use deterministic seed for random LISA layers

2024-04-04 18:16:55 -07:00
384 changed files with 5189 additions and 26495 deletions
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@@ -21,12 +21,12 @@ All contributors are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT

 ## Getting Started

-Bugs? Please check for open issue else create a new [Issue](https://github.com/axolotl-ai-cloud/axolotl/issues/new).
+Bugs? Please check for open issue else create a new [Issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues/new).

 PRs are **greatly welcome**!

 1. Fork the repository and clone it to your local machine.
-2. Set up the development environment by following the instructions in the [README.md](https://github.com/axolotl-ai-cloud/axolotl/tree/main/README.md) file.
+2. Set up the development environment by following the instructions in the [README.md](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main/README.md) file.
 3. Explore the codebase, run tests, and verify that everything works as expected.

 Please run below to setup env
@@ -42,11 +42,11 @@ pytest tests/

 ### Reporting Bugs

-If you encounter a bug or issue while using axolotl, please open a new issue on the [GitHub Issues](https://github.com/axolotl-ai-cloud/axolotl/issues) page. Provide a clear and concise description of the problem, steps to reproduce it, and any relevant error messages or logs.
+If you encounter a bug or issue while using axolotl, please open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Provide a clear and concise description of the problem, steps to reproduce it, and any relevant error messages or logs.

 ### Suggesting Enhancements

-We welcome ideas for improvements and new features. To suggest an enhancement, open a new issue on the [GitHub Issues](https://github.com/axolotl-ai-cloud/axolotl/issues) page. Describe the enhancement in detail, explain the use case, and outline the benefits it would bring to the project.
+We welcome ideas for improvements and new features. To suggest an enhancement, open a new issue on the [GitHub Issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues) page. Describe the enhancement in detail, explain the use case, and outline the benefits it would bring to the project.

 ### Submitting Pull Requests

--- a/.github/ISSUE_TEMPLATE/bug-report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yaml
@@ -15,7 +15,7 @@ body:
      label: "Please check that this issue hasn't been reported before."
      description: "The **Label filters** may help make your search more focussed."
      options:
-        - label: "I searched previous [Bug Reports](https://github.com/axolotl-ai-cloud/axolotl/labels/bug) didn't find any similar reports."
+        - label: "I searched previous [Bug Reports](https://github.com/OpenAccess-AI-Collective/axolotl/labels/bug) didn't find any similar reports."
          required: true

  - type: textarea
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,7 +1,7 @@
 blank_issues_enabled: false
 contact_links:
  - name: Ask a question
-    url: https://github.com/axolotl-ai-cloud/axolotl/discussions/categories/q-a
+    url: https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/q-a
    about: Ask questions and discuss with other community members
  - name: Discuss the Project in Discord
    url: https://discord.gg/HhrNrHJPRb
--- a/.github/ISSUE_TEMPLATE/docs.yml
+++ b/.github/ISSUE_TEMPLATE/docs.yml
@@ -10,7 +10,7 @@ body:
      value: |
        * Ask questions in [Discord](https://discord.gg/HhrNrHJPRb).
        * Before you file an issue read the [Contributing guide](./CONTRIBUTING.md).
-        * Check to make sure someone hasn't already opened a [similar issue](https://github.com/axolotl-ai-cloud/axolotl/issues).
+        * Check to make sure someone hasn't already opened a [similar issue](https://github.com/OpenAccess-AI-Collective/axolotl/issues).
  - type: textarea
    attributes:
      label: What piece of documentation is affected?
--- a/.github/ISSUE_TEMPLATE/feature-request.yaml
+++ b/.github/ISSUE_TEMPLATE/feature-request.yaml
@@ -8,9 +8,9 @@ body:
      label: "⚠️ Please check that this feature request hasn't been suggested before."
      description: "There are two locations for previous feature requests. Please search in both. Thank you. The **Label filters** may help make your search more focussed."
      options:
-        - label: "I searched previous [Ideas in Discussions](https://github.com/axolotl-ai-cloud/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
+        - label: "I searched previous [Ideas in Discussions](https://github.com/OpenAccess-AI-Collective/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
          required: true
-        - label: "I searched previous [Issues](https://github.com/axolotl-ai-cloud/axolotl/labels/enhancement) didn't find any similar feature requests."
+        - label: "I searched previous [Issues](https://github.com/OpenAccess-AI-Collective/axolotl/labels/enhancement) didn't find any similar feature requests."
          required: true

  - type: textarea
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -1,74 +1,52 @@
 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:
  build-base:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
    runs-on: axolotl-gpu-runner
    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: "121"
-            cuda_version: 12.1.1
-            cudnn_version: 8
+          - cuda: "118"
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
+            pytorch: 2.1.2
            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
          - cuda: "121"
-            cuda_version: 12.1.1
-            cudnn_version: 8
-            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: ""
+            cuda_version: 12.1.0
            python_version: "3.10"
-            pytorch: 2.4.1
+            pytorch: 2.1.2
            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: ""
+          - cuda: "121"
+            cuda_version: 12.1.0
            python_version: "3.11"
-            pytorch: 2.4.1
+            pytorch: 2.1.2
            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: ""
+          - cuda: "121"
+            cuda_version: 12.1.0
            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.2.1
            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: |
-            winglian/axolotl-base
-            axolotlai/axolotl-base
+          images: winglian/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:
@@ -79,7 +57,6 @@ jobs:
          labels: ${{ steps.metadata.outputs.labels }}
          build-args: |
            CUDA_VERSION=${{ matrix.cuda_version }}
-            CUDNN_VERSION=${{ matrix.cudnn_version }}
            CUDA=${{ matrix.cuda }}
            PYTHON_VERSION=${{ matrix.python_version }}
            PYTORCH_VERSION=${{ matrix.pytorch }}
--- a/.github/workflows/docs.yml
+++ b/.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
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -1,14 +1,12 @@
 name: lint
 on:
  # check on PRs, and manual triggers
-  merge_group:
  pull_request:
      paths:
       - '**.py'
       - 'requirements.txt'
       - '.github/workflows/*.yml'
-       - "*.[q]md"
-       - "examples/**/*.y[a]?ml"
+       - "*.md"
  workflow_dispatch:

 jobs:
@@ -16,9 +14,9 @@ jobs:
    name: pre-commit
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v3
+      - 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
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -4,38 +4,32 @@ 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: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 118
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
-            axolotl_extras: mamba-ssm
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            axolotl_extras: mamba-ssm
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.1.2
            axolotl_extras:
+            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
            is_latest: true
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.2
+            axolotl_extras:
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.11"
+            pytorch: 2.2.1
+            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
      - name: Checkout
@@ -44,12 +38,7 @@ jobs:
        id: metadata
        uses: docker/metadata-action@v5
        with:
-          images: |
-            winglian/axolotl
-            axolotlai/axolotl
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
+          images: winglian/axolotl
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Login to Docker Hub
@@ -63,7 +52,7 @@ jobs:
        with:
          context: .
          build-args: |
-            BASE_TAG=${{ 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 }}
@@ -71,38 +60,32 @@ jobs:
          push: ${{ github.event_name != 'pull_request' }}
          tags: |
            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
            ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
          labels: ${{ steps.metadata.outputs.labels }}

  build-axolotl-cloud:
    needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
    # this job needs to be run on self-hosted GPU runners...
    strategy:
      matrix:
        include:
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 118
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.1.2
            axolotl_extras:
            is_latest: true
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.2
+            axolotl_extras:
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.11"
+            pytorch: 2.2.1
+            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
      - name: Checkout
@@ -111,25 +94,20 @@ jobs:
        id: metadata
        uses: docker/metadata-action@v5
        with:
-          images: |
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
-          tags: |
-            type=ref,event=branch
-            type=pep440,pattern={{version}}
+          images: winglian/axolotl-cloud
      - 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' }}
@@ -137,50 +115,3 @@ jobs:
             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
          labels: ${{ steps.metadata.outputs.labels }}
-
-  build-axolotl-cloud-no-tmux:
-    needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    # this job needs to be run on self-hosted GPU runners...
-    strategy:
-      matrix:
-        include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            axolotl_extras:
-    runs-on: axolotl-gpu-runner
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            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: ${{ secrets.DOCKERHUB_TOKEN }}
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Build
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          build-args: |
-            BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            CUDA=${{ matrix.cuda }}
-          file: ./docker/Dockerfile-cloud-no-tmux
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: |
-             ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-             ${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
-          labels: ${{ steps.metadata.outputs.labels }}
--- a/.github/workflows/multi-gpu-e2e.yml
+++ b/.github/workflows/multi-gpu-e2e.yml
@@ -1,67 +0,0 @@
-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: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
-            axolotl_extras:
-            num_gpus: 2
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 120
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.multigpu
--- a/.github/workflows/nightlies.yml
+++ b/.github/workflows/nightlies.yml
@@ -7,31 +7,27 @@ 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 == 'OpenAccess-AI-Collective' }}
    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 118
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
+            pytorch: 2.1.2
+            axolotl_extras:
+            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
+            is_latest: true
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.2
            axolotl_extras:
          - cuda: 121
-            cuda_version: 12.1.1
+            cuda_version: 12.1.0
            python_version: "3.11"
-            pytorch: 2.3.1
-            axolotl_extras:
-            is_latest: true
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.2.1
            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
@@ -41,9 +37,7 @@ jobs:
        id: metadata
        uses: docker/metadata-action@v5
        with:
-          images: |
-            winglian/axolotl
-            axolotlai/axolotl
+          images: winglian/axolotl
          tags: |
            type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
      - name: Set up Docker Buildx
@@ -71,31 +65,26 @@ 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 == 'OpenAccess-AI-Collective' }}
    # this job needs to be run on self-hosted GPU runners...
    strategy:
      matrix:
        include:
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 118
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.1.2
            axolotl_extras:
            is_latest: true
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.2
            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 121
+            cuda_version: 12.1.0
            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.2.1
            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
@@ -105,9 +94,7 @@ jobs:
        id: metadata
        uses: docker/metadata-action@v5
        with:
-          images: |
-            winglian/axolotl-cloud
-            axolotlai/axolotl-cloud
+          images: winglian/axolotl-cloud
          tags: |
            type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
      - name: Login to Docker Hub
@@ -116,7 +103,7 @@ 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:
--- a/.github/workflows/pypi.yml
+++ b/.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 -r requirements-dev.txt -r requirements-tests.txt
+          pip3 install -e .
+          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
--- a/.github/workflows/tests-nightly.yml
+++ b/.github/workflows/tests-nightly.yml
@@ -1,144 +0,0 @@
-name: Tests Nightly against upstream main
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
-
-jobs:
-  pre-commit:
-    name: pre-commit
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-          cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
-        env:
-          SKIP: no-commit-to-branch
-
-  pytest:
-    name: PyTest
-    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
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ 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#^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 .
-          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 '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
-      - name: Run tests
-        run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
-          pytest tests/patched/
-
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
-  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 60
-    needs: [pre-commit, pytest]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 121
-            cuda_version: 12.1.1
-            python_version: "3.10"
-            pytorch: 2.3.1
-            num_gpus: 1
-            axolotl_extras: mamba-ssm
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
-    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==0.71.8 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.tests
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,7 +1,6 @@
 name: Tests
 on:
  # check on push/merge to main, PRs, and manual triggers
-  merge_group:
  push:
    branches:
      - "main"
@@ -9,253 +8,82 @@ 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/setup-python@v5
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
        with:
          python-version: "3.10"
          cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.1
-        env:
-          SKIP: no-commit-to-branch
+      - uses: pre-commit/action@v3.0.0

  pytest:
    name: PyTest
    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
-
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
+        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==${{ matrix.pytorch_version }}
-
      - name: Install dependencies
        run: |
-          pip3 show torch
-          pip3 install --no-build-isolation -U -e .
-          python scripts/unsloth_install.py | sh
-          python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging
+          pip3 install -U -e .
+          pip3 install -r requirements-tests.txt

      - 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 {} \;
-
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
-
-  pytest-sdist:
-    name: PyTest from Source Dist
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      max-parallel: 1
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.4.1", "2.5.1"]
-    timeout-minutes: 20
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
-
-      - 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 {} \;
-
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
-
-  docker-e2e-tests-1st:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, pytest-sdist]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.4.1
-            num_gpus: 1
-            axolotl_extras:
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.10"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.tests
+          pytest --ignore=tests/e2e/ tests/

  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
    runs-on: [self-hosted, modal]
-    timeout-minutes: 90
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    timeout-minutes: 60
+    needs: [pre-commit, pytest]

    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: 121
-            cuda_version: 12.1.1
+          - cuda: 118
+            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.3.1
+            pytorch: 2.1.2
+            axolotl_args: "--extra-index-url https://download.pytorch.org/whl/cu118"
            num_gpus: 1
-            axolotl_extras: mamba-ssm
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 121
+            cuda_version: 12.1.0
+            python_version: "3.10"
+            pytorch: 2.1.2
+            num_gpus: 1
+          - cuda: 121
+            cuda_version: 12.1.0
            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.2.1
            num_gpus: 1
-            axolotl_extras:
    steps:
      - name: Checkout
        uses: actions/checkout@v4
@@ -266,13 +94,12 @@ jobs:
      - name: Install Modal
        run: |
          python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal 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
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 **/axolotl.egg-info
 configs
 last_run_prepared/
-outputs
 .vscode
 _site/

@@ -134,7 +133,6 @@ venv/
 ENV/
 env.bak/
 venv.bak/
-venv3.10/

 # Spyder project settings
 .spyderproject
@@ -177,12 +175,3 @@ qlora-out/*
 mlruns/*

 /.quarto/
-prepared-datasets/
-submit.sh
-*.out*
-
-typings/
-out/
-
-# vim
-*.swp
--- a/.isort.cfg
+++ b/.isort.cfg
@@ -1,3 +1,3 @@
 [settings]
 profile=black
-known_third_party=wandb,comet_ml
+known_third_party=wandb
--- a/.mypy.ini
+++ b/.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

--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -8,8 +8,6 @@ repos:
    -   id: check-yaml
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
-    -   id: no-commit-to-branch
-        args: ['--branch', 'main']
 -   repo: https://github.com/psf/black
    rev: 23.3.0
    hooks:
@@ -23,7 +21,7 @@ repos:
    hooks:
    - id: flake8
 -   repo: https://github.com/PyCQA/pylint
-    rev: v3.3.0
+    rev: v2.17.4
    hooks:
    - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
--- a/.pylintrc
+++ b/.pylintrc
@@ -1,5 +1,5 @@
 [MASTER]
-init-hook="from pylint.config import find_default_config_files; import sys; sys.path.append(next(find_default_config_files()).parent.as_posix())"
+init-hook="from pylint.config import find_pylintrc; import os, sys; sys.path.append(os.path.dirname(find_pylintrc()))"

 [TYPECHECK]

@@ -12,4 +12,3 @@ generated-members=numpy.*, torch.*
 disable=missing-function-docstring, line-too-long, import-error,
    too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
    too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
-    too-many-positional-arguments, possibly-used-before-assignment
--- a/MANIFEST.in
+++ b/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
--- a/README.md
+++ b/README.md
@@ -1,25 +1,4 @@
-<p align="center">
-    <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">
-    <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
-    <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

 Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.

@@ -28,10 +7,10 @@ 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">
@@ -43,52 +22,42 @@ Features:
 <td>

 ## 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)
-  - [Advanced Setup](#advanced-setup)
-    - [Environment](#environment)
-      - [Docker](#docker)
-      - [Conda/Pip venv](#condapip-venv)
-      - [Cloud GPU](#cloud-gpu)
-      - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
-        - [LambdaLabs](#lambdalabs)
-        - [GCP](#gcp)
-      - [Windows](#windows)
-      - [Mac](#mac)
-      - [Google Colab](#google-colab)
-      - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
-      - [Launching on public clouds via dstack](#launching-on-public-clouds-via-dstack)
-    - [Dataset](#dataset)
-    - [Config](#config)
-      - [All Config Options](#all-config-options)
-    - [Train](#train)
-      - [Preprocess dataset](#preprocess-dataset)
-      - [Multi-GPU](#multi-gpu)
-        - [DeepSpeed](#deepspeed)
-        - [FSDP](#fsdp)
-        - [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-)
+- [Introduction](#axolotl)
+- [Supported Features](#axolotl-supports)
+- [Quickstart](#quickstart-)
+- [Environment](#environment)
+  - [Docker](#docker)
+  - [Conda/Pip venv](#condapip-venv)
+  - [Cloud GPU](#cloud-gpu) - Latitude.sh, JarvisLabs, RunPod
+  - [Bare Metal Cloud GPU](#bare-metal-cloud-gpu)
+  - [Windows](#windows)
+  - [Mac](#mac)
+  - [Google Colab](#google-colab)
+  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
+- [Dataset](#dataset)
+- [Config](#config)
+  - [Train](#train)
+  - [Inference](#inference-playground)
+  - [Merge LORA to Base](#merge-lora-to-base)
+  - [Special Tokens](#special-tokens)
+  - [All Config Options](#all-config-options)
+- Advanced Topics
+  - [Multipack](./docs/multipack.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
+  - [RLHF & DPO](./docs/rlhf.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
+- [Common Errors](#common-errors-)
+  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
+- [Debugging Axolotl](#debugging-axolotl)
+- [Need Help?](#need-help-)
+- [Badge](#badge-)
+- [Community Showcase](#community-showcase)
+- [Contributing](#contributing-)
+- [Sponsors](#sponsors-)

 </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>
@@ -96,8 +65,8 @@ Features:
    <p>
      Go ahead and Axolotl questions!!
    </p>
-    <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
-    <img alt="PyTest Status" src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
+    <img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
+    <img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
  </div>
 </div>

@@ -105,148 +74,6 @@ 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 |
@@ -254,7 +81,6 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 | llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
 | Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
 | Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Mixtral8X22 | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 | Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
 | cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
 | btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
@@ -266,12 +92,46 @@ Thanks to all of our contributors to date. Help drive open source AI progress fo
 | RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 | Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
 | Gemma       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |
-| Jamba       | ✅         | ✅    | ✅     | ❓             | ❓                 | ✅          | ❓            |

 ✅: supported
 ❌: 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/OpenAccess-AI-Collective/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="./lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./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/OpenAccess-AI-Collective/axolotl/main/examples/openllama-3b/lora.yml
+```
+
 ## Advanced Setup

 ### Environment
@@ -279,7 +139,7 @@ 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 +158,7 @@ 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 +180,7 @@ 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 +190,7 @@ 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 +259,7 @@ 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)

@@ -430,47 +290,11 @@ HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
 HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
 ```

-#### Launching on public clouds via dstack
-To launch on GPU instance (both on-demand and spot instances) on public clouds (GCP, AWS, Azure, Lambda Labs, TensorDock, Vast.ai, and CUDO), you can use [dstack](https://dstack.ai/).
-
-Write a job description in YAML as below:
-
-```yaml
-# dstack.yaml
-type: task
-
-image: axolotlai/axolotl-cloud:main-latest
-
-env:
-  - HUGGING_FACE_HUB_TOKEN
-  - WANDB_API_KEY
-
-commands:
-  - accelerate launch -m axolotl.cli.train config.yaml
-
-ports:
-  - 6006
-
-resources:
-  gpu:
-    memory: 24GB..
-    count: 2
-```
-
-then, simply run the job with `dstack run` command. Append `--spot` option if you want spot instance. `dstack run` command will show you the instance with cheapest price across multi cloud services:
-
-```bash
-pip install dstack
-HUGGING_FACE_HUB_TOKEN=xxx WANDB_API_KEY=xxx dstack run . -f dstack.yaml # --spot
-```
-
-For further and fine-grained use cases, please refer to the official [dstack documents](https://dstack.ai/docs/) and the detailed description of [axolotl example](https://github.com/dstackai/dstack/tree/master/examples/fine-tuning/axolotl) on the official repository.
-
 ### Dataset

 Axolotl supports a variety of dataset formats.  It is recommended to use a JSONL.  The schema of the JSONL depends upon the task and the prompt template you wish to use.  Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.

-See [the documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.
+See [these docs](https://openaccess-ai-collective.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.

 ### Config

@@ -503,10 +327,11 @@ 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
-      chat_template: chatml # defaults to tokenizer's chat_template
+      type: sharegpt
+      conversation: chatml # default: vicuna_v1.1

      # local
    - path: data.jsonl # or json
@@ -600,7 +425,7 @@ deepspeed: deepspeed_configs/zero1.json
 ```

 ```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json
+accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed_configs/zero1.json
 ```

 ##### FSDP
@@ -634,22 +459,6 @@ 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 +475,6 @@ 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.
@@ -781,7 +570,7 @@ If you decode a prompt constructed by axolotl, you might see spaces between toke
 3. Make sure the inference string from #2 looks **exactly** like the data you fine tuned on from #1, including spaces and new lines.  If they aren't the same, adjust your inference server accordingly.
 4. As an additional troubleshooting step, you can look at the token ids between 1 and 2 to make sure they are identical.

-Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this.  See [this blog post](https://hamel.dev/notes/llm/finetuning/05_tokenizer_gotchas.html) for a concrete example.
+Having misalignment between your prompts during training and inference can cause models to perform very poorly, so it is worth checking this.  See [this blog post](https://hamel.dev/notes/llm/05_tokenizer_gotchas.html) for a concrete example.

 ## Debugging Axolotl

@@ -789,6 +578,86 @@ 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+```
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/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/OpenAccess-AI-Collective/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/openaccess-ai-collective/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)
+
+---
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -14,7 +14,7 @@ website:
    - icon: twitter
      href: https://twitter.com/axolotl_ai
    - icon: github
-      href: https://github.com/axolotl-ai-cloud/axolotl/
+      href: https://github.com/OpenAccess-AI-Collective/axolotl/
    - icon: discord
      href: https://discord.gg/7m9sfhzaf3

@@ -36,8 +36,6 @@ website:
            - docs/nccl.qmd
            - docs/mac.qmd
            - docs/multi-node.qmd
-            - docs/unsloth.qmd
-            - docs/amd_hpc.qmd
        - section: "Dataset Formats"
          contents: docs/dataset-formats/*
        - section: "Reference"
--- a/cicd/Dockerfile.jinja
+++ b/cicd/Dockerfile.jinja
@@ -1,21 +1,20 @@
-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 }}"
-ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
-ENV HF_HOME="{{ HF_HOME }}"

 RUN apt-get update && \
    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev

 WORKDIR /workspace

-RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
+RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git

 WORKDIR /workspace/axolotl

@@ -23,25 +22,15 @@ RUN git fetch origin +$GITHUB_REF && \
    git checkout FETCH_HEAD

 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
-        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
-        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
-        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
-        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
-        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
-    fi
-
+RUN pip install causal_conv1d
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
    else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore] $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 pytest

 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
--- a/cicd/cicd.sh
+++ b/cicd/cicd.sh
@@ -1,10 +1,5 @@
 #!/bin/bash
-set -e

-python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
-
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
-# pytest -v --durations=10 -n8 --dist loadfile /workspace/axolotl/tests/patched/
-pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 /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/
+pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest /workspace/axolotl/tests/e2e/patched/
+pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/
--- a/cicd/multigpu.py
+++ b/cicd/multigpu.py
@@ -1,85 +0,0 @@
-"""
- modal application to run axolotl gpu tests in Modal
- """
-# pylint: disable=duplicate-code
-
-import os
-import pathlib
-import tempfile
-
-import jinja2
-import modal
-from jinja2 import select_autoescape
-from modal import App, Image
-
-cicd_path = pathlib.Path(__file__).parent.resolve()
-
-template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
-template_env = jinja2.Environment(
-    loader=template_loader, autoescape=select_autoescape()
-)
-df_template = template_env.get_template("Dockerfile.jinja")
-
-df_args = {
-    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
-    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.3.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.3.1"),
-    "CUDA": os.environ.get("CUDA", "121"),
-    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
-    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "HF_HOME": "/workspace/data/huggingface-cache/hub",
-}
-
-dockerfile_contents = df_template.render(**df_args)
-
-temp_dir = tempfile.mkdtemp()
-with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
-    f.write(dockerfile_contents)
-
-cicd_image = (
-    Image.from_dockerfile(
-        pathlib.Path(temp_dir) / "Dockerfile",
-        force_build=True,
-        gpu="A10G",
-    )
-    .env(df_args)
-    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
-)
-
-app = App("Axolotl CI/CD", secrets=[])
-
-hf_cache_volume = modal.Volume.from_name(
-    "axolotl-ci-hf-hub-cache", create_if_missing=True
-)
-VOLUME_CONFIG = {
-    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
-}
-
-N_GPUS = int(os.environ.get("N_GPUS", 2))
-GPU_CONFIG = modal.gpu.H100(count=N_GPUS)
-
-
-def run_cmd(cmd: str, run_folder: str):
-    import subprocess  # nosec
-
-    # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
-
-
-@app.function(
-    image=cicd_image,
-    gpu=GPU_CONFIG,
-    timeout=60 * 60,
-    cpu=8.0,
-    memory=131072 * N_GPUS,
-    volumes=VOLUME_CONFIG,
-)
-def cicd_pytest():
-    run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")
-
-
-@app.local_entrypoint()
-def main():
-    cicd_pytest.remote()
--- a/cicd/multigpu.sh
+++ b/cicd/multigpu.sh
@@ -1,5 +0,0 @@
-#!/bin/bash
-set -e
-
-# only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/
--- a/cicd/tests.py
+++ b/cicd/tests.py
@@ -1,8 +1,6 @@
 """
 modal application to run axolotl gpu tests in Modal
 """
-# pylint: disable=duplicate-code
-
 import os
 import pathlib
 import tempfile
@@ -10,7 +8,7 @@ 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()

@@ -23,13 +21,11 @@ df_template = template_env.get_template("Dockerfile.jinja")
 df_args = {
    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.3.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.3.1"),
-    "CUDA": os.environ.get("CUDA", "121"),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.0.1"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.10-cu118-2.0.1"),
+    "CUDA": os.environ.get("CUDA", "118"),
    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
-    "HF_HOME": "/workspace/data/huggingface-cache/hub",
 }

 dockerfile_contents = df_template.render(**df_args)
@@ -41,7 +37,6 @@ 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",
    )
@@ -49,14 +44,8 @@ cicd_image = (
    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
 )

-app = App("Axolotl CI/CD", secrets=[])
+stub = Stub("Axolotl CI/CD", secrets=[])

-hf_cache_volume = modal.Volume.from_name(
-    "axolotl-ci-hf-hub-cache", create_if_missing=True
-)
-VOLUME_CONFIG = {
-    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
-}

 N_GPUS = int(os.environ.get("N_GPUS", 1))
 GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
@@ -70,18 +59,17 @@ 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,
-    volumes=VOLUME_CONFIG,
 )
 def cicd_pytest():
    run_cmd("./cicd/cicd.sh", "/workspace/axolotl")


-@app.local_entrypoint()
+@stub.local_entrypoint()
 def main():
    cicd_pytest.remote()
--- a/deepspeed_configs/zero1_torch_compile.json
+++ b/deepspeed_configs/zero1_torch_compile.json
@@ -1,27 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 1,
-    "overlap_comm": true
-  },
-  "bf16": {
-    "enabled": "auto"
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "compile": {
-    "disable": false,
-    "backend": "inductor"
-  },
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}
--- a/deepspeed_configs/zero3_bf16.json
+++ b/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",
--- a/deepspeed_configs/zero3_bf16_cpuoffload_all.json
+++ b/deepspeed_configs/zero3_bf16_cpuoffload_all.json
@@ -1,6 +1,4 @@
 {
-  "zero_force_ds_cpu_optimizer": false,
-  "zero_allow_untested_optimizer": true,
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
@@ -24,6 +22,15 @@
  "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",
--- a/deepspeed_configs/zero3_bf16_cpuoffload_params.json
+++ b/deepspeed_configs/zero3_bf16_cpuoffload_params.json
@@ -1,6 +1,4 @@
 {
-  "zero_force_ds_cpu_optimizer": false,
-  "zero_allow_untested_optimizer": true,
  "zero_optimization": {
    "stage": 3,
    "offload_param": {
@@ -20,6 +18,15 @@
  "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",
--- a/devtools/dev_chat_template.yml
+++ b/devtools/dev_chat_template.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
-    type: chat_template
+  - path: philschmid/guanaco-sharegpt-style
+    type: sharegpt
    shards: 10
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -1,33 +1,32 @@
 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

 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev

 WORKDIR /workspace

-RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
+RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/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] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
    else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore] $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

--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -3,7 +3,7 @@ ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4

-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION as base-builder

 ENV PATH="/root/miniconda3/bin:${PATH}"

@@ -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$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA

 RUN git lfs install --skip-repo && \
    pip3 install awscli && \
--- a/docker/Dockerfile-cloud
+++ b/docker/Dockerfile-cloud
@@ -1,8 +1,9 @@
 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 TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
 ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 ENV HF_HUB_ENABLE_HF_TRANSFER="1"

--- a/docker/Dockerfile-cloud-no-tmux
+++ b/docker/Dockerfile-cloud-no-tmux
@@ -1,26 +0,0 @@
-ARG BASE_TAG=main
-FROM axolotlai/axolotl:$BASE_TAG
-
-ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-ENV HF_HUB_CACHE="/workspace/data/huggingface-cache/hub"
-ENV HF_HOME="/workspace/data/huggingface-cache/hub"
-ENV HF_HUB_ENABLE_HF_TRANSFER="1"
-
-EXPOSE 8888
-EXPOSE 22
-
-COPY scripts/cloud-entrypoint-term.sh /root/cloud-entrypoint.sh
-COPY scripts/motd /etc/motd
-
-RUN pip install jupyterlab notebook ipywidgets && \
-    jupyter lab clean
-RUN apt install --yes --no-install-recommends openssh-server tmux sudo && \
-    pip3 install -U --no-cache-dir grpcio ray[default]==2.9.3 && \
-    mkdir -p ~/.ssh && \
-    chmod 700 ~/.ssh && \
-    printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
-    chmod +x /workspace/axolotl/scripts/cloud-entrypoint.sh && \
-    chmod +x /root/cloud-entrypoint.sh
-
-ENTRYPOINT ["/root/cloud-entrypoint.sh"]
-CMD ["sleep", "infinity"]
--- a/docker/Dockerfile-tests
+++ b/docker/Dockerfile-tests
@@ -1,10 +1,11 @@
 ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base:$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"

@@ -15,7 +16,7 @@ RUN apt-get update && \

 WORKDIR /workspace

-RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
+RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git

 WORKDIR /workspace/axolotl

@@ -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
--- a/docs/amd_hpc.qmd
+++ b/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. 🚂
--- a/docs/config.qmd
+++ b/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
@@ -116,61 +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 chat template. Used only if `chat_template: jinja` or empty.
-    chat_template_jinja:
-
-    # Key containing the messages (default: "messages")
-    field_messages: messages
-    # Key for role in each message (default: "role")
-    message_field_role: role
-    # Key for content in each message (default:  "content")
-    message_field_content: content
-
-    # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
-    roles:
-      user: ["human", "user"]
-      assistant: ["gpt", "assistant"]
-      system: ["system"]
-      tool: ["tool"]
-
-    # IMPORTANT: The following fields determine which parts of the conversation to train on.
-    # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
-    # See examples at `docs/dataset-formats/conversation.qmd`
-    # Note: If the below 4 fields are empty, defaults to training only on the last message.
-
-    # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
-    roles_to_train: ["assistant"]  # default
-    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
-    # - all: train on all EOS tokens
-    # - turn (default): 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).
-    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:
@@ -182,21 +138,12 @@ test_datasets:
    data_files:
      - /workspace/data/eval.jsonl

-# use RL training: 'dpo', 'ipo', 'kto'
+# use RL training: 'dpo', 'ipo', 'kto_pair'
 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:
-# - 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.
-# - 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. 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
+# Saves the desired chat template to the tokenizer_config.json for easier inferencing
+# Currently supports chatml and inst (mistral/mixtral)
+chat_template: chatml
 # 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
@@ -239,14 +186,6 @@ eval_sample_packing:
 # The trainer will provide recommended values for these values.
 sample_packing_eff_est:
 total_num_tokens:
-# Increasing the following values helps with packing, but usually only slightly (<%1.)
-# The number of samples packed at a time.
-sample_packing_group_size: 100000
-# The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
-sample_packing_bin_size: 200
-
-# Use batch flattening for speedups when not using sample_packing
-batch_flattening:

 # Passed through to transformers when loading the model when launched without accelerate
 # Use `sequential` when training w/ model parallelism to limit memory
@@ -288,12 +227,6 @@ lora_modules_to_save:

 lora_fan_in_fan_out: false

-# LoRA+ hyperparameters
-# For more details about the following options, see:
-# https://arxiv.org/abs/2402.12354  and `src/axolotl/core/train_builder.py`
-loraplus_lr_ratio: # loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.
-loraplus_lr_embedding: #  loraplus learning rate for lora embedding layers. Default value is 1e-6.
-
 peft:
  # Configuration options for loftq initialization for LoRA
  # https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
@@ -321,27 +254,13 @@ 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

 # Whether to use torch.compile and which backend to use
-# setting to `auto` will enable torch compile when torch>=2.5.1
-torch_compile:  # Optional[Union[Literal["auto"], bool]]
+torch_compile:  # bool
 torch_compile_backend:  # Optional[str]

 # Training hyperparameters
@@ -349,7 +268,6 @@ torch_compile_backend:  # Optional[str]
 # If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
 gradient_accumulation_steps: 1
 # The number of samples to include in each batch. This is the number of samples sent to each GPU.
-# Batch size per gpu = micro_batch_size * gradient_accumulation_steps
 micro_batch_size: 2
 eval_batch_size:
 num_epochs: 4
@@ -360,7 +278,7 @@ lr_quadratic_warmup:
 logging_steps:
 eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
 evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
-save_strategy: # Set to `"no"` to skip checkpoint saves
+save_strategy: # Set to `no` to skip checkpoint saves
 save_steps: # Leave empty to save at each epoch
 saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
 save_total_limit: # Checkpoints saved at a time
@@ -371,11 +289,7 @@ 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"]
-
-profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
-                # see https://pytorch.org/blog/understanding-gpu-memory-1/ for more information
-                # snapshots can be visualized @ https://pytorch.org/memory_viz
+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)
@@ -423,7 +337,6 @@ 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
@@ -499,7 +412,6 @@ special_tokens:
  # bos_token: "<s>"
  # eos_token: "</s>"
  # unk_token: "<unk>"
-  # pad_token: "[PAD]"

 # Add extra tokens.
 tokens:
--- a/docs/dataset-formats/conversation.qmd
+++ b/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 +38,26 @@ IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
 {"conversations": [{"role": "...", "value": "..."}]}
 ```

+## sharegpt.load_role

-## chat_template
-
-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.
+conversations where `role` is used instead of `from`

 ```{.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
-
-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
-    roles_to_train:
-    train_on_eos:
-```
-
-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"]  # default value
-```
-
-3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
-
-```yaml
-chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
-datasets:
-  - path: ...
-    type: chat_template
-```
-
-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
-```
-
-5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
-
-For a data sample that looks like:
+conversations where `from` is `prompter` `assistant` instead of default sharegpt

 ```{.json filename="data.jsonl"}
-{
-  "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}
-  ]
-}
+{"conversations": [{"from": "...", "value": "..."}]}
 ```

-The configuration would look like:
+## sharegpt_jokes

-```yaml
-datasets:
-  - path: ...
-    type: chat_template
-    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
+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": "..."}]}
 ```
-
-Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.
--- a/docs/dataset-formats/pretraining.qmd
+++ b/docs/dataset-formats/pretraining.qmd
@@ -19,14 +19,7 @@ For pretraining, there is no prompt template or roles.  The only required field
 Axolotl usually loads the entire dataset into memory. This will be challenging for large datasets. Use the following config to enable streaming:

 ```{.yaml filename="config.yaml"}
-pretraining_dataset:
-  - name:
-    path:
-    split:
-    text_column: # column in dataset with the data, usually `text`
-    type: pretrain
-    trust_remote_code:
-    skip: # number of rows of data to skip over from the beginning
+pretraining_dataset: # hf path only
 ...
 ```

--- a/docs/dataset-formats/tokenized.qmd
+++ b/docs/dataset-formats/tokenized.qmd
@@ -4,25 +4,9 @@ description: How to use a custom pre-tokenized dataset.
 order: 5
 ---

- Pass an empty `type:` in your axolotl config.
+- Do not pass a `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.
- For pretraining, do not truncate/pad documents to the context window length.
- For instruction training, documents must be truncated/padded as desired.
-
-Sample config:

 ```{.yaml filename="config.yml"}
-datasets:
-  - path: /path/to/your/file.jsonl
-    ds_type: json
-    type:
-```
-
-Sample jsonl:
-
-```jsonl
-{"input_ids":[271,299,99],"attention_mask":[1,1,1],"labels":[271,-100,99]}
-{"input_ids":[87,227,8383,12],"attention_mask":[1,1,1,1],"labels":[87,227,8383,12]}
+- path: ...
 ```
--- a/docs/dataset_preprocessing.qmd
+++ b/docs/dataset_preprocessing.qmd
@@ -1,35 +0,0 @@
---
-title: Dataset Preprocessing
-description: How datasets are processed
---
-
-Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the (dataset format)[../dataset-formats/] and prompt strategies to:
- - parse the dataset based on the *dataset format*
- - transform the dataset to how you would interact with the model based on the *prompt strategy*
- - tokenize the dataset based on the configured model & tokenizer
- - shuffle and merge multiple datasets together if using more than one
-
-The processing of the datasets can happen one of two ways:
-
-1. Before kicking off training by calling `python -m axolotl.cli.preprocess /path/to/your.yaml --debug`
-2. When training is started
-
-What are the benefits of pre-processing? When training interactively or for sweeps
-(e.g. you are restarting the trainer often), processing the datasets can oftentimes be frustratingly
-slow. Pre-processing will cache the tokenized/formatted datasets according to a hash of dependent
-training parameters so that it will intelligently pull from its cache when possible.
-
-The path of the cache is controlled by `dataset_prepared_path:` and is often left blank in example
-YAMLs as this leads to a more robust solution that prevents unexpectedly reusing cached data.
-
-If `dataset_prepared_path:` is left empty, when training, the processed dataset will be cached in a
-default path of `./last_run_prepared/`, but will ignore anything already cached there. By explicitly
-setting `dataset_prepared_path: ./last_run_prepared`, the trainer will use whatever pre-processed
-data is in the cache.
-
-What are the edge cases? Let's say you are writing a custom prompt strategy or using a user-defined
-prompt template. Because the trainer cannot readily detect these changes, we cannot change the
-calculated hash value for the pre-processed dataset. If you have `dataset_prepared_path: ...` set
-and change your prompt templating logic, it may not pick up the changes you made and you will be
-training over the old prompt.
--- a/docs/debugging.qmd
+++ b/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
-    type: chat_template
+  - path: <path to your sharegpt formatted dataset> # example on HF Hub: philschmid/guanaco-sharegpt-style
+    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
@@ -185,14 +185,14 @@ 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

 On the host that is running axolotl (ex: if you are using a remote host), clone the axolotl repo and change your current directory to the root:

 ```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl
+git clone https://github.com/OpenAccess-AI-Collective/axolotl
 cd axolotl
 ```

@@ -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).
--- a/docs/fsdp_qlora.qmd
+++ b/docs/fsdp_qlora.qmd
@@ -20,7 +20,7 @@ To enable `QLoRA` with `FSDP`, you need to perform the following steps:
 > See the [example config](#example-config) file in addition to reading these instructions.

 1. Set `adapter: qlora` in your axolotl config file.
-2. Enable FSDP in your axolotl config, as [described here](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#fsdp).
+2. Enable FSDP in your axolotl config, as [described here](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#fsdp).
 3. Use one of the supported model types: `llama`, `mistral` or `mixtral`.

 ## Example Config
@@ -29,7 +29,7 @@ To enable `QLoRA` with `FSDP`, you need to perform the following steps:

 ## References

- [PR #1378](https://github.com/axolotl-ai-cloud/axolotl/pull/1378) enabling QLoRA in FSDP in Axolotl.
+- [PR #1378](https://github.com/OpenAccess-AI-Collective/axolotl/pull/1378) enabling QLoRA in FSDP in Axolotl.
 - [Blog Post](https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html) from the [Answer.AI](https://www.answer.ai/) team describing the work that enabled QLoRA in FSDP.
 - Related HuggingFace PRs Enabling FDSP + QLoRA:
    - Accelerate [PR#2544](https://github.com/huggingface/accelerate/pull/2544 )
--- a/docs/input_output.qmd
+++ b/docs/input_output.qmd
@@ -25,7 +25,7 @@ description: "Template-free prompt construction with the `input_output` format"
 ### Masking Inputs

 One of the most popular features of
-[axolotl](https://github.com/axolotl-ai-cloud/axolotl) is
+[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) is
 setting the following configuration value:


@@ -33,7 +33,7 @@ setting the following configuration value:
 train_on_inputs: false
 ```

-If you declare a [dataset formats](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#dataset)
+If you declare a [dataset formats](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#dataset)
 such as `alpaca` or `chatml`, axolotl knows what is an input
 (i.e. human) vs. an output (i.e. the assistant) and masks the input
 labels so that your model can focus on predicting the outputs only.
@@ -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
--- a/docs/multimodal.qmd
+++ b/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'
-```
--- a/docs/rlhf.qmd
+++ b/docs/rlhf.qmd
@@ -49,27 +49,7 @@ remove_unused_columns: false
 chat_template: chatml
 datasets:
  - path: argilla/ultrafeedback-binarized-preferences-cleaned
-    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
+    type: orpo.chat_template
 ```

 #### Using local dataset files
--- a/docs/torchao.qmd
+++ b/docs/torchao.qmd
@@ -1,19 +0,0 @@
---
-title: "PyTorch ao"
-description: "Custom data types and layouts for training and inference"
---
-
-### Installation
-
-Stable Release from the PyTorch index
-
-```bash
-pip install torchao --extra-index-url https://download.pytorch.org/whl/cu121 # full options are cpu/cu118/cu121/cu124
-```
-
-
-Nightly release
-
-```bash
-pip install --pre torchao-nightly --index-url https://download.pytorch.org/whl/nightly/cu121 # full options are cpu/cu118/cu121/cu124
-```
--- a/docs/unsloth.qmd
+++ b/docs/unsloth.qmd
@@ -1,47 +0,0 @@
---
-title: "Unsloth"
-description: "Hyper-optimized QLoRA finetuning for single GPUs"
---
-
-### Overview
-
-Unsloth provides hand-written optimized kernels for LLM finetuning that slightly improve speed and VRAM over
-standard industry baselines.
-
-
-### Installation
-
-The following will install the correct unsloth and extras from source.
-
-```bash
-python scripts/unsloth_install.py | sh
-```
-
-### Using unsloth w Axolotl
-
-Axolotl exposes a few configuration options to try out unsloth and get most of the performance gains.
-
-Our unsloth integration is currently limited to the following model architectures:
- - llama
-
-These options are specific to LoRA finetuning and cannot be used for multi-GPU finetuning
-```yaml
-unsloth_lora_mlp: true
-unsloth_lora_qkv: true
-unsloth_lora_o: true
-```
-
-These options are composable and can be used with multi-gpu finetuning
-```yaml
-unsloth_cross_entropy_loss: true
-unsloth_rms_norm: true
-unsloth_rope: true
-```
-
-### Limitations
-
- Single GPU only; e.g. no multi-gpu support
- No deepspeed or FSDP support (requires multi-gpu)
- LoRA + QLoRA support only. No full fine tunes or fp8 support.
- Limited model architecture support. Llama, Phi, Gemma, Mistral only
- No MoE support.
--- a/examples/cerebras/btlm-ft.yml
+++ b/examples/cerebras/btlm-ft.yml
@@ -1,10 +1,6 @@
 base_model: cerebras/btlm-3b-8k-base
-# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: GPT2Tokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 trust_remote_code: true
 tokenizer_use_fast: true
 tokenizer_legacy: true
@@ -42,7 +38,7 @@ wandb_watch:
 wandb_name:
 wandb_log_model:

-output_dir: ./outputs/btlm-out
+output_dir: btlm-out
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 1
--- a/examples/cerebras/qlora.yml
+++ b/examples/cerebras/qlora.yml
@@ -1,7 +1,4 @@
 base_model: cerebras/Cerebras-GPT-1.3B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 load_in_4bit: true
 strict: false
@@ -28,7 +25,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 batch_size: 4
 micro_batch_size: 4
 num_epochs: 2
--- a/examples/code-llama/13b/lora.yml
+++ b/examples/code-llama/13b/lora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-13b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: true
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/code-llama/13b/qlora.yml
+++ b/examples/code-llama/13b/qlora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-13b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 adapter: qlora
 lora_model_dir:
--- a/examples/code-llama/34b/lora.yml
+++ b/examples/code-llama/34b/lora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-34b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: true
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/code-llama/34b/qlora.yml
+++ b/examples/code-llama/34b/qlora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-34b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 adapter: qlora
 lora_model_dir:
--- a/examples/code-llama/7b/lora.yml
+++ b/examples/code-llama/7b/lora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: true
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/code-llama/7b/qlora.yml
+++ b/examples/code-llama/7b/qlora.yml
@@ -1,9 +1,6 @@
 base_model: codellama/CodeLlama-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: CodeLlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 adapter: qlora
 lora_model_dir:
--- a/examples/colab-notebooks/colab-axolotl-example.ipynb
+++ b/examples/colab-notebooks/colab-axolotl-example.ipynb
@@ -1,357 +1,216 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKjdG7tbTb-n"
+      },
+      "source": [
+        "# Example notebook for running Axolotl on google colab"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RcbNpOgWRcii"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\n",
+        "assert (torch.cuda.is_available()==True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3nLav8oTRA5"
+      },
+      "source": [
+        "## Install Axolotl and dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3c3yGAwnOIdi",
+        "outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install torch==\"2.1.2\"\n",
+        "!pip install -e git+https://github.com/OpenAccess-AI-Collective/axolotl#egg=axolotl\n",
+        "!pip install flash-attn==\"2.5.0\"\n",
+        "!pip install deepspeed==\"0.13.1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BW2MFr7HTjub"
+      },
+      "source": [
+        "## Create an yaml config file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9pkF2dSoQEUN"
+      },
+      "outputs": [],
+      "source": [
+        "import yaml\n",
+        "\n",
+        "# Your YAML string\n",
+        "yaml_string = \"\"\"\n",
+        "base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
+        "model_type: LlamaForCausalLM\n",
+        "tokenizer_type: LlamaTokenizer\n",
+        "is_llama_derived_model: true\n",
+        "\n",
+        "load_in_8bit: false\n",
+        "load_in_4bit: true\n",
+        "strict: false\n",
+        "\n",
+        "datasets:\n",
+        "  - path: mhenrichsen/alpaca_2k_test\n",
+        "    type: alpaca\n",
+        "dataset_prepared_path:\n",
+        "val_set_size: 0.05\n",
+        "output_dir: ./qlora-out\n",
+        "\n",
+        "adapter: qlora\n",
+        "lora_model_dir:\n",
+        "\n",
+        "sequence_len: 1096\n",
+        "sample_packing: true\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",
+        "\n",
+        "wandb_project:\n",
+        "wandb_entity:\n",
+        "wandb_watch:\n",
+        "wandb_name:\n",
+        "wandb_log_model:\n",
+        "\n",
+        "mlflow_experiment_name: colab-example\n",
+        "\n",
+        "gradient_accumulation_steps: 1\n",
+        "micro_batch_size: 1\n",
+        "num_epochs: 4\n",
+        "max_steps: 20\n",
+        "optimizer: paged_adamw_32bit\n",
+        "lr_scheduler: cosine\n",
+        "learning_rate: 0.0002\n",
+        "\n",
+        "train_on_inputs: false\n",
+        "group_by_length: false\n",
+        "bf16: false\n",
+        "fp16: true\n",
+        "tf32: false\n",
+        "\n",
+        "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",
+        "\n",
+        "warmup_steps: 10\n",
+        "evals_per_epoch:\n",
+        "saves_per_epoch:\n",
+        "debug:\n",
+        "deepspeed:\n",
+        "weight_decay: 0.0\n",
+        "fsdp:\n",
+        "fsdp_config:\n",
+        "special_tokens:\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "# Convert the YAML string to a Python dictionary\n",
+        "yaml_dict = yaml.safe_load(yaml_string)\n",
+        "\n",
+        "# Specify your file path\n",
+        "file_path = 'test_axolotl.yaml'\n",
+        "\n",
+        "# Write the YAML file\n",
+        "with open(file_path, 'w') as file:\n",
+        "    yaml.dump(yaml_dict, file)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bidoj8YLTusD"
+      },
+      "source": [
+        "## Launch the training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ydTI2Jk2RStU",
+        "outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
+      },
+      "outputs": [],
+      "source": [
+        "# Buy using the ! the comand will be executed as a bash command\n",
+        "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Play with inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Buy using the ! the comand will be executed as a bash command\n",
+        "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
+        "    --qlora_model_dir=\"./qlora-out\" --gradio"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "# Check so there is a gpu available, a T4(free tier) is enough to run this notebook\n",
-    "assert (torch.cuda.is_available()==True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --no-build-isolation axolotl[deepspeed]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hugging Face login (optional)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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",
-    "yaml_string = \"\"\"\n",
-    "base_model: NousResearch/Meta-Llama-3.1-8B\n",
-    "\n",
-    "load_in_8bit: false\n",
-    "load_in_4bit: true\n",
-    "strict: false\n",
-    "\n",
-    "datasets:\n",
-    "  - path: tatsu-lab/alpaca\n",
-    "    type: alpaca\n",
-    "dataset_prepared_path: last_run_prepared\n",
-    "val_set_size: 0.05\n",
-    "output_dir: ./outputs/lora-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",
-    "lora_r: 32\n",
-    "lora_alpha: 16\n",
-    "lora_dropout: 0.05\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",
-    "wandb_watch:\n",
-    "wandb_name:\n",
-    "wandb_log_model:\n",
-    "\n",
-    "gradient_accumulation_steps: 2\n",
-    "micro_batch_size: 1\n",
-    "num_epochs: 1\n",
-    "optimizer: paged_adamw_8bit\n",
-    "lr_scheduler: cosine\n",
-    "learning_rate: 2e-5\n",
-    "\n",
-    "train_on_inputs: false\n",
-    "group_by_length: false\n",
-    "bf16: auto\n",
-    "fp16:\n",
-    "tf32: false\n",
-    "\n",
-    "gradient_checkpointing: true\n",
-    "early_stopping_patience:\n",
-    "resume_from_checkpoint:\n",
-    "logging_steps: 1\n",
-    "xformers_attention:\n",
-    "flash_attention: false\n",
-    "sdp_attention: true\n",
-    "\n",
-    "warmup_steps: 1\n",
-    "max_steps: 25\n",
-    "evals_per_epoch: 1\n",
-    "eval_table_size:\n",
-    "saves_per_epoch: 1\n",
-    "debug:\n",
-    "deepspeed:\n",
-    "weight_decay: 0.0\n",
-    "fsdp:\n",
-    "fsdp_config:\n",
-    "special_tokens:\n",
-    "  pad_token: <|end_of_text|>\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "# Convert the YAML string to a Python dictionary\n",
-    "yaml_dict = yaml.safe_load(yaml_string)\n",
-    "\n",
-    "# Specify your file path\n",
-    "file_path = 'test_axolotl.yaml'\n",
-    "\n",
-    "# Write the YAML file\n",
-    "with open(file_path, 'w') as file:\n",
-    "    yaml.dump(yaml_dict, file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "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",
-    "\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": {},
-   "outputs": [],
-   "source": [
-    "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict with trained model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
-    "    --lora_model_dir=\"./outputs/lora-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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
--- a/examples/dbrx/16bit-lora.yaml
+++ b/examples/dbrx/16bit-lora.yaml
@@ -1,84 +0,0 @@
-base_model: LnL-AI/dbrx-base-converted-v2
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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: 512
-sample_packing: false
-pad_to_sequence_len: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-adapter: lora
-lora_model_dir:
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-# w1, w2, & v1 will hang the trainer
-lora_target_modules:
-  - q_proj # attn
-  - k_proj # attn
-  - v_proj # attn
-  - out_proj # attn
-  - layer # router
-#  - w1
-#  - w2
-#  - v1
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: false  # don't use with fsdp_activation_checkpointing
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch:
-saves_per_epoch: 1
-debug:
-weight_decay: 0.0
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: false
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: DbrxBlock
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_activation_checkpointing: true
--- a/examples/dbrx/8bit-lora.yaml
+++ b/examples/dbrx/8bit-lora.yaml
@@ -1,84 +0,0 @@
-base_model: LnL-AI/dbrx-base-converted-v2
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-trust_remote_code: true
-
-load_in_8bit: true
-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: 512
-sample_packing: false
-pad_to_sequence_len: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-adapter: lora
-lora_model_dir:
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-# w1, w2, & v1 will hang the trainer
-lora_target_modules:
-  - q_proj # attn
-  - k_proj # attn
-  - v_proj # attn
-  - out_proj # attn
-  - layer # router
-#  - w1
-#  - w2
-#  - v1
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: false  # don't use with fsdp_activation_checkpointing
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch:
-saves_per_epoch: 1
-debug:
-weight_decay: 0.0
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: false
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: DbrxBlock
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_activation_checkpointing: true
--- a/examples/dbrx/README.md
+++ b/examples/dbrx/README.md
@@ -1,26 +0,0 @@
-# DBRX MoE
-
-Currently, for LoRA, only the `q_proj`, `k_proj`, `v_proj` `out_proj` and `layer` Linear layers are trainable.
-
-We are using the "converted" base models based on [this issue](https://huggingface.co/databricks/dbrx-instruct/discussions/10)
-where the Experts are fused as an `nn.Parameter` rather than a `nn.Linear` layer. However, the implementation
-is still a bit buggy and attempting to train a LoRA adapter over those `w1`, `w2` and `v1` layers
-results in the trainer hanging.
-
-
-### FSDP
-We've tested using the [`LnL-AI/dbrx-base-converted-v2`](https://huggingface.co/LnL-AI/dbrx-base-converted-v2) model as the base model for FSDP.
-
-The high memory usage seen w/ FSDP is due to FSDP not supporting 8bit optimizers.
-
- 16-bit LoRA w/ FSDP
-  - ✅ w/o CPU Offload - 8x80GB uses ~80GiB/gpu
-  - ❌ w/ CPU Offload - `paged_adamw_8bit` optimizer errors from being on cpu
- ✅ 8-bit LoRA w/ FSDP
- ❌ 4-bit QLoRA w/ FSDP - errors w/: `Error an illegal memory access was encountered at line 90 in file /src/csrc/ops.cu`
- ✅ bf16 full finetune w/ FSDP, freezing all but first 8 layers (8x80GB uses ~78GiB/gpu)
-
-
-### Deepspeed
-
-WIP
--- a/examples/dbrx/fft-ds-zero3.yaml
+++ b/examples/dbrx/fft-ds-zero3.yaml
@@ -1,59 +0,0 @@
-base_model: LnL-AI/dbrx-base-converted-v2
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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: 512
-sample_packing: false
-pad_to_sequence_len: false
-
-unfrozen_parameters:
-  - transformer.blocks.[0-7].
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: paged_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
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch:
-saves_per_epoch: 1
-debug:
-weight_decay: 0.0
-deepspeed: deepspeed_configs/zero3_bf16.json
--- a/examples/deepseek-v2/fft-fsdp-16b.yaml
+++ b/examples/deepseek-v2/fft-fsdp-16b.yaml
@@ -1,69 +0,0 @@
-base_model: deepseek-ai/DeepSeek-V2-Lite
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-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
--- a/examples/deepseek-v2/qlora-fsdp-2_5.yaml
+++ b/examples/deepseek-v2/qlora-fsdp-2_5.yaml
@@ -1,89 +0,0 @@
-base_model: axolotl-quants/DeepSeek-V2.5-bnb-nf4-bf16
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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
--- a/examples/falcon/config-7b-lora.yml
+++ b/examples/falcon/config-7b-lora.yml
@@ -1,12 +1,7 @@
 base_model: tiiuae/falcon-7b
-# optionally might have model_type or tokenizer_type
+trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
-trust_remote_code: true

 load_in_8bit: true
 load_in_4bit: false
@@ -33,7 +28,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/falcon-7b
+output_dir: ./falcon-7b
 batch_size: 2
 micro_batch_size: 1
 num_epochs: 4
--- a/examples/falcon/config-7b-qlora.yml
+++ b/examples/falcon/config-7b-qlora.yml
@@ -1,15 +1,10 @@
 # 1b: tiiuae/falcon-rw-1b
 # 40b: tiiuae/falcon-40b
 base_model: tiiuae/falcon-7b
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 # required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
 trust_remote_code: true
-
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer

 load_in_8bit: false
 # enable 4bit for QLoRA
@@ -47,7 +42,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 # QLoRA paper Table 9
 # - 16 for 7b & 13b
--- a/examples/falcon/config-7b.yml
+++ b/examples/falcon/config-7b.yml
@@ -1,12 +1,7 @@
 base_model: tiiuae/falcon-7b
-# optionally might have model_type or tokenizer_type
+trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
-trust_remote_code: true

 load_in_8bit: false
 load_in_4bit: false
@@ -33,7 +28,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/falcon-7b
+output_dir: ./falcon-7b
 batch_size: 2
 micro_batch_size: 1
 num_epochs: 4
--- a/examples/gemma/qlora.yml
+++ b/examples/gemma/qlora.yml
@@ -1,10 +1,7 @@
 # use google/gemma-7b if you have access
 base_model: mhenrichsen/gemma-7b
-# optionally might have model_type or tokenizer_type
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -15,7 +12,7 @@ datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
 val_set_size: 0.1
-output_dir: ./outputs/out
+output_dir: ./out

 adapter: qlora
 lora_r: 32
--- a/examples/gemma2/qlora.yml
+++ b/examples/gemma2/qlora.yml
@@ -1,74 +0,0 @@
-base_model: google/gemma-2-9b
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    drop_system_message: true
-    field_messages: conversations
-    message_field_role: from
-    message_field_content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-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: 1
-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: true
-
-gradient_checkpointing: true
-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:
--- a/examples/gemma2/reward-model.yaml
+++ b/examples/gemma2/reward-model.yaml
@@ -1,66 +0,0 @@
-base_model: google/gemma-2-2b
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForSequenceClassification
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-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:
--- a/examples/gptj/qlora.yml
+++ b/examples/gptj/qlora.yml
@@ -1,7 +1,4 @@
 base_model: EleutherAI/gpt-j-6b
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 load_in_4bit: true
 strict: false
@@ -26,7 +23,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 gradient_accumulation_steps: 2
 micro_batch_size: 2
 num_epochs: 2
--- a/examples/jamba/README.md
+++ b/examples/jamba/README.md
@@ -6,5 +6,5 @@
 - ✅ qlora w/ deepspeed Zero-3 needs at least 2x GPUs and 67GiB VRAM (wtf?)
 - ✅ qlora single-gpu, ~51GiB VRAM
 - ✅ multipack
- ✅ FSDP
+- ❓ FSDP
 - ❓ 8-bit LoRA
--- a/examples/jamba/qlora.yaml
+++ b/examples/jamba/qlora.yaml
@@ -1,7 +1,4 @@
 base_model: ai21labs/Jamba-v0.1
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 trust_remote_code: true

 load_in_8bit: false
@@ -13,7 +10,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out

 sequence_len: 4096
 sample_packing: false
--- a/examples/jamba/qlora_deepspeed.yaml
+++ b/examples/jamba/qlora_deepspeed.yaml
@@ -1,6 +1,4 @@
 base_model: ai21labs/Jamba-v0.1
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
 trust_remote_code: true

 load_in_8bit: false
@@ -12,7 +10,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out

 sequence_len: 4096
 sample_packing: false
--- a/examples/jamba/qlora_fsdp_large.yaml
+++ b/examples/jamba/qlora_fsdp_large.yaml
@@ -1,68 +0,0 @@
-base_model: ai21labs/AI21-Jamba-1.5-Large
-# optionally might have model_type or tokenizer_type
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_4bit: true
-strict: false
-use_tensorboard: true
-chat_template: jamba
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    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
-save_safetensors: true
-adapter: qlora
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 16
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: [down_proj,gate_proj,in_proj,k_proj,o_proj,out_proj,q_proj,up_proj,v_proj,x_proj]
-lora_target_linear: false
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 2
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 0.00001
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_limit_all_gathers: true
-  fsdp_sync_module_states: true
-  fsdp_offload_params: false
-  fsdp_use_orig_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: JambaAttentionDecoderLayer,JambaMambaDecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
--- a/examples/jeopardy-bot/config.yml
+++ b/examples/jeopardy-bot/config.yml
@@ -1,10 +1,6 @@
 base_model: huggyllama/llama-7b
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 load_in_8bit: false
 datasets:
  - path: openaccess-ai-collective/jeopardy
@@ -25,7 +21,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/jeopardy-bot-7b
+output_dir: ./jeopardy-bot-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 4
--- a/examples/llama-2/fft_optimized.yml
+++ b/examples/llama-2/fft_optimized.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-output_dir: ./outputs/out
+output_dir: ./out

 sequence_len: 4096
 sample_packing: true
--- a/examples/llama-2/gptq-lora.yml
+++ b/examples/llama-2/gptq-lora.yml
@@ -1,13 +1,8 @@
 base_model: TheBloke/Llama-2-7B-GPTQ
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 gptq: true
 gptq_disable_exllama: true
-
+model_type: AutoModelForCausalLM
+tokenizer_type: LlamaTokenizer
 tokenizer_use_fast: true
 tokenizer_legacy: true
 load_in_8bit: false
@@ -38,7 +33,7 @@ wandb_project:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/model-out
+output_dir: ./model-out
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 4
--- a/examples/llama-2/lisa.yml
+++ b/examples/llama-2/lisa.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-output_dir: ./outputs/lisa-out
+output_dir: ./lisa-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/llama-2/loftq.yml
+++ b/examples/llama-2/loftq.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: true
 load_in_4bit: false
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out

 sequence_len: 4096
 sample_packing: true
--- a/examples/llama-2/qlora-fsdp.yml
+++ b/examples/llama-2/qlora-fsdp.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 adapter: qlora
 lora_model_dir:
@@ -68,14 +65,12 @@ 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_state_dict_type: SHARDED_STATE_DICT
 special_tokens:
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -1,9 +1,6 @@
 base_model: NousResearch/Llama-2-7b-hf
-# optionally might have model_type or tokenizer_type
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name

 load_in_8bit: false
 load_in_4bit: true
@@ -14,7 +11,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out

 adapter: qlora
 lora_model_dir:
--- a/examples/llama-2/relora.yml
+++ b/examples/llama-2/relora.yml
@@ -12,7 +12,7 @@ datasets:
    type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/relora-out
+output_dir: ./relora-out

 adapter: qlora
 lora_model_dir:
--- a/examples/llama-3-vision/lora-11b.yaml
+++ b/examples/llama-3-vision/lora-11b.yaml
@@ -1,67 +0,0 @@
-base_model: alpindale/Llama-3.2-11B-Vision-Instruct
-# optionally might have model_type or tokenizer_type or processor_type
-processor_type: AutoProcessor
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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:
--- a/examples/llama-3/README.md
+++ b/examples/llama-3/README.md
@@ -1,13 +0,0 @@
-# Llama-3
-
-https://llama.meta.com/llama3/
-
-[8B Base Model](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- - [Full Fine Tune](./fft-8b.yaml)
-   - Single GPU @ 48GB VRAM
- - [LoRA](./lora-8b.yml)
-   - Single GPU @ 11GB VRAM
-
-[70B Base Model](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
- - [QLORA+FSDP](./qlora-fsdp-70b.yaml)
-   - Dual GPU @ 21GB VRAM
--- a/examples/llama-3/fft-8b-liger-fsdp.yaml
+++ b/examples/llama-3/fft-8b-liger-fsdp.yaml
@@ -1,82 +0,0 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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|>
--- a/examples/llama-3/fft-8b.yaml
+++ b/examples/llama-3/fft-8b.yaml
@@ -1,58 +0,0 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-output_dir: ./outputs/out
-
-sequence_len: 8192
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: <|end_of_text|>
--- a/examples/llama-3/instruct-dpo-lora-8b.yml
+++ b/examples/llama-3/instruct-dpo-lora-8b.yml
@@ -1,83 +0,0 @@
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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
-
-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: 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:
--- a/examples/llama-3/instruct-lora-8b.yml
+++ b/examples/llama-3/instruct-lora-8b.yml
@@ -1,80 +0,0 @@
-base_model: NousResearch/Meta-Llama-3-8B-Instruct
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: llama3
-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: 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|>
--- a/examples/llama-3/lora-1b-deduplicate-dpo.yml
+++ b/examples/llama-3/lora-1b-deduplicate-dpo.yml
@@ -1,98 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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:
--- a/examples/llama-3/lora-1b-deduplicate-sft.yml
+++ b/examples/llama-3/lora-1b-deduplicate-sft.yml
@@ -1,79 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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|>
--- a/examples/llama-3/lora-1b.yml
+++ b/examples/llama-3/lora-1b.yml
@@ -1,76 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-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|>"
--- a/examples/llama-3/lora-8b.yml
+++ b/examples/llama-3/lora-8b.yml
@@ -1,74 +0,0 @@
-base_model: NousResearch/Meta-Llama-3-8B
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/lora-out
-
-sequence_len: 4096
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_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|>
--- a/examples/llama-3/qlora-1b-kto.yaml
+++ b/examples/llama-3/qlora-1b-kto.yaml
@@ -1,77 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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|>"
--- a/examples/llama-3/qlora-1b.yml
+++ b/examples/llama-3/qlora-1b.yml
@@ -1,78 +0,0 @@
-base_model: NousResearch/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-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|>"
--- a/examples/llama-3/qlora-fsdp-405b.yaml
+++ b/examples/llama-3/qlora-fsdp-405b.yaml
@@ -1,66 +0,0 @@
-base_model: hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
-# optionally might have model_type or tokenizer_type
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out/qlora-llama3_1-405b
-save_safetensors: true
-
-adapter: qlora
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 16
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 2
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 0.00001
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: true
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-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
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
--- a/examples/llama-3/qlora-fsdp-70b.yaml
+++ b/examples/llama-3/qlora-fsdp-70b.yaml
@@ -1,83 +0,0 @@
-base_model: casperhansen/llama-3-70b-fp16
-# optionally might have model_type or tokenizer_type
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-output_dir: ./outputs/out/qlora-llama3-70b
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 512
-sample_packing: false
-pad_to_sequence_len: true
-
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-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: 1
-num_epochs: 4
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 0.00001
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-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: 10
-evals_per_epoch: 4
-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
-special_tokens:
-  pad_token: <|end_of_text|>
--- a/examples/llama-3/qlora.yml
+++ b/examples/llama-3/qlora.yml
@@ -1,70 +0,0 @@
-base_model: NousResearch/Meta-Llama-3-8B
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: aaditya/alpaca_subset_1
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-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: paged_adamw_32bit
-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:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|end_of_text|>"
--- a/Show More
+++ b/Show More