ipython": "ipython",
-    }
-    if train_on_inputs:
-        role_default_weights_mappings = {
-            "system": 1,
-            "user": 1,
-            "assistant": 1,
-            "tool": 1,
-            "ipython": 1,
-        }
-    else:
-        role_default_weights_mappings = {
-            "system": 0,
-            "user": 0,
-            "assistant": 1,
-            "tool": 0,
-            "ipython": 0,
-        }
-
-    def transform_builder(sample: Mapping[str, Any]):
-        if conversations_field not in sample:
-            raise ValueError(f"Field '{conversations_field}' not found in sample.")
-        # if none of the role fields are in the message, raise an error
-        if not any(
-            role in sample[conversations_field][0] for role in message_field_role
-        ):
-            raise ValueError("No role field found in message.")
-        role_field = next(
-            role
-            for role in message_field_role
-            if role in sample[conversations_field][0]
-        )
-        if not any(
-            field in sample[conversations_field][0] for field in message_field_content
-        ):
-            raise ValueError("No message_content field found in message.")
-        message_content_field = next(
-            field
-            for field in message_field_content
-            if field in sample[conversations_field][0]
-        )
-        if not any(
-            field in sample[conversations_field][0] for field in message_field_training
-        ):
-            message_weight_field = None
-        else:
-            message_weight_field = next(
-                field
-                for field in message_weight_fields
-                if field in sample[conversations_field][0]
-            )
-
-        messages = []
-        for message in sample[conversations_field]:
-            role = role_value_mappings[message[role_field]]
-            weight = (
-                int(message[message_weight_field])
-                if message_weight_field
-                else role_default_weights_mappings[role]
-            )
-
-            # TODO if "tool_calls" in message[message_content_field]: then convert tool call to ToolCallContents
-            if isinstance(message[message_content_field], str):
-                messages.append(
-                    {
-                        "role": role,
-                        "content": [
-                            {
-                                "type": "text",
-                                "value": message[message_content_field],
-                            }
-                        ],
-                        "weight": weight,
-                    }
-                )
-            else:
-                messages.append(
-                    {
-                        "role": role,
-                        "content": message[message_content_field],
-                        "weight": weight,
-                    }
-                )
-
-        return {"conversation": messages}
-
-    return transform_builder

src/axolotl/core/tokenizer_utils.py

@@ -3,88 +3,36 @@ helper functions for fixing the embeddings/tokenizer
 """
 
 # Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
-# GNU LESSER GENERAL PUBLIC LICENSE
-# Version 3, 29 June 2007
 #
-# Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+# Licensed under the Apache License, Version 2.0 (the "License");
-# Everyone is permitted to copy and distribute verbatim copies
+# you may not use this file except in compliance with the License.
-# of this license document, but changing it is not allowed.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import gc
 import itertools
-import logging
-from collections import Counter
 
-import datasets
 import numpy as np
 import torch
 
-LOG = logging.getLogger("axolotl.core.tokenizer_utils")
 
-
+@torch.inference_mode
-@torch.inference_mode()
+def fix_untrained_tokens(model, tokenizer, train_dataset, eps=1e-16):
-def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
-    model, tokenizer, train_dataset, ignored_tokenizer_names=None, eps=1e-16
-):
     """
-    Llama-3 for eg has untrained vectors in the base model.
+    Many of the newer models have reserved tokens that are not trained.
-    These include <|eot_id|>, <|start_header_id|>, <|end_header_id|>
-    We reset them to the mean of the rest of the tokens
     """
-    # Code licensed under LGPL
     embedding_matrix = model.get_input_embeddings().weight
     lm_head_matrix = model.get_output_embeddings().weight
-    chat_template = getattr(tokenizer, "chat_template", None)
-    tokenizer = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer
-
-    # Ignore some model checks for now
-    if not ignored_tokenizer_names:
-        ignored_tokenizer_names = []
-    if (
-        model.config._name_or_path  # pylint: disable=protected-access
-        in ignored_tokenizer_names
-    ):
-        return
-
-    # Sometimes the sizes can be different like in vision models
-    # Ie <image> is in input, but not in output
-    min_size = min(embedding_matrix.shape[1], lm_head_matrix.shape[1])
-    embedding_matrix = embedding_matrix[:, :min_size]
-    lm_head_matrix = lm_head_matrix[:, :min_size]
 
     # Get untrained tokens
-    indicator_untrained1 = torch.amax(embedding_matrix, axis=1) <= eps
+    indicator_untrained = torch.amax(embedding_matrix, axis=1) <= eps
-    # Check lm_head as well
-
-    # Does NOT work for Llama 3.1!!
-    indicator_untrained2 = torch.amax(lm_head_matrix, axis=1) <= eps
-
-    # We instead check for repeated vectors
-    lm_head_where = torch.where(indicator_untrained1)[0]
-    lm_head_bad = lm_head_matrix[lm_head_where]
-    lm_head_bad = lm_head_bad.cpu().float().numpy().round(3)
-    counter = Counter()
-    for row in lm_head_bad:
-        counter[hash(row.data.tobytes())] += 1
-    counter = Counter({k: c for k, c in counter.items() if c >= 2})
-
-    lm_head_where = lm_head_where.cpu().numpy()
-    final_bad_lm_head = []
-    for j, row in enumerate(lm_head_bad):
-        if hash(row.data.tobytes()) in counter:
-            final_bad_lm_head.append(lm_head_where[j])
-    indicator_untrained2 = indicator_untrained2 | torch.zeros_like(indicator_untrained2)
-    indicator_untrained2[final_bad_lm_head] = True
-
-    # Combine both checks
-    indicator_untrained = indicator_untrained1 & indicator_untrained2
-
-    # Remove pad token possibility
-    if hasattr(tokenizer, "pad_token_id"):
-        pad_token_id = tokenizer.pad_token_id
-        if pad_token_id is not None and pad_token_id < indicator_untrained.shape[0]:
-            indicator_untrained[pad_token_id] = False
-
     where_untrained = torch.where(indicator_untrained)[0]
     n_untrained = where_untrained.shape[0]
     n_trained = embedding_matrix.shape[0] - n_untrained
@@ -92,9 +40,10 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     # Get set and actual tokens
     where_untrained = where_untrained.tolist()
     if len(where_untrained) == 0:
-        return
+        return False
 
     # Remove untrained indices where it's longer
+
     where_untrained_set = frozenset(where_untrained)
     actual_bad_tokens = tokenizer.convert_ids_to_tokens(where_untrained)
     # Remove None items in actual_bad_tokens
@@ -104,14 +53,10 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     if_bad_first = False
     if_bad_second = False
     # Check tokenizer's chat template for any untrained tokens
+    chat_template = getattr(tokenizer, "chat_template", None)
     if chat_template is not None:
         if_bad_first = any(x in chat_template for x in actual_bad_tokens)
 
-    if isinstance(train_dataset, datasets.IterableDataset):
-        # Skip the check, since the code below assumes
-        # an indexable dataset
-        return
-
     # Check the first 250, last 250 input_ids
     size_dataset = len(train_dataset)
     size = min(size_dataset, 250)
@@ -138,69 +83,7 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
 
     # Check if bad tokens exists!
     if not if_bad_first and not if_bad_second:
-        return
+        return False
-
-    # Check if lm_head / embed_token are trainable!
-    bad_not_trainable = False
-    if not embedding_matrix.requires_grad:
-        bad_not_trainable = True
-    if not lm_head_matrix.requires_grad:
-        bad_not_trainable = True
-
-    if bad_not_trainable:  # pylint: disable=too-many-nested-blocks
-        final_bad_items = []
-
-        # Re-check the first 250, last 250 input_ids
-        size_dataset = len(train_dataset)
-        size = min(size_dataset, 250)
-        for j in range(size):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # Re-check last 250
-        left = max(size_dataset - 250, 0)
-        for j in range(left, size_dataset):
-            input_ids = train_dataset[j]
-            if "input_ids" in input_ids:
-                input_ids = input_ids["input_ids"]
-                for item in input_ids:
-                    if item in where_untrained_set:
-                        final_bad_items.append(item)
-
-        # If no bad tokens, possibly chat template itself has issues?
-        if len(final_bad_items) == 0:
-            # Recheck 2000 and last 2000 items
-            size_dataset = len(train_dataset)
-            size = min(size_dataset, 2000)
-            for j in range(size):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Re-check last 2000
-            left = max(size_dataset - 2000, 0)
-            for j in range(left, size_dataset):
-                input_ids = train_dataset[j]
-                if "input_ids" in input_ids:
-                    input_ids = input_ids["input_ids"]
-                    for item in input_ids:
-                        if item in where_untrained_set:
-                            final_bad_items.append(item)
-
-            # Most likely false signal!
-            if len(final_bad_items) == 0:
-                return
-
-        raise ValueError(
-            f"Untrained tokens of [{list(set(final_bad_items))}] found, but embed_tokens & lm_head not trainable, causing NaNs. "
-        )
 
     # Count all the possible bad tokens
     final_counts = np.zeros(
@@ -214,23 +97,6 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
 
     train_dataset.map(mapping, batched=True, desc="Counting untrained tokens")
 
-    # Get counts for untrained tokens
-    counts_untrained = final_counts[where_untrained]
-    # Identify untrained tokens seen in train_dataset
-    indices_seen_in_train = np.where(counts_untrained > 0)[0]
-    tokens_to_update = [where_untrained[i] for i in indices_seen_in_train]
-
-    if len(tokens_to_update) == 0:
-        LOG.info(
-            "No untrained tokens found in train_dataset. No embeddings were modified."
-        )
-        return
-
-    # Log the token IDs that are being rescaled
-    LOG.info(
-        f"Rescaling embeddings for tokens seen in train_dataset: {tokens_to_update}"
-    )
-
     # Get sum of all items
     sum_embedding = torch.sum(embedding_matrix, dtype=torch.float32, axis=0)
     sum_lm_head = torch.sum(lm_head_matrix, dtype=torch.float32, axis=0)
@@ -247,26 +113,38 @@ def fix_untrained_tokens(  # pylint: disable=too-many-return-statements
     mean_embedding = sum_embedding / n_trained
     mean_lm_head = sum_lm_head / n_trained
 
-    # Compute scaling for tokens to update
+    # Scale each to be equal to 1/max_frequency. Also set some to 0 if none seen
-    scaling = counts_untrained[indices_seen_in_train] / max(final_counts.max(), 1)
+    scaling = final_counts[where_untrained] / max(final_counts.max(), 1)
     scaling = torch.tensor(scaling, device=mean_embedding.device).unsqueeze(1)
+    mean_embedding = (
+        mean_embedding.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    mean_lm_head = (
+        mean_lm_head.repeat(
+            (
+                n_untrained,
+                1,
+            )
+        )
+        * scaling
+    )
+    where_null = scaling.ravel() == 0
+    mean_embedding[where_null] = 0
+    mean_lm_head[where_null] = 0
 
-    # Prepare mean embeddings for tokens to update
+    # Set them to the mean
-    mean_embedding_repeated = (
+    embedding_matrix[where_untrained] = mean_embedding.to(embedding_matrix.dtype)
-        mean_embedding.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
+    lm_head_matrix[where_untrained] = mean_lm_head.to(lm_head_matrix.dtype)
-    )
-    mean_lm_head_repeated = (
-        mean_lm_head.unsqueeze(0).repeat(len(tokens_to_update), 1) * scaling
-    )
-
-    # Update embeddings only for tokens seen in train_dataset
-    embedding_matrix[tokens_to_update] = mean_embedding_repeated.to(
-        embedding_matrix.dtype
-    )
-    lm_head_matrix[tokens_to_update] = mean_lm_head_repeated.to(lm_head_matrix.dtype)
 
     # Clean up
     for _ in range(3):
         gc.collect()
         torch.cuda.empty_cache()
-    return
+
+    return True

src/axolotl/core/trainers/trl.py

@@ -40,7 +40,7 @@ class TRLPPOTrainer(PPOTrainer):
                 query_tensors,
                 return_prompt=False,
                 generate_ref_response=True,
-                **generation_kwargs,
+                **generation_kwargs
             )
             batch["response"] = self.tokenizer.batch_decode(response_tensors)
             batch["ref_response"] = self.tokenizer.batch_decode(ref_response_tensors)

src/axolotl/integrations/LICENSE.md

@@ -1,58 +0,0 @@
-### AXOLOTL COMMUNITY LICENSE AGREEMENT
-
-This Axolotl Community License Agreement (“Agreement”) is entered into by and between Axolotl AI Corp. (“Axolotl”) and
-any individual or entity (“Licensee”) who wishes to use the Software (as defined below) in accordance with the terms
-and conditions set forth in this Agreement.
-
-1.  Definitions
-    1.1 “Licensee” refers to any individual or entity who has obtained a copy of the Software under this Agreement.
-    1.2 “Plugin Integration” means independent integration software modules which may or may not be offered by Axolotl,
-        which may be licensed separately by their respective  authors and/or licensors.
-    1.3 “Software” refers to the specific sub-directory of the Axolotl, Inc. software located at
-        https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations and its subdirectories which
-        permits Plugin Integrations to integrate with the Axolotl service.
-2.  Grant of License
-    2.1	Axolotl hereby grants Licensee a worldwide, non-exclusive, royalty-free, license to use, copy, modify, merge,
-        publish, distribute, sublicense, and/or otherwise exploit the Software, subject to the following conditions:
-        - Licensee must comply with all the terms and conditions of this Agreement.
-        - Licensee must include the original copyright notice and disclaimer of warranty in all copies or substantial
-          portions of the Software.
-    2.2 Licensee may use the Software for any lawful purpose, except as restricted in Section 3.
-3.  Restrictions
-    3.1 Licensee shall not use the Software for any activity that constitutes a commercial activity of offering for
-        free or for sale any services, platform, or equivalent  to third parties for the purposes of allowing such
-        third parties to fine-tune artificial intelligence models.
-    3.2 Licensee shall not:
-        - Use the Software for any illegal or unauthorized purpose.
-        - Reverse engineer, decompile, or disassemble the Software.
-        - Remove or modify any copyright, trademark, or other proprietary notices contained in the Software.
-        - Use the Software in a way that could damage, disable, overburden, or impair the functionality of the
-          Software or interfere with any third-party use of the Software.
-    3.3 Axolotl reserves the right to restrict certain Plugin Integrations for use with the Software. To the extent Licensee integrates a permitted, applicable Plugin Integration with the Software, Licensee shall comply with any additional terms and conditions imposed by the licensors of such Plugin Integration for use of such Plugin Integrations. Licensee shall contact Axolotl if it has questions about whether its use of the Software falls beyond the scope of this Agreement.
-4.  Intellectual Property Rights
-    4.1 Axolotl and its contributors retain all intellectual property rights in and to the Software. Licensee
-        acknowledges that this Agreement does not transfer any ownership rights or intellectual property rights to
-        Licensee.
-5.  Disclaimer of Warranty
-    5.1 THE SOFTWARE IS PROVIDED “AS IS,” WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
-        TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. IN NO EVENT SHALL
-        THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF
-        CONTRACT, TORT, OR OTHERWISE, ARISING FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-        DEALINGS IN THE SOFTWARE.
-6.  Termination
-    6.1 Axolotl may terminate this Agreement at any time if Licensee fails to comply with any of the terms and
-        conditions set forth herein. Upon termination, Licensee shall cease all use of the Software and destroy any
-        copies in its possession.
-7.  Governing Law
-    7.1 This Agreement shall be governed by and construed in accordance with the laws of the State of California,
-        without regards to conflicts of laws provisions thereof.
-8.  Entire Agreement
-    8.1 This Agreement constitutes the entire agreement between Axolotl and Licensee with respect to the subject matter
-        hereof and supersedes all prior or contemporaneous understandings or agreements between the parties concerning
-        the Software, whether written or oral. Axolotl may update the terms of this Agreement from time to time, and
-        Licensee’s continued use of the Software after any such updates shall constitute acceptance of updated terms
-        on a go-forward basis.  Axolotl will use commercially reasonable efforts to provide Licensee notice of any
-        material updates. By using the Software, Licensee acknowledges that it has read, understood, and agrees to be
-        bound by the terms and conditions of this Agreement.
-
-This Agreement was last updated on August 23, 2024.

src/axolotl/integrations/base.py

@@ -1,432 +0,0 @@
-# Copyright 2024 Axolotl AI. All rights reserved.
-#
-# This software may be used and distributed according to
-# the terms of the Axolotl Community License Agreement (the "License");
-# you may not use this file except in compliance with the License.
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-# License for the specific language governing permissions and limitations under
-# the License.
-
-"""
-Base class for all plugins.
-
-A plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.
-Plugins can be used to integrate third-party models, modify the training process, or add new features.
-
-To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
-"""
-import collections
-import importlib
-import logging
-from typing import OrderedDict
-
-
-class BasePlugin:
-    """
-    Base class for all plugins. Defines the interface for plugin methods.
-
-    Attributes:
-    None
-
-    Methods:
-    register(cfg): Registers the plugin with the given configuration.
-    pre_model_load(cfg): Performs actions before the model is loaded.
-    post_model_load(cfg, model): Performs actions after the model is loaded.
-    pre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.
-    post_lora_load(cfg, model): Performs actions after LoRA weights are loaded.
-    create_optimizer(cfg, trainer): Creates and returns an optimizer for training.
-    create_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.
-    add_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.
-    add_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.
-    """
-
-    def __init__(self):
-        """
-        Initializes the BasePlugin.
-        """
-
-    def register(self, cfg):  # pylint: disable=unused-argument
-        """
-        Registers the plugin with the given configuration.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-
-        Returns:
-        None
-        """
-
-    def get_input_args(self):
-        """
-        Returns a pydantic model for the plugin's input arguments.
-        """
-
-    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
-        """
-        Performs actions before the model is loaded.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-
-        Returns:
-        None
-        """
-
-    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
-        """
-        Performs actions after the model is loaded.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-
-    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
-        """
-        Performs actions before LoRA weights are loaded.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-
-    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
-        """
-        Performs actions after LoRA weights are loaded.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-
-    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
-        """
-        Creates and returns an optimizer for training.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
-
-        Returns:
-        object: The created optimizer.
-        """
-
-    def create_lr_scheduler(
-        self, cfg, trainer, optimizer
-    ):  # pylint: disable=unused-argument
-        """
-        Creates and returns a learning rate scheduler.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
-        optimizer (object): The optimizer for training.
-
-        Returns:
-        object: The created learning rate scheduler.
-        """
-
-    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
-        """
-        setup callbacks before creating the trainer.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        model (object): The loaded model.
-
-        Returns:
-        List[callable]: A list of callback functions to be added to the TrainingArgs
-        """
-        return []
-
-    def add_callbacks_post_trainer(
-        self, cfg, trainer
-    ):  # pylint: disable=unused-argument
-        """
-        Adds callbacks to the trainer after creating the trainer.
-        This is useful for callbacks that require access to the model or trainer.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-        trainer (object): The trainer object for training.
-
-        Returns:
-        List[callable]: A list of callback functions to be added
-        """
-        return []
-
-    def post_train(self, cfg, model):  # pylint: disable=unused-argument
-        """
-        Performs actions after training is complete.
-
-        Parameters:
-        cfg (dict): The axolotl configuration
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-
-    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
-        """
-        Performs actions after training is complete and the model is unloaded.
-
-        Parameters:
-        cfg (dict): The configuration for the plugin.
-
-        Returns:
-        None
-        """
-
-
-def load_plugin(plugin_name: str) -> BasePlugin:
-    """
-    Loads a plugin based on the given plugin name.
-
-    The plugin name should be in the format "module_name.class_name".
-    This function splits the plugin name into module and class, imports the module,
-    retrieves the class from the module, and creates an instance of the class.
-
-    Parameters:
-    plugin_name (str): The name of the plugin to be loaded. The name should be in the format "module_name.class_name".
-
-    Returns:
-    BasePlugin: An instance of the loaded plugin.
-
-    Raises:
-    ImportError: If the plugin module cannot be imported.
-    """
-    # split the plugin name into module and class
-    module_name, class_name = plugin_name.rsplit(".", 1)
-
-    # import the module
-    module = importlib.import_module(module_name)
-    # instantiate the class
-    plugin_class = getattr(module, class_name)
-    # create an instance of the class
-    plugin = plugin_class()
-
-    return plugin
-
-
-class PluginManager:
-    """
-    The PluginManager class is responsible for loading and managing plugins.
-    It should be a singleton so it can be accessed from anywhere in the codebase.
-
-    Attributes:
-    plugins (List[BasePlugin]): A list of loaded plugins.
-
-    Methods:
-    get_instance(): Static method to get the singleton instance of PluginManager.
-    register(plugin_name: str): Registers a new plugin by its name.
-    pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
-    """
-
-    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
-
-    _instance = None
-
-    def __new__(cls):
-        """
-        Creates a new instance of PluginManager if it doesn't exist yet.
-        """
-        if cls._instance is None:
-            cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins = collections.OrderedDict()
-        return cls._instance
-
-    @staticmethod
-    def get_instance() -> "PluginManager":
-        """
-        Returns the singleton instance of PluginManager.
-        If the instance doesn't exist, it creates a new one.
-        """
-        if PluginManager._instance is None:
-            PluginManager()
-        return PluginManager._instance  # type: ignore
-
-    def register(self, plugin_name: str):
-        """
-        Registers a new plugin by its name.
-
-        Parameters:
-        plugin_name (str): The name of the plugin to be registered.
-
-        Returns:
-        None
-
-        Raises:
-        ImportError: If the plugin module cannot be imported.
-        """
-        try:
-            plugin = load_plugin(plugin_name)
-            self.plugins[plugin_name] = plugin
-        except ImportError:
-            logging.error(f"Failed to load plugin: {plugin_name}")
-
-    def get_input_args(self):
-        """
-        Returns a list of Pydantic classes for all registered plugins' input arguments.'
-
-        Returns:
-        list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
-        """
-        input_args = []
-        for plugin in self.plugins.values():
-            input_args_from_plugin = plugin.get_input_args()
-            if input_args_from_plugin is not None:
-                input_args.append(input_args_from_plugin)
-        return input_args
-
-    def pre_model_load(self, cfg):
-        """
-        Calls the pre_model_load method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-
-        Returns:
-        None
-        """
-        for plugin in self.plugins.values():
-            plugin.pre_model_load(cfg)
-
-    def post_model_load(self, cfg, model):
-        """
-        Calls the post_model_load method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-        for plugin in self.plugins.values():
-            plugin.post_model_load(cfg, model)
-
-    def pre_lora_load(self, cfg, model):
-        """
-        Calls the pre_lora_load method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-        for plugin in self.plugins.values():
-            plugin.pre_lora_load(cfg, model)
-
-    def post_lora_load(self, cfg, model):
-        """
-        Calls the post_lora_load method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-        for plugin in self.plugins.values():
-            plugin.post_lora_load(cfg, model)
-
-    def create_optimizer(self, cfg, trainer):
-        """
-        Calls the create_optimizer method of all registered plugins and returns the first non-None optimizer.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        trainer (object): The trainer object for training.
-
-        Returns:
-        object: The created optimizer, or None if none was found.
-        """
-        for plugin in self.plugins.values():
-            optimizer = plugin.create_optimizer(cfg, trainer)
-            if optimizer is not None:
-                return optimizer
-        return None
-
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
-        """
-        Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        trainer (object): The trainer object for training.
-        optimizer (object): The optimizer for training.
-
-        Returns:
-        object: The created learning rate scheduler, or None if none was found.
-        """
-        for plugin in self.plugins.values():
-            scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
-            if scheduler is not None:
-                return scheduler
-        return None
-
-    def add_callbacks_pre_trainer(self, cfg, model):
-        """
-        Calls the add_callbacks_pre_trainer method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        model (object): The loaded model.
-
-        Returns:
-        List[callable]: A list of callback functions to be added to the TrainingArgs.
-        """
-        callbacks = []
-        for plugin in self.plugins.values():
-            plugin_callbacks = plugin.add_callbacks_pre_trainer(cfg, model)
-            if plugin_callbacks:  # if the plugin returned a list of callbacks
-                callbacks.extend(plugin_callbacks)
-        return callbacks
-
-    def add_callbacks_post_trainer(self, cfg, trainer):
-        """
-        Calls the add_callbacks_post_trainer method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        trainer (object): The trainer object for training.
-
-        Returns:
-        List[callable]: A list of callback functions to be added to the TrainingArgs.
-        """
-        callbacks = []
-        for plugin in self.plugins.values():
-            plugin_callbacks = plugin.add_callbacks_post_trainer(cfg, trainer)
-            if plugin_callbacks:
-                callbacks.extend(plugin_callbacks)
-        return callbacks
-
-    def post_train_unload(self, cfg):
-        """
-        Calls the post_train_unload method of all registered plugins.
-
-        Parameters:
-        cfg (dict): The configuration for the plugins.
-        model (object): The loaded model.
-
-        Returns:
-        None
-        """
-        for plugin in self.plugins.values():
-            plugin.post_train_unload(cfg)