run 2.5.1 test without waiting for 1st e2e

.github/workflows/base.yml

@@ -22,36 +22,38 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "121"
-            cuda_version: 12.1.1
-            cudnn_version: 8
-            python_version: "3.10"
-            pytorch: 2.3.1
-            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: ""
-            python_version: "3.10"
-            pytorch: 2.4.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.4.1
-            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: "3.10"
+#            pytorch: 2.3.1
+#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+#            from_base_img: ""
+#            from_base_tag: ""
+#          - 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"
+#            from_base_img: ""
+#            from_base_tag: ""
+#          - cuda: "124"
+#            cuda_version: 12.4.1
+#            cudnn_version: ""
+#            python_version: "3.11"
+#            pytorch: 2.4.1
+#            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+#            from_base_img: ""
+#            from_base_tag: ""
           - cuda: "124"
             cuda_version: 12.4.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.5.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            from_base_img: nvcr.io/nvidia/pytorch
+            from_base_tag: 24.10-py3
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -61,7 +63,7 @@ jobs:
         with:
           images: |
             winglian/axolotl-base
-            axolotlai/axolotl-base
+#            axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
@@ -74,7 +76,8 @@ jobs:
         with:
           context: .
           file: ./docker/Dockerfile-base
-          push: ${{ github.event_name != 'pull_request' }}
+          push: true
+#          push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
           build-args: |
@@ -84,3 +87,5 @@ jobs:
             PYTHON_VERSION=${{ matrix.python_version }}
             PYTORCH_VERSION=${{ matrix.pytorch }}
             TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
+            BASE_IMAGE=${{ matrix.from_base_img || '' }}
+            BASE_TAG=${{ matrix.from_base_tag || '' }}

.github/workflows/lint.yml

@@ -1,7 +1,6 @@
 name: lint
 on:
   # check on PRs, and manual triggers
-  merge_group:
   pull_request:
       paths:
        - '**.py'

.github/workflows/main.yml

@@ -25,6 +25,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras: mamba-ssm
+            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -35,7 +36,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -92,6 +92,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.3.1
             axolotl_extras:
+            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -102,7 +103,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -52,7 +52,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal==0.63.64 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

.github/workflows/pypi.yml

@@ -13,13 +13,10 @@ jobs:
     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
+        run: gh release create "$GITHUB_REF_NAME" # GITHUB_REF_NAME is the tag name in `on.push.tags` workflows
   pypi-publish:
     name: Upload release to PyPI
     runs-on: ubuntu-latest
@@ -41,7 +38,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 install wheel packaging
-          pip3 install --no-build-isolation -e .
+          pip3 install -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
       - name: Extract tag name

.github/workflows/tests-nightly.yml

@@ -44,11 +44,6 @@ jobs:
           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
@@ -65,15 +60,11 @@ jobs:
         run: |
           pip3 install --upgrade pip
           pip3 install --upgrade packaging
-          pip3 install --no-build-isolation -U -e .
+          pip3 install -U -e .
           python scripts/unsloth_install.py | sh
           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 '${{ matrix.pytorch_version }}' in torch.__version__"
-
       - name: Ensure axolotl CLI was installed
         run: |
           axolotl --help
@@ -129,7 +120,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal==0.63.64 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

.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"
@@ -61,14 +60,56 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
+      - name: Setup Python
+        uses: actions/setup-python@v5
         with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
+          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 -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: 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 {} \;
+
+  pytest-sdist:
+    name: PyTest from Source Dist
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      max-parallel: 1
+      matrix:
+        python_version: ["3.11"]
+        pytorch_version: ["2.4.1", "2.5.1"]
+    timeout-minutes: 20
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -88,168 +129,83 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 show torch
-          pip3 install --no-build-isolation -U -e .
+          python3 setup.py sdist
+          pip3 install 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/
+          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 {} \;
 
-      - 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
+#  docker-e2e-tests-1st:
+#    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
+#    # this job needs to be run on self-hosted GPU runners...
+#    runs-on: [self-hosted, modal]
+#    timeout-minutes: 90
+#    needs: [pre-commit, pytest, pytest-sdist]
+#
+#    strategy:
+#      fail-fast: false
+#      matrix:
+#        include:
+#          - cuda: 124
+#            cuda_version: 12.4.1
+#            python_version: "3.11"
+#            pytorch: 2.4.1
+#            num_gpus: 1
+#            axolotl_extras:
+#    steps:
+#      - name: Checkout
+#        uses: actions/checkout@v4
+#      - name: Install Python
+#        uses: actions/setup-python@v5
+#        with:
+#          python-version: "3.10"
+#      - name: Install Modal
+#        run: |
+#          python -m pip install --upgrade pip
+#          pip install modal==0.63.64 jinja2
+#      - name: Update env vars
+#        run: |
+#          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+#          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
+#          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
+#          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
+#          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
+#          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+#      - name: Run tests job on Modal
+#        run: |
+#          modal run cicd.tests
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 90
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+#    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    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
+#          - cuda: 121
+#            cuda_version: 12.1.1
+#            python_version: "3.10"
+#            pytorch: 2.3.1
+#            num_gpus: 1
+#            axolotl_extras: mamba-ssm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -266,10 +222,10 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          pip install modal==0.63.64 jinja2
       - name: Update env vars
         run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
+          echo "BASE_TAG=pr-2139-base-py${{ 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

.gitignore

@@ -1,7 +1,6 @@
 **/axolotl.egg-info
 configs
 last_run_prepared/
-outputs
 .vscode
 _site/
 
@@ -186,6 +185,3 @@ out/
 
 # vim
 *.swp
-
-# symlinked to axolotl-artifacts in docker containers
-outputs

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ 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

.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

MANIFEST.in

@@ -1,5 +1,4 @@
 include requirements.txt
 include README.md
 include LICENSE
-include src/setuptools_axolotl_dynamic_dependencies.py
 recursive-include axolotl *.py

README.md

@@ -10,13 +10,9 @@
     <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/>
+</p>
+<p align="center">
     <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>
@@ -46,8 +42,7 @@ Features:
 - [Axolotl](#axolotl)
   - [Table of Contents](#table-of-contents)
   - [Quickstart ⚡](#quickstart-)
-    - [Edge Builds](#edge-builds-)
-    - [Axolotl CLI Usage](#axolotl-cli-usage)
+    - [Usage](#usage)
   - [Badge ❤🏷️](#badge-️)
   - [Contributing 🤝](#contributing-)
   - [Sponsors 🤝❤](#sponsors-)
@@ -112,49 +107,58 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 **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
+git clone https://github.com/axolotl-ai-cloud/axolotl
 cd axolotl
+
 pip3 install packaging ninja
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+pip3 install -e '.[flash-attn,deepspeed]'
 ```
 
-### Axolotl CLI Usage
-We now support a new, more streamlined CLI using [click](https://click.palletsprojects.com/en/stable/).
+### Usage
+```bash
+# preprocess datasets - optional but recommended
+CUDA_VISIBLE_DEVICES="0" python -m axolotl.cli.preprocess examples/openllama-3b/lora.yml
+
+# finetune lora
+accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
+
+# inference
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out"
+
+# gradio
+accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
+    --lora_model_dir="./outputs/lora-out" --gradio
+
+# remote yaml files - the yaml config can be hosted on a public URL
+# Note: the yaml config must directly link to the **raw** yaml
+accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
+```
+
+### Axolotl CLI
+
+If you've installed this package using `pip` from source, we now support a new, more
+streamlined CLI using [click](https://click.palletsprojects.com/en/stable/). Rewriting
+the above commands:
 
 ```bash
 # preprocess datasets - optional but recommended
-CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/llama-3/lora-1b.yml
+CUDA_VISIBLE_DEVICES="0" axolotl preprocess examples/openllama-3b/lora.yml
 
 # finetune lora
-axolotl train examples/llama-3/lora-1b.yml
+axolotl train examples/openllama-3b/lora.yml
 
 # inference
-axolotl inference examples/llama-3/lora-1b.yml \
+axolotl inference examples/openllama-3b/lora.yml \
     --lora-model-dir="./outputs/lora-out"
 
 # gradio
-axolotl inference examples/llama-3/lora-1b.yml \
+axolotl inference examples/openllama-3b/lora.yml \
     --lora-model-dir="./outputs/lora-out" --gradio
 
 # remote yaml files - the yaml config can be hosted on a public URL
 # Note: the yaml config must directly link to the **raw** yaml
-axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/llama-3/lora-1b.yml
+axolotl train https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
 ```
 
 We've also added a new command for fetching `examples` and `deepspeed_configs` to your
@@ -171,36 +175,6 @@ axolotl fetch deepspeed_configs
 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.
@@ -320,7 +294,7 @@ 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
@@ -399,7 +373,7 @@ 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)
 

cicd/Dockerfile.jinja

@@ -1,4 +1,4 @@
-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 }}"
@@ -8,7 +8,6 @@ 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
@@ -32,9 +31,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -1,9 +1,7 @@
 #!/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 /workspace/axolotl/tests/e2e/patched/
-pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
+pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/patched/
+pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -1,6 +1,6 @@
 """
-modal application to run axolotl gpu tests in Modal
-"""
+ modal application to run axolotl gpu tests in Modal
+ """
 # pylint: disable=duplicate-code
 
 import os
@@ -28,7 +28,6 @@ df_args = {
     "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)
@@ -49,12 +48,6 @@ cicd_image = (
 
 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)
@@ -74,7 +67,6 @@ def run_cmd(cmd: str, run_folder: str):
     timeout=60 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
-    volumes=VOLUME_CONFIG,
 )
 def cicd_pytest():
     run_cmd("./cicd/multigpu.sh", "/workspace/axolotl")

cicd/tests.py

@@ -29,7 +29,6 @@ df_args = {
     "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)
@@ -51,12 +50,6 @@ cicd_image = (
 
 app = App("Axolotl CI/CD", secrets=[])
 
-hf_cache_volume = modal.Volume.from_name(
-    "axolotl-ci-hf-hub-cache", create_if_missing=True
-)
-VOLUME_CONFIG = {
-    "/workspace/data/huggingface-cache/hub": hf_cache_volume,
-}
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
 GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
@@ -76,7 +69,6 @@ def run_cmd(cmd: str, run_folder: str):
     timeout=60 * 60,
     cpu=8.0,
     memory=131072,
-    volumes=VOLUME_CONFIG,
 )
 def cicd_pytest():
     run_cmd("./cicd/cicd.sh", "/workspace/axolotl")

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
-}

docker/Dockerfile

@@ -1,5 +1,6 @@
+ARG BASE_IMAGE=axolotlai/axolotl-base
 ARG BASE_TAG=main-base
-FROM axolotlai/axolotl-base:$BASE_TAG
+FROM $BASE_IMAGE:$BASE_TAG
 
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
@@ -20,9 +21,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,optimizers] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile-base

@@ -3,7 +3,10 @@ ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
 
-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+ARG BASE_IMAGE=nvidia/cuda
+ARG DEFAULT_TAG=${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_TAG=""
+FROM ${BASE_IMAGE:-nvidia/cuda}:${BASE_TAG:-${DEFAULT_TAG}} AS base-builder
 
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
@@ -16,7 +19,7 @@ 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 \

docker/Dockerfile-tests

@@ -24,9 +24,9 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docs/amd_hpc.qmd

@@ -52,7 +52,7 @@ 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 .
+pip install .
 ```
 
 ### 6. Install Axolotl
@@ -63,7 +63,7 @@ Clone and install Axolotl:
 git clone https://github.com/axolotl-ai-cloud/axolotl
 cd axolotl
 pip install packaging ninja
-pip install --no-build-isolation -e .
+pip install -e .
 ```
 
 ### 7. Apply xformers Workaround

docs/config.qmd

@@ -127,40 +127,34 @@ datasets:
     # - 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.
+    # Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
     chat_template_jinja:
-
-    # Key containing the messages (default: "messages")
+    # The key in the data example that contains the messages. Default is "messages".
     field_messages: messages
-    # Key for role in each message (default: "role")
+    # The key in the message turn that contains the role. Default is "role".
     message_field_role: role
-    # Key for content in each message (default:  "content")
+    # The key in the message turn that contains the content. Default is "content".
     message_field_content: content
-
-    # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
+    # Optional[Dict[str, List]]. Roles mapping for the messages.
     roles:
       user: ["human", "user"]
-      assistant: ["gpt", "assistant"]
+      assistant: ["gpt", "assistant", "ai"]
       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.
+    ## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
 
     # Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
-    roles_to_train: ["assistant"]  # default
+    roles_to_train: ["gpt", "assistant"]
     # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
     # - all: train on all EOS tokens
-    # - turn (default): train on the EOS token at the end of each trainable turn
+    # - turn: train on the EOS token at the end of each trainable turn
     # - last: train on the last EOS token in the conversation
     train_on_eos: last
     # The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
     message_field_training: training
     # The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
     # The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
+    # See example at `docs/dataset-formats/conversation.qmd`
     message_field_training_detail: train_detail
 
 
@@ -245,9 +239,6 @@ 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
 device_map:
@@ -340,8 +331,7 @@ comet_experiment_config: # Dictionary for additional configuration settings, see
 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
@@ -373,10 +363,6 @@ eval_table_size: # Approximate number of predictions sent to wandb depending on
 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
-
 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)
 

docs/dataset-formats/conversation.qmd

@@ -68,8 +68,6 @@ We recommend checking the below examples for other usecases.
 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.
@@ -79,7 +77,7 @@ chat_template: gemma # this overwrites the tokenizer's chat_template
 datasets:
   - path: ...
     type: chat_template
-    roles_to_train: ["assistant"]  # default value
+    roles_to_train: ["assistant"]
 ```
 
 3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
@@ -89,6 +87,7 @@ chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer
 datasets:
   - path: ...
     type: chat_template
+    roles_to_train: ["assistant"]
 ```
 
 4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
@@ -100,6 +99,7 @@ chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message
 datasets:
   - path: ...
     type: chat_template
+    roles_to_train: ["assistant"]
 ```
 
 5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation

docs/debugging.qmd

@@ -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
@@ -212,7 +212,7 @@ 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

docs/rlhf.qmd

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

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

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

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

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

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

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

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

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

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

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install --no-build-isolation axolotl[deepspeed]"
+    "!pip install axolotl[deepspeed]"
    ]
   },
   {

examples/dbrx/16bit-lora.yaml

@@ -1,7 +1,4 @@
 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

examples/dbrx/8bit-lora.yaml

@@ -1,7 +1,4 @@
 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

examples/dbrx/fft-ds-zero3.yaml

@@ -1,7 +1,4 @@
 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

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

@@ -1,6 +1,4 @@
 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

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

@@ -1,7 +1,4 @@
 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

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

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

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

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

examples/gemma2/qlora.yml

@@ -1,9 +1,6 @@
 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

examples/gemma2/reward-model.yaml

@@ -1,9 +1,6 @@
 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

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

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

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

examples/jamba/qlora_fsdp_large.yaml

@@ -1,8 +1,5 @@
 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

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

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

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

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

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

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

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

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

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

@@ -1,9 +1,5 @@
 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

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

@@ -1,6 +1,4 @@
 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

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

@@ -1,6 +1,4 @@
 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

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

@@ -1,9 +1,6 @@
 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

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

@@ -1,9 +1,6 @@
 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

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

@@ -1,9 +1,6 @@
 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

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

@@ -1,9 +1,6 @@
 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

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|>"

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

@@ -1,9 +1,6 @@
 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

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|>"

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

@@ -1,6 +1,4 @@
-base_model: NousResearch/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
+base_model: meta-llama/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
@@ -24,6 +22,7 @@ pad_to_sequence_len: true
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
+lora_target_linear: true
 lora_fan_in_fan_out:
 lora_target_modules:
   - gate_proj

examples/llama-3/qlora-fsdp-405b.yaml

@@ -1,8 +1,5 @@
 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

examples/llama-3/qlora-fsdp-70b.yaml

@@ -1,9 +1,6 @@
 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

examples/llama-3/qlora.yml

@@ -1,9 +1,6 @@
 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

examples/mamba/config.yml

@@ -1,10 +1,7 @@
 base_model: state-spaces/mamba-2.8b
-# optionally might have model_type or tokenizer_type or tokenizer_config
 model_type: MambaLMHeadModel
 tokenizer_type: AutoTokenizer
 tokenizer_config: EleutherAI/gpt-neox-20b
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
 
 load_in_8bit: false
 load_in_4bit: false

examples/mistral/bigstral-ds-zero3.yaml

@@ -1,10 +1,6 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
-# 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
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/config.yml

@@ -1,9 +1,6 @@
 base_model: mistralai/Mistral-7B-v0.1
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mistral/lora-mps.yml

@@ -1,9 +1,6 @@
 base_model: mistralai/Mistral-7B-v0.1
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mistral/lora.yml

@@ -1,9 +1,6 @@
 base_model: mistralai/Mistral-7B-v0.1
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mistral/mistral-dpo-qlora.yml

@@ -4,11 +4,8 @@
 #face problems with the special tokens.
 
 base_model: mistralai/Mistral-7B-Instruct-v0.2
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mistral/mistral-qlora-fsdp.yml

@@ -1,10 +1,6 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
-# 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
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mistral-qlora-orpo.yml

@@ -1,9 +1,6 @@
 base_model: mistralai/Mistral-7B-v0.1
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mistral/mixtral-8x22b-qlora-fsdp.yml

@@ -1,9 +1,6 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
-# 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
 
 load_in_8bit: false
 load_in_4bit: true

examples/mistral/mixtral-qlora-fsdp.yml

@@ -1,10 +1,6 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
-# 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
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mixtral.yml

@@ -1,10 +1,6 @@
 base_model: mistralai/Mixtral-8x7B-v0.1
-# 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
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/mixtral_22.yml

@@ -1,10 +1,6 @@
 base_model: mistral-community/Mixtral-8x22B-v0.1
-# 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
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/mistral/qlora.yml

@@ -1,9 +1,6 @@
 base_model: mistralai/Mistral-7B-v0.1
-# optionally might have model_type or tokenizer_type
 model_type: MistralForCausalLM
 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

examples/mpt-7b/config.yml

@@ -1,9 +1,5 @@
 base_model: mosaicml/mpt-7b
-# 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
-
 trust_remote_code: true  # required for mpt as their model class is not merged into transformers yet
 load_in_8bit: false
 datasets:

examples/openllama-3b/config.yml

@@ -1,10 +1,6 @@
 base_model: openlm-research/open_llama_3b_v2
-# 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
 strict: false

examples/openllama-3b/lora.yml

@@ -1,10 +1,6 @@
 base_model: openlm-research/open_llama_3b_v2
-# 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
 strict: false

examples/openllama-3b/qlora.yml

@@ -1,10 +1,6 @@
 base_model: openlm-research/open_llama_3b_v2
-# 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
 strict: false

examples/phi/lora-3.5.yaml

@@ -1,9 +1,6 @@
 base_model: microsoft/Phi-3.5-mini-instruct
-# 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: true
 load_in_4bit: false

examples/phi/phi-ft.yml

@@ -1,9 +1,6 @@
 base_model: microsoft/phi-1_5
-# 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: false

examples/phi/phi-qlora.yml

@@ -1,9 +1,6 @@
 base_model: microsoft/phi-1_5
-# 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

examples/phi/phi2-ft.yml

@@ -1,9 +1,6 @@
 base_model: microsoft/phi-2
-# 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: false

examples/phi/phi3-ft-fsdp.yml

@@ -1,9 +1,6 @@
 base_model: microsoft/Phi-3-mini-4k-instruct
-# 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: false

examples/phi/phi3-ft.yml

@@ -1,11 +1,7 @@
 base_model: microsoft/Phi-3-mini-4k-instruct
-# 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
-
 chat_template: phi_3
 
 load_in_8bit: false

examples/pythia-12b/config.yml

@@ -1,11 +1,7 @@
 base_model: EleutherAI/pythia-12b-deduped
 base_model_ignore_patterns: pytorch*  # prefer safetensors
-# optionally might have model_type or tokenizer_type
 model_type: GPTNeoXForCausalLM
 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
 gptq: false

examples/pythia/lora.yml

@@ -1,7 +1,4 @@
 base_model: EleutherAI/pythia-1.4b-deduped
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 load_in_8bit: true
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/qwen/lora.yml

@@ -1,9 +1,6 @@
 base_model: Qwen/Qwen-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
 
 trust_remote_code: true
 

examples/qwen/qlora.yml

@@ -1,9 +1,6 @@
 base_model: Qwen/Qwen-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
 
 trust_remote_code: true
 

examples/qwen/qwen2-moe-lora.yaml

@@ -1,7 +1,4 @@
 base_model: Qwen/Qwen1.5-MoE-A2.7B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 trust_remote_code: true
 
 load_in_8bit: false

examples/qwen/qwen2-moe-qlora.yaml

@@ -1,7 +1,4 @@
 base_model: Qwen/Qwen1.5-MoE-A2.7B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
 trust_remote_code: true
 
 load_in_8bit: false