add support for NCA

.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
 

.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

.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

.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?

.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

.github/workflows/base.yml

@@ -5,7 +5,7 @@ on:
 
 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:
@@ -30,18 +30,13 @@ jobs:
           - cuda: "121"
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
+            pytorch: 2.2.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "121"
             cuda_version: 12.1.0
             python_version: "3.11"
             pytorch: 2.3.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            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"
     steps:
       - name: Checkout
         uses: actions/checkout@v3

.github/workflows/main.yml

@@ -8,7 +8,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
     strategy:
       fail-fast: false
       matrix:
@@ -19,6 +19,7 @@ jobs:
             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"
@@ -27,14 +28,13 @@ jobs:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
+            pytorch: 2.2.1
             axolotl_extras:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.3.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -70,7 +70,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -80,6 +80,7 @@ jobs:
             python_version: "3.10"
             pytorch: 2.1.2
             axolotl_extras:
+            is_latest: true
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.10"
@@ -88,14 +89,13 @@ jobs:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
+            pytorch: 2.2.1
             axolotl_extras:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.3.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -125,45 +125,3 @@ 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: 12.1.0
-            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
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-      - name: Build
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-            CUDA=${{ matrix.cuda }}
-          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 }}

.github/workflows/nightlies.yml

@@ -7,7 +7,7 @@ on:
 
 jobs:
   build-axolotl:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
     strategy:
       fail-fast: false
       matrix:
@@ -18,6 +18,7 @@ jobs:
             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"
@@ -26,14 +27,13 @@ jobs:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
+            pytorch: 2.2.1
             axolotl_extras:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.3.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -70,7 +70,7 @@ jobs:
 
   build-axolotl-cloud:
     needs: build-axolotl
-    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip docker]]') && github.repository_owner == 'OpenAccess-AI-Collective' }}
     # this job needs to be run on self-hosted GPU runners...
     strategy:
       matrix:
@@ -80,6 +80,7 @@ jobs:
             python_version: "3.10"
             pytorch: 2.1.2
             axolotl_extras:
+            is_latest: true
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.10"
@@ -88,14 +89,13 @@ jobs:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
+            pytorch: 2.2.1
             axolotl_extras:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.3.0
             axolotl_extras:
-            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests.yml

@@ -58,7 +58,7 @@ jobs:
           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: 60
@@ -82,12 +82,7 @@ jobs:
           - cuda: 121
             cuda_version: 12.1.0
             python_version: "3.11"
-            pytorch: 2.2.2
-            num_gpus: 1
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.1
+            pytorch: 2.2.1
             num_gpus: 1
     steps:
       - name: Checkout

.gitignore

@@ -176,9 +176,3 @@ qlora-out/*
 mlruns/*
 
 /.quarto/
-prepared-datasets/
-submit.sh
-*.out*
-
-typings/
-out/

README.md

@@ -34,7 +34,6 @@ Features:
   - [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)
   - [Train](#train)
@@ -67,8 +66,8 @@ 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>
 
@@ -107,7 +106,7 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 **Requirements**: Python >=3.10 and Pytorch >=2.1.1.
 
 ```bash
-git clone https://github.com/axolotl-ai-cloud/axolotl
+git clone https://github.com/OpenAccess-AI-Collective/axolotl
 cd axolotl
 
 pip3 install packaging ninja
@@ -124,15 +123,15 @@ 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"
+    --lora_model_dir="./lora-out"
 
 # gradio
 accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./outputs/lora-out" --gradio
+    --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/axolotl-ai-cloud/axolotl/main/examples/openllama-3b/lora.yml
+accelerate launch -m axolotl.cli.train https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/examples/openllama-3b/lora.yml
 ```
 
 ## Advanced Setup
@@ -293,47 +292,11 @@ 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: winglian/axolotl-cloud:main-20240429-py3.11-cu121-2.2.2
-
-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 [these docs](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
 
@@ -609,7 +572,7 @@ 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
 
@@ -626,10 +589,10 @@ Need dedicated support? Please contact us at [✉️wing@openaccessaicollective.
 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/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/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/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
 
@@ -647,7 +610,7 @@ PocketDoc Labs
 
 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.
+Bugs? Please check the [open issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues/bug) else create a new Issue.
 
 PRs are **greatly welcome**!
 
@@ -665,7 +628,7 @@ 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">
+<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>
 

_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
 

cicd/Dockerfile.jinja

@@ -14,7 +14,7 @@ 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
 

cicd/cicd.sh

@@ -1,5 +1,4 @@
 #!/bin/bash
-set -e
 
 pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
 pytest /workspace/axolotl/tests/e2e/patched/

docker/Dockerfile

@@ -11,11 +11,11 @@ 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
 

docker/Dockerfile-cloud-no-tmux

@@ -1,27 +0,0 @@
-ARG BASE_TAG=main
-FROM winglian/axolotl:$BASE_TAG
-
-ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
-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"
-
-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"]

docker/Dockerfile-tests

@@ -16,7 +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
 

docs/config.qmd

@@ -138,7 +138,7 @@ test_datasets:
     data_files:
       - /workspace/data/eval.jsonl
 
-# use RL training: 'dpo', 'ipo', 'kto'
+# use RL training: 'dpo', 'ipo', 'kto_pair', 'orpo', 'sppo_hard', 'nca_pair'
 rl:
 
 # Saves the desired chat template to the tokenizer_config.json for easier inferencing
@@ -186,11 +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
 
 # Passed through to transformers when loading the model when launched without accelerate
 # Use `sequential` when training w/ model parallelism to limit memory
@@ -290,7 +285,7 @@ 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

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`.
-- Do not add BOS/EOS. Axolotl will add them for you based on the default tokenizer for the model you're using.
-- For pretraining, do not truncate/pad documents to the context window length.
-- For instruction training, documents must be truncated/padded as desired.
-
-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: ...
 ```

docs/debugging.qmd

@@ -192,7 +192,7 @@ Using [official Axolotl Docker images](https://hub.docker.com/r/winglian/axolotl
 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
 ```
 

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 )

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.

examples/cerebras/btlm-ft.yml

@@ -38,7 +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

examples/cerebras/qlora.yml

@@ -25,7 +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

examples/code-llama/13b/lora.yml

@@ -11,7 +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

examples/code-llama/13b/qlora.yml

@@ -11,7 +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:

examples/code-llama/34b/lora.yml

@@ -11,7 +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

examples/code-llama/34b/qlora.yml

@@ -11,7 +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:

examples/code-llama/7b/lora.yml

@@ -11,7 +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

examples/code-llama/7b/qlora.yml

@@ -11,7 +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:

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

@@ -1,223 +1,216 @@
 {
- "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/"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKjdG7tbTb-n"
+      },
+      "source": [
+        "# Example notebook for running Axolotl on google colab"
+      ]
     },
-    "id": "3c3yGAwnOIdi",
-    "outputId": "e3777b5a-40ef-424f-e181-62dfecd1dd01"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install torch==\"2.1.2\"\n",
-    "!pip install -e git+https://github.com/axolotl-ai-cloud/axolotl#egg=axolotl\n",
-    "!pip install flash-attn==\"2.5.0\"\n",
-    "!pip install deepspeed==\"0.13.1\"!pip install mlflow==\"2.13.0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "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",
-    "\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: ./outputs/qlora-out\n",
-    "\n",
-    "adapter: qlora\n",
-    "lora_model_dir:\n",
-    "\n",
-    "sequence_len: 4096\n",
-    "sample_packing: true\n",
-    "eval_sample_packing: false\n",
-    "pad_to_sequence_len: true\n",
-    "\n",
-    "lora_r: 32\n",
-    "lora_alpha: 16\n",
-    "lora_dropout: 0.05\n",
-    "lora_target_modules:\n",
-    "lora_target_linear: true\n",
-    "lora_fan_in_fan_out:\n",
-    "\n",
-    "wandb_project:\n",
-    "wandb_entity:\n",
-    "wandb_watch:\n",
-    "wandb_name:\n",
-    "wandb_log_model:\n",
-    "\n",
-    "gradient_accumulation_steps: 4\n",
-    "micro_batch_size: 2\n",
-    "num_epochs: 4\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: auto\n",
-    "fp16:\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: true\n",
-    "\n",
-    "warmup_steps: 10\n",
-    "evals_per_epoch: 4\n",
-    "saves_per_epoch: 1\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/"
+    {
+      "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)"
+      ]
     },
-    "id": "ydTI2Jk2RStU",
-    "outputId": "d6d0df17-4b53-439c-c802-22c0456d301b"
-   },
-   "outputs": [],
-   "source": [
-    "# By using the ! the comand will be executed as a bash command\n",
-    "!accelerate launch -m axolotl.cli.train /content/test_axolotl.yaml"
-   ]
+    {
+      "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": "markdown",
-   "metadata": {},
-   "source": [
-    "## Play with inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# By using the ! the comand will be executed as a bash command\n",
-    "!accelerate launch -m axolotl.cli.inference /content/test_axolotl.yaml \\\n",
-    "    --qlora_model_dir=\"./qlora-out\" --gradio"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

examples/dbrx/16bit-lora.yaml

@@ -10,7 +10,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 512
 sample_packing: false

examples/dbrx/8bit-lora.yaml

@@ -10,7 +10,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 512
 sample_packing: false

examples/dbrx/fft-ds-zero3.yaml

@@ -10,7 +10,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 512
 sample_packing: false

examples/falcon/config-7b-lora.yml

@@ -28,7 +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

examples/falcon/config-7b-qlora.yml

@@ -42,7 +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

examples/falcon/config-7b.yml

@@ -28,7 +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

examples/gemma/qlora.yml

@@ -12,7 +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

examples/gemma2/qlora.yml

@@ -1,68 +0,0 @@
-base_model: google/gemma-2-9b
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    chat_template: gemma
-    drop_system_message: true
-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:

examples/gptj/qlora.yml

@@ -23,7 +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

examples/jamba/qlora.yaml

@@ -10,7 +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

examples/jamba/qlora_deepspeed.yaml

@@ -10,7 +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

examples/jeopardy-bot/config.yml

@@ -21,7 +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

examples/llama-2/fft_optimized.yml

@@ -11,7 +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

examples/llama-2/gptq-lora.yml

@@ -33,7 +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

examples/llama-2/lisa.yml

@@ -11,7 +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

examples/llama-2/loftq.yml

@@ -11,7 +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

examples/llama-2/lora.yml

@@ -11,7 +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

examples/llama-2/qlora-fsdp.yml

@@ -11,7 +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:

examples/llama-2/qlora.yml

@@ -11,7 +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:

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:

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

@@ -11,7 +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: 8192
 sample_packing: true

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

@@ -1,76 +0,0 @@
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-chat_template: llama3
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-    chat_template: llama3
-    field_messages: messages
-    message_field_role: role
-    message_field_content: content
-    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:

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

@@ -11,11 +11,10 @@ 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
-eval_sample_packing: false
 pad_to_sequence_len: true
 
 adapter: lora
@@ -25,9 +24,6 @@ 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:

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

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-output_dir: ./outputs/out/qlora-llama3-70b
+output_dir: ./out/qlora-llama3-70b
 
 adapter: qlora
 lora_model_dir:

examples/llama-3/qlora.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 adapter: qlora
 lora_model_dir:

examples/mamba/config.yml

@@ -12,7 +12,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.0
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 2048
 sample_packing: false

examples/mistral/bigstral-ds-zero3.yaml

@@ -23,7 +23,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 2048
 sample_packing: true

examples/mistral/config.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 8192
 sample_packing: true

examples/mistral/lora-mps.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 eval_sample_packing: false
 
 adapter: lora

examples/mistral/lora.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 
 adapter: lora
 lora_model_dir:

examples/mistral/mistral-qlora-fsdp.yml

@@ -12,7 +12,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 model_config:
   output_router_logits: true

examples/mistral/mistral-qlora-orpo.yml

@@ -16,7 +16,7 @@ datasets:
     type: chat_template.argilla
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
-output_dir: ./outputs/mistral-qlora-orpo-out
+output_dir: ./mistral-qlora-orpo-out
 
 adapter: qlora
 lora_model_dir:

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

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 model_config:
   output_router_logits: true

examples/mistral/mixtral-qlora-fsdp.yml

@@ -12,7 +12,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 model_config:
   output_router_logits: true

examples/mistral/mixtral.yml

@@ -12,7 +12,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 ## You can optionally freeze the entire model and unfreeze a subset of parameters
 unfrozen_parameters:

examples/mistral/mixtral_22.yml

@@ -21,7 +21,7 @@ model_config:
 datasets:
   - path: yahma/alpaca-cleaned
     type: alpaca
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 8000
 sample_packing: true

examples/mistral/qlora.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 
 adapter: qlora
 lora_model_dir:

examples/mpt-7b/config.yml

@@ -23,7 +23,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/mpt-alpaca-7b
+output_dir: ./mpt-alpaca-7b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 4

examples/openllama-3b/config.yml

@@ -25,7 +25,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/openllama-out
+output_dir: ./openllama-out
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 4

examples/openllama-3b/lora.yml

@@ -31,7 +31,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 gradient_accumulation_steps: 1
 micro_batch_size: 2
 num_epochs: 4

examples/openllama-3b/qlora.yml

@@ -25,7 +25,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 gradient_accumulation_steps: 1
 micro_batch_size: 2
 num_epochs: 4

examples/phi/phi-ft.yml

@@ -12,7 +12,7 @@ datasets:
 
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/phi-sft-out
+output_dir: ./phi-sft-out
 
 sequence_len: 2048
 sample_packing: true

examples/phi/phi-qlora.yml

@@ -12,7 +12,7 @@ datasets:
 
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/phi-sft-out
+output_dir: ./phi-sft-out
 
 sequence_len: 2048
 sample_packing: true

examples/phi/phi2-ft.yml

@@ -12,7 +12,7 @@ datasets:
 
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/phi-sft-out
+output_dir: ./phi-sft-out
 
 sequence_len: 2048
 sample_packing: true

examples/phi/phi3-ft-fsdp.yml

@@ -1,83 +0,0 @@
-base_model: microsoft/Phi-3-mini-4k-instruct
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./phi-sft-out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-trust_remote_code: true
-
-adapter:
-lora_model_dir:
-lora_r:
-lora_alpha:
-lora_dropout:
-lora_target_linear:
-lora_fan_in_fan_out:
-
-wandb_project: phi3
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 12
-num_epochs: 2
-optimizer: adamw_torch
-adam_beta2: 0.95
-adam_epsilon: 0.00001
-max_grad_norm: 1.0
-lr_scheduler: cosine
-learning_rate: 0.000003
-
-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: 100
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.1
-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: Phi3DecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-resize_token_embeddings_to_32x: true
-special_tokens:
-  pad_token: "<|endoftext|>"

examples/phi/phi3-ft.yml

@@ -1,64 +0,0 @@
-base_model: microsoft/Phi-3-mini-4k-instruct
-trust_remote_code: true
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-chat_template: phi_3
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: garage-bAInd/Open-Platypus
-    type: alpaca:phi
-
-dataset_prepared_path:
-val_set_size: 0.01
-output_dir: ./out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 64
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_torch
-adam_beta2: 0.95
-adam_epsilon: 0.00001
-max_grad_norm: 1.0
-lr_scheduler: cosine
-learning_rate: 5.0e-6
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: True
-early_stopping_patience: 3
-logging_steps: 1
-flash_attention: true
-
-eval_steps: 1000
-save_steps: 5000
-eval_table_size: 2
-eval_batch_size: 2
-eval_sample_packing: false
-eval_max_new_tokens: 32
-eval_causal_lm_metrics: ["perplexity"]
-do_causal_lm_eval: true
-
-warmup_ratio: 0.2
-debug: true
-weight_decay: 0.1
-resize_token_embeddings_to_32x: true

examples/pythia-12b/config.yml

@@ -26,7 +26,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/pythia-12b
+output_dir: ./pythia-12b
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 5

examples/pythia/lora.yml

@@ -20,7 +20,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/lora-alpaca-pythia
+output_dir: ./lora-alpaca-pythia
 gradient_accumulation_steps: 1
 micro_batch_size: 4
 num_epochs: 4

examples/qwen/lora.yml

@@ -13,7 +13,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 
 sequence_len: 2048  # supports up to 8192
 sample_packing: false

examples/qwen/qlora.yml

@@ -13,7 +13,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 
 sequence_len: 2048  # supports up to 8192
 sample_packing: false

examples/qwen/qwen2-moe-lora.yaml

@@ -10,7 +10,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 1024  # supports up to 32k
 sample_packing: false

examples/qwen/qwen2-moe-qlora.yaml

@@ -10,7 +10,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0.05
-output_dir: ./outputs/out
+output_dir: ./out
 
 sequence_len: 1024  # supports up to 32k
 sample_packing: false

examples/qwen2/qlora-fsdp.yaml

@@ -1,75 +0,0 @@
-base_model: Qwen/Qwen2-7B
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/out
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-pad_to_sequence_len: true
-
-adapter: qlora
-lora_model_dir:
-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: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_torch
-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: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-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: Qwen2DecoderLayer
-  fsdp_state_dict_type: FULL_STATE_DICT
-special_tokens:

examples/redpajama/config-3b.yml

@@ -24,7 +24,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/redpajama-alpaca-3b
+output_dir: ./redpajama-alpaca-3b
 batch_size: 4
 micro_batch_size: 1
 num_epochs: 4

examples/replit-3b/config-lora.yml

@@ -23,7 +23,7 @@ wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
-output_dir: ./outputs/lora-replit
+output_dir: ./lora-replit
 batch_size: 8
 micro_batch_size: 1
 num_epochs: 4

examples/stablelm-2/1.6b/fft.yml

@@ -12,7 +12,7 @@ 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

examples/stablelm-2/1.6b/lora.yml

@@ -12,7 +12,7 @@ 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

examples/starcoder2/qlora.yml

@@ -11,7 +11,7 @@ datasets:
 
 dataset_prepared_path:
 val_set_size: 0.2
-output_dir: ./outputs/qlora
+output_dir: ./qlora
 
 adapter: qlora
 lora_model_dir:

examples/tiny-llama/lora-mps.yml

@@ -11,7 +11,7 @@ datasets:
     type: alpaca
 dataset_prepared_path:
 val_set_size: 0
-output_dir: ./outputs/lora-out
+output_dir: ./lora-out
 
 sequence_len: 4096
 sample_packing: true

examples/tiny-llama/lora.yml

@@ -11,7 +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

examples/tiny-llama/pretrain.yml

@@ -14,7 +14,7 @@ pretraining_dataset:
   type: pretrain
 dataset_prepared_path:
 val_set_size: 0.0
-output_dir: ./outputs/model-out
+output_dir: ./model-out
 
 sequence_len: 2048
 sample_packing: true

examples/tiny-llama/qlora.yml

@@ -11,14 +11,13 @@ 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:
 
 sequence_len: 4096
 sample_packing: true
-eval_sample_packing: false
 pad_to_sequence_len: true
 
 lora_r: 32

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -40,7 +40,7 @@ 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

examples/yi-34B-chat/qlora.yml

@@ -33,7 +33,7 @@ eval_sample_packing: false
 eval_batch_size: 1
 
 # LoRA
-output_dir: ./outputs/qlora-out
+output_dir: ./qlora-out
 adapter: qlora
 lora_model_dir:
 lora_r: 32

requirements.txt

@@ -1,22 +1,22 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.11.1
-transformers==4.42.3
-tokenizers==0.19.1
-bitsandbytes==0.43.1
-accelerate==0.32.0
-deepspeed==0.14.4
+peft==0.10.0
+transformers @ git+https://github.com/huggingface/transformers.git@43d17c18360ac9c3d3491389328e2fe55fe8f9ce
+tokenizers==0.15.0
+bitsandbytes==0.43.0
+accelerate==0.28.0
+deepspeed==0.13.1
 pydantic==2.6.3
 addict
 fire
 PyYAML>=6.0
 requests
-datasets==2.19.1
-flash-attn==2.5.8
+datasets==2.15.0
+flash-attn==2.5.5
 sentencepiece
 wandb
 einops
-xformers==0.0.27
+xformers==0.0.22
 optimum==1.16.2
 hf_transfer
 colorama
@@ -28,10 +28,9 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
+fschat @ git+https://github.com/lm-sys/FastChat.git@5095615810cf613dba7f27dd155f571fcff976d8
 gradio==3.50.2
 tensorboard
-python-dotenv==1.0.1
 
 mamba-ssm==1.2.0.post1
 
@@ -40,6 +39,6 @@ s3fs
 gcsfs
 # adlfs
 
-trl==0.9.6
+trl @ git+https://github.com/huggingface/trl.git@75de236c09bd5846f79c24d9bf371481b0b7582c
 zstandard==0.22.0
 fastcore

scripts/cloud-entrypoint-term.sh

@@ -1,82 +0,0 @@
-#!/bin/bash
-
-# Export specific ENV variables to /etc/rp_environment
-echo "Exporting environment variables..."
-printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
-conda init
-# this needs to come after conda init
-echo 'source /etc/rp_environment' >> ~/.bashrc
-
-add_keys_to_authorized() {
-    local key_value=$1
-
-    # Create the ~/.ssh directory and set permissions
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-
-    # Create the authorized_keys file if it doesn't exist
-    touch ~/.ssh/authorized_keys
-
-    # Initialize an empty key variable
-    local key=""
-
-    # Read the key variable word by word
-    for word in $key_value; do
-        # Check if the word looks like the start of a key
-        if [[ $word == ssh-* ]]; then
-            # If there's a key being built, add it to the authorized_keys file
-            if [[ -n $key ]]; then
-                echo $key >> ~/.ssh/authorized_keys
-            fi
-            # Start a new key
-            key=$word
-        else
-            # Append the word to the current key
-            key="$key $word"
-        fi
-    done
-
-    # Add the last key to the authorized_keys file
-    if [[ -n $key ]]; then
-        echo $key >> ~/.ssh/authorized_keys
-    fi
-
-    # Set the correct permissions
-    chmod 600 ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-}
-
-if [[ $PUBLIC_KEY ]]; then
-    # runpod
-    add_keys_to_authorized "$PUBLIC_KEY"
-    # Start the SSH service in the background
-    service ssh start
-elif [[ $SSH_KEY ]]; then
-    # latitude.sh
-    add_keys_to_authorized "$SSH_KEY"
-    # Start the SSH service in the background
-    service ssh start
-else
-    echo "No PUBLIC_KEY or SSH_KEY environment variable provided, not starting openSSH daemon"
-fi
-
-# Check if JUPYTER_PASSWORD is set and not empty
-if [ -n "$JUPYTER_PASSWORD" ]; then
-    # Set JUPYTER_TOKEN to the value of JUPYTER_PASSWORD
-    export JUPYTER_TOKEN="$JUPYTER_PASSWORD"
-fi
-
-if [ "$JUPYTER_DISABLE" != "1" ]; then
-    # Run Jupyter Lab in the background
-    jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* &
-fi
-
-if [ ! -d "/workspace/data/axolotl-artifacts" ]; then
-    mkdir -p /workspace/data/axolotl-artifacts
-fi
-if [ ! -L "/workspace/axolotl/outputs" ]; then
-    ln -sf /workspace/data/axolotl-artifacts /workspace/axolotl/outputs
-fi
-
-# Execute the passed arguments (CMD)
-exec "$@"

scripts/cloud-entrypoint.sh

@@ -5,53 +5,20 @@ echo "Exporting environment variables..."
 printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
 echo 'source /etc/rp_environment' >> ~/.bashrc
 
-add_keys_to_authorized() {
-    local key_value=$1
-
-    # Create the ~/.ssh directory and set permissions
-    mkdir -p ~/.ssh
-    chmod 700 ~/.ssh
-
-    # Create the authorized_keys file if it doesn't exist
-    touch ~/.ssh/authorized_keys
-
-    # Initialize an empty key variable
-    local key=""
-
-    # Read the key variable word by word
-    for word in $key_value; do
-        # Check if the word looks like the start of a key
-        if [[ $word == ssh-* ]]; then
-            # If there's a key being built, add it to the authorized_keys file
-            if [[ -n $key ]]; then
-                echo $key >> ~/.ssh/authorized_keys
-            fi
-            # Start a new key
-            key=$word
-        else
-            # Append the word to the current key
-            key="$key $word"
-        fi
-    done
-
-    # Add the last key to the authorized_keys file
-    if [[ -n $key ]]; then
-        echo $key >> ~/.ssh/authorized_keys
-    fi
-
-    # Set the correct permissions
-    chmod 600 ~/.ssh/authorized_keys
-    chmod 700 -R ~/.ssh
-}
-
 if [[ $PUBLIC_KEY ]]; then
     # runpod
-    add_keys_to_authorized "$PUBLIC_KEY"
+    mkdir -p ~/.ssh
+    chmod 700 ~/.ssh
+    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+    chmod 700 -R ~/.ssh
     # Start the SSH service in the background
     service ssh start
-elif [[ $SSH_KEY ]]; then
+elif [ -n "$SSH_KEY" ]; then
     # latitude.sh
-    add_keys_to_authorized "$SSH_KEY"
+    mkdir -p ~/.ssh
+    chmod 700 ~/.ssh
+    echo $SSH_KEY >> ~/.ssh/authorized_keys
+    chmod 700 -R ~/.ssh
     # Start the SSH service in the background
     service ssh start
 else
@@ -69,12 +36,5 @@ if [ "$JUPYTER_DISABLE" != "1" ]; then
     jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* &
 fi
 
-if [ ! -d "/workspace/data/axolotl-artifacts" ]; then
-    mkdir -p /workspace/data/axolotl-artifacts
-fi
-if [ ! -L "/workspace/axolotl/outputs" ]; then
-    ln -sf /workspace/data/axolotl-artifacts /workspace/axolotl/outputs
-fi
-
 # Execute the passed arguments (CMD)
 exec "$@"

scripts/motd

@@ -11,7 +11,7 @@ Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace a
 ```
 cd /workspace
 rm -rf /workspace/axolotl
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
+git clone https://github.com/OpenAccess-AI-Collective/axolotl.git
 cd axolotl
 pip install --no-deps -e .
 ```

setup.py

@@ -29,13 +29,9 @@ def parse_requirements():
                 _install_requires.append(line)
 
     try:
-        xformers_version = [req for req in _install_requires if "xformers" in req][0]
         if "Darwin" in platform.system():
-            # don't install xformers on MacOS
-            _install_requires.pop(_install_requires.index(xformers_version))
+            _install_requires.pop(_install_requires.index("xformers==0.0.22"))
         else:
-            # detect the version of torch already installed
-            # and set it so dependencies don't clobber the torch version
             torch_version = version("torch")
             _install_requires.append(f"torch=={torch_version}")
 
@@ -49,17 +45,9 @@ def parse_requirements():
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 3):
-                if patch == 0:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                    _install_requires.append("xformers>=0.0.26.post1")
-            elif (major, minor) >= (2, 2):
-                _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.append("xformers>=0.0.25.post1")
-            else:
-                _install_requires.pop(_install_requires.index(xformers_version))
-                _install_requires.append("xformers>=0.0.23.post1")
-
+            if (major, minor) >= (2, 1):
+                _install_requires.pop(_install_requires.index("xformers==0.0.22"))
+                _install_requires.append("xformers>=0.0.23")
     except PackageNotFoundError:
         pass
 
@@ -71,7 +59,7 @@ install_requires, dependency_links = parse_requirements()
 
 setup(
     name="axolotl",
-    version="0.4.1",
+    version="0.4.0",
     description="LLM Trainer",
     long_description="Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.",
     package_dir={"": "src"},
@@ -80,13 +68,13 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.5.8",
+            "flash-attn==2.5.5",
         ],
         "fused-dense-lib": [
-            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.5.8#subdirectory=csrc/fused_dense_lib",
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/flash-attention@v2.3.3#subdirectory=csrc/fused_dense_lib",
         ],
         "deepspeed": [
-            "deepspeed @ git+https://github.com/microsoft/DeepSpeed.git@bc48371c5e1fb8fd70fc79285e66201dbb65679b",
+            "deepspeed==0.13.1",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [