try

app.py

@@ -3,13 +3,8 @@ Chat Interface App
 """
 
 import gradio as gr
-
-# from axolotl.cli import print_axolotl_text_art
-# print_axolotl_text_art()
-
 import sys
-sys.path.append('axolotl/src/axolotl/common/cli.py')
-from cli import test_func
+from .axolotl import test_func
 
 def generate(instruction):
     return "Hello " + instruction + "!" + test_func()

axolotl.py

@@ -0,0 +1,3 @@
+
+def test_func():
+    return "Hello Everybodyyyy!"

axolotl/.bandit

@@ -1,3 +0,0 @@
-[bandit]
-exclude = tests
-skips = B101

axolotl/.editorconfig

@@ -1,14 +0,0 @@
-root = true
-
-[*]
-end_of_line = lf
-insert_final_newline = true
-trim_trailing_whitespace = true
-
-[*.py]
-indent_style = space
-indent_size = 4
-
-[**.yml]
-indent_style = space
-indent_size = 2

axolotl/.flake8

@@ -1,5 +0,0 @@
-[flake8]
-max-line-length = 88
-
-select = C,E,F,W,B,B950
-extend-ignore = E203, E501, W503

axolotl/.gitattributes

@@ -1 +0,0 @@
-data/*.jsonl filter=lfs diff=lfs merge=lfs -text

axolotl/.github/CODE_OF_CONDUCT.md

@@ -1,129 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
-  overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
-  advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
-  address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement on Discord
-at https://discord.gg/QYF8QrtEUm
-
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.

axolotl/.github/CONTRIBUTING.md

@@ -1,76 +0,0 @@
-# Contributing to axolotl
-
-First of all, thank you for your interest in contributing to axolotl! We appreciate the time and effort you're willing to invest in making our project better. This document provides guidelines and information to make the contribution process as smooth as possible.
-
-## Table of Contents
-
-- [Code of Conduct](#code-of-conduct)
-- [Getting Started](#getting-started)
-- [How to Contribute](#how-to-contribute)
-  - [Reporting Bugs](#reporting-bugs)
-  - [Suggesting Enhancements](#suggesting-enhancements)
-  - [Submitting Pull Requests](#submitting-pull-requests)
-- [Style Guidelines](#style-guidelines)
-  - [Code Style](#code-style)
-  - [Commit Messages](#commit-messages)
-- [Additional Resources](#additional-resources)
-
-## Code of Conductcode
-
-All contributors are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md). Please read it before participating in the axolotl community.
-
-## Getting Started
-
-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/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
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
-
-# test
-pytest tests/
-```
-
-## How to Contribute
-
-### Reporting Bugs
-
-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/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
-
-1. Create a new branch for your feature or bugfix. Use a descriptive name like `feature/your-feature-name` or `fix/your-bugfix-name`.
-2. Make your changes, following the [Style Guidelines](#style-guidelines) below.
-3. Test your changes and ensure that they don't introduce new issues or break existing functionality.
-4. Commit your changes, following the [commit message guidelines](#commit-messages).
-5. Push your branch to your fork on GitHub.
-6. Open a new pull request against the `main` branch of the axolotl repository. Include a clear and concise description of your changes, referencing any related issues.
-
-## Style Guidelines
-
-### Code Style
-
-axolotl uses [{codestyle}]({URLofCodestyle}) as its code style guide. Please ensure that your code follows these guidelines.
-
-### Commit Messages
-
-Write clear and concise commit messages that briefly describe the changes made in each commit. Use the imperative mood and start with a capitalized verb, e.g., "Add new feature" or "Fix bug in function".
-
-## Additional Resources
-
-- [GitHub Help](https://help.github.com/)
-- [GitHub Pull Request Documentation](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests)
-- [{codestyle}]({URLofCodestyle})
-
-Thank you once again for your interest in contributing to axolotl. We look forward to collaborating with you and creating an even better project together!

axolotl/.github/FUNDING.yml

@@ -1,13 +0,0 @@
-# These are supported funding model platforms
-
-github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
-patreon: # Replace with a single Patreon username
-open_collective: # Replace with a single Open Collective username
-ko_fi: # Replace with a single Ko-fi username
-tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
-community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
-liberapay: # Replace with a single Liberapay username
-issuehunt: # Replace with a single IssueHunt username
-otechie: # Replace with a single Otechie username
-lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
-custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

axolotl/.github/ISSUE_TEMPLATE/bug-report.yaml

@@ -1,112 +0,0 @@
-name: Bug Report
-description: File a bug report
-labels: ["bug", "needs triage"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        ## Before you start
-        Please **make sure you are on the latest version.**
-        If you encountered the issue after you installed, updated, or reloaded, **please try restarting before reporting the bug**.
-
-  - type: checkboxes
-    id: no-duplicate-issues
-    attributes:
-      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/OpenAccess-AI-Collective/axolotl/labels/bug) didn't find any similar reports."
-          required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected Behavior
-      description: Tell us what **should** happen.
-    validations:
-      required: true
-
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: Current behaviour
-      description: |
-        Tell us what happens instead of the expected behavior.
-        Provide stacktrace and/or screenshots.
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduce
-    attributes:
-      label: Steps to reproduce
-      description: |
-        Which exact steps can a developer take to reproduce the issue?
-        The more detail you provide, the easier it will be to narrow down and fix the bug.
-        Please paste in tasks and/or queries **as text, not screenshots**.
-      placeholder: |
-        Example of the level of detail needed to reproduce any bugs efficiently and reliably.
-        1. Go to the '...' page.
-        2. Click on the '...' button.
-        3. Scroll down to '...'.
-        4. Observe the error.
-    validations:
-      required: true
-
-  - type: textarea
-    id: config
-    attributes:
-      label: Config yaml
-      description: |
-        Please attach the config yaml!
-
-  - type: textarea
-    id: possible-solution
-    attributes:
-      label: Possible solution
-      description: |
-        Not obligatory, but please suggest a fix or reason for the bug, if you have an idea.
-
-
-  - type: checkboxes
-    id: operating-systems
-    attributes:
-      label: Which Operating Systems are you using?
-      description: You may select more than one.
-      options:
-        - label: Linux
-        - label: macOS
-        - label: Windows
-
-  - type: input
-    id: Python-version
-    attributes:
-      label: Python Version
-      description: Which {Programming} version are you using?
-      placeholder: 3.10 / please change accordingly
-    validations:
-      required: true
-
-  - type: input
-    id: axolotl-branch-commit
-    attributes:
-      label: axolotl branch-commit
-      description: On which branch/commit are you?
-      placeholder: main/4d6490b
-    validations:
-      required: true
-
-  - type: checkboxes
-    id: acknowledgements
-    attributes:
-      label: 'Acknowledgements'
-      description: 'Please confirm the following:'
-      options:
-        - label: 'My issue title is concise, descriptive, and in title casing.'
-          required: true
-        - label: 'I have searched the existing issues to make sure this bug has not been reported yet.'
-          required: true
-        - label: 'I am using the latest version of axolotl.'
-          required: true
-        - label: 'I have provided enough information for the maintainers to reproduce and diagnose the issue.'
-          required: true

axolotl/.github/ISSUE_TEMPLATE/config.yml

@@ -1,7 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: Ask a question
-    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

axolotl/.github/ISSUE_TEMPLATE/docs.yml

@@ -1,46 +0,0 @@
-name: Documentation Improvement / Clarity
-description: Make a suggestion to improve the project documentation.
-labels: ['needs triage', 'docs']
-body:
-  - type: markdown
-    attributes:
-      value: '## :book: Documentation :book:'
-  - type: markdown
-    attributes:
-      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/OpenAccess-AI-Collective/axolotl/issues).
-  - type: textarea
-    attributes:
-      label: What piece of documentation is affected?
-      description: Please link to the article you'd like to see updated.
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: What part(s) of the article would you like to see updated?
-      description: |
-        - Give as much detail as you can to help us understand the change you want to see.
-        - Why should the docs be changed? What use cases does it support?
-        - What is the expected outcome?
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: Additional Information
-      description: Add any other context or screenshots about the feature request here.
-    validations:
-      required: false
-  - type: checkboxes
-    id: acknowledgements
-    attributes:
-      label: 'Acknowledgements'
-      description: 'Please confirm the following:'
-      options:
-        - label: 'My issue title is concise, descriptive, and in title casing.'
-          required: true
-        - label: 'I have searched the existing issues to make sure this feature has not been requested yet.'
-          required: true
-        - label: 'I have provided enough information for the maintainers to understand and evaluate this request.'
-          required: true

axolotl/.github/ISSUE_TEMPLATE/feature-request.yaml

@@ -1,63 +0,0 @@
-name: Feature Request / Enhancement
-description: Suggest a new feature or feature enhancement for the project
-labels: ["enhancement", "needs triage"]
-body:
-  - type: checkboxes
-    id: no-duplicate-issues
-    attributes:
-      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/OpenAccess-AI-Collective/axolotl/discussions/categories/ideas) didn't find any similar feature requests."
-          required: true
-        - 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
-    id: feature-description
-    validations:
-      required: true
-    attributes:
-      label: "🔖 Feature description"
-      description: "A clear and concise description of what the feature request is."
-      placeholder: "You should add ..."
-
-  - type: textarea
-    id: solution
-    validations:
-      required: true
-    attributes:
-      label: "✔️ Solution"
-      description: "A clear and concise description of what you want to happen, and why."
-      placeholder: "In my use-case, ..."
-
-  - type: textarea
-    id: alternatives
-    validations:
-      required: false
-    attributes:
-      label: "❓ Alternatives"
-      description: "A clear and concise description of any alternative solutions or features you've considered."
-      placeholder: "I have considered ..."
-
-  - type: textarea
-    id: additional-context
-    validations:
-      required: false
-    attributes:
-      label: "📝 Additional Context"
-      description: "Add any other context or screenshots about the feature request here."
-      placeholder: "..."
-
-  - type: checkboxes
-    id: acknowledgements
-    attributes:
-      label: 'Acknowledgements'
-      description: 'Please confirm the following:'
-      options:
-        - label: 'My issue title is concise, descriptive, and in title casing.'
-          required: true
-        - label: 'I have searched the existing issues to make sure this feature has not been requested yet.'
-          required: true
-        - label: 'I have provided enough information for the maintainers to understand and evaluate this request.'
-          required: true

axolotl/.github/PULL_REQUEST_TEMPLATE/pull_request_template_simple.md

@@ -1,22 +0,0 @@
-<!--- Provide a general summary of your changes in the Title above -->
-
-# Description
-
-<!--- Describe your changes in detail -->
-
-## Motivation and Context
-
-<!--- Why is this change required? What problem does it solve? -->
-<!--- If it fixes an open issue, please link to the issue here. -->
-
-## How has this been tested?
-
-<!--- Please describe in detail how you tested your changes. -->
-<!--- Include details of your testing environment, tests ran to see how -->
-<!--- your change affects other areas of the code, etc. -->
-
-## Screenshots (if appropriate)
-
-## Types of changes
-
-<!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: -->

axolotl/.github/SECURITY.md

@@ -1,9 +0,0 @@
-# Security Policy
-
-## Supported Versions
-
-Due to the nature of the fast development that is happening in this project, only the latest released version can be supported.
-
-## Reporting a Vulnerability
-
-If you find a vulnerability, please contact us on  [Discord](https://discord.gg/xcu3ECkH9a) rather than creating a GitHub issue to allow us some time to fix it before it is a known vulnerability to others.

axolotl/.github/SUPPORT.md

@@ -1,10 +0,0 @@
-# Support
-
-If you need help with this project or have questions, please:
-
-1. Check the documentation.
-2. Search the existing issues and pull requests.
-3. Create a new issue if your question is not answered or your problem is not solved.
-4. Have a look in the [Discord server](https://discord.gg/HhrNrHJPRb)
-
-Please note that this project is maintained by volunteers who have limited availability. We'll do our best to address your questions and concerns in a timely manner.

axolotl/.github/release-drafter.yml

@@ -1,31 +0,0 @@
-name-template: 'v$RESOLVED_VERSION'
-tag-template: 'v$RESOLVED_VERSION'
-categories:
-  - title: '🚀 Features'
-    labels:
-      - 'feature'
-      - 'enhancement'
-  - title: '🐛 Bug Fixes'
-    labels:
-      - 'fix'
-      - 'bugfix'
-      - 'bug'
-  - title: '🧰 Maintenance'
-    label: 'chore'
-change-template: '- $TITLE @$AUTHOR (#$NUMBER)'
-change-title-escapes: '\<*_&' # You can add # and @ to disable mentions, and add ` to disable code blocks.
-version-resolver:
-  major:
-    labels:
-      - 'major'
-  minor:
-    labels:
-      - 'minor'
-  patch:
-    labels:
-      - 'patch'
-  default: patch
-template: |
-  ## What’s Changed
-
-  $CHANGES

axolotl/.github/workflows/base.yml

@@ -1,66 +0,0 @@
-name: ci-cd-base
-
-on:
-  push:
-    branches:
-      - "main-base"
-      - "dev-base"
-
-jobs:
-  build-base:
-    if: github.repository_owner == 'OpenAccess-AI-Collective'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: self-hosted
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: "118"
-            cuda_version: 11.8.0
-            python_version: "3.9"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-          - cuda: "118"
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.0.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-          - cuda: "118"
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v3
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v3
-        with:
-          images: winglian/axolotl-base
-      - name: Login to Docker Hub
-        uses: docker/login-action@v2
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-      - name: Build
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          file: ./docker/Dockerfile-base
-          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 }}
-          labels: ${{ steps.metadata.outputs.labels }}
-          build-args: |
-            CUDA_VERSION=${{ matrix.cuda_version }}
-            CUDA=${{ matrix.cuda }}
-            PYTHON_VERSION=${{ matrix.python_version }}
-            PYTORCH_VERSION=${{ matrix.pytorch }}
-            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}

axolotl/.github/workflows/main.yml

@@ -1,136 +0,0 @@
-name: ci-cd
-
-on:
-  push:
-    branches:
-      - "main"
-
-jobs:
-  build-axolotl:
-    if: github.repository_owner == 'OpenAccess-AI-Collective'
-    # this job needs to be run on self-hosted GPU runners...
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.9"
-            pytorch: 2.0.1
-            axolotl_extras:
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.0.1
-            axolotl_extras:
-            is_latest: true
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            axolotl_extras:
-    runs-on: [self-hosted, gpu, docker]
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
-        with:
-          images: winglian/axolotl
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-      # guidance for testing before pushing: https://docs.docker.com/build/ci/github-actions/test-before-push/
-      - name: Build and export to Docker
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          load: true
-          build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
-            CUDA=${{ matrix.cuda }}
-            PYTORCH_VERSION=${{ matrix.pytorch }}
-          file: ./docker/Dockerfile
-          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 }}
-      - name: Unit Tests
-        run: |
-          docker run --rm ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }} pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-      - name: Push to Docker Hub
-        if: github.event_name != 'pull_request'
-        run: |
-          docker push ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
-          latest_tag=${{ (matrix.is_latest) && format('{0}-latest', steps.metadata.outputs.tags) || '' }}
-          if [ -n "$latest_tag" ]; then
-            docker push "$latest_tag"
-          fi
-
-  build-axolotl-runpod:
-    needs: build-axolotl
-    if: github.repository_owner == 'OpenAccess-AI-Collective'
-    # this job needs to be run on self-hosted GPU runners...
-    strategy:
-      matrix:
-        include:
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.9"
-            pytorch: 2.0.1
-            axolotl_extras:
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.0.1
-            axolotl_extras:
-            is_latest: true
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.10"
-            pytorch: 2.1.1
-            axolotl_extras:
-    runs-on: [self-hosted, gpu, docker]
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Docker metadata
-        id: metadata
-        uses: docker/metadata-action@v5
-        with:
-          images: winglian/axolotl-runpod
-      - 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-runpod
-          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 }}

axolotl/.github/workflows/pypi.yml

@@ -1,45 +0,0 @@
-name: publish pypi
-
-on:
-  push:
-    tags:
-      - '*'
-
-jobs:
-  pypi-publish:
-    name: Upload release to PyPI
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/axolotl
-    permissions:
-      id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-
-      - name: Install dependencies
-        run: |
-          pip3 install wheel
-          pip3 install -e .
-          pip3 install -r requirements-tests.txt
-
-      - name: Extract tag name
-        id: tag
-        run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
-
-      - name: Update version in setup.py
-        run: >-
-          sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
-
-      - name: Build a binary wheel
-        run: >-
-          python setup.py sdist bdist_wheel
-
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1

axolotl/.github/workflows/tests.yml

@@ -1,81 +0,0 @@
-name: Tests
-on:
-  # check on push/merge to main, PRs, and manual triggers
-  push:
-    branches:
-      - "main"
-    paths:
-      - '**.py'
-      - 'requirements.txt'
-  pull_request:
-      paths:
-       - '**.py'
-       - 'requirements.txt'
-  workflow_dispatch:
-
-jobs:
-  pre-commit:
-    name: pre-commit
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
-        with:
-          python-version: "3.9"
-          cache: 'pip' # caching pip dependencies
-      - uses: pre-commit/action@v3.0.0
-
-  pytest:
-    name: PyTest
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.9", "3.10"]
-    timeout-minutes: 10
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: Install dependencies
-        run: |
-          pip3 install -U -e .
-          pip3 install -r requirements-tests.txt
-
-      - name: Run tests
-        run: |
-          pytest --ignore=tests/e2e/ tests/
-
-  e2e-test:
-    name: E2E Tests
-    runs-on: [self-hosted, gpu]
-    timeout-minutes: 20
-    needs: [pre-commit, pytest]
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-#          cache: 'pip' # caching pip dependencies
-
-      - name: Install dependencies
-        run: |
-          pip3 install --extra-index-url https://download.pytorch.org/whl/cu118 -U torch==2.0.1
-          pip3 uninstall -y transformers accelerate
-          pip3 install -U -e .[flash-attn,mamba-ssm]
-          pip3 install -r requirements-tests.txt
-
-      - name: Run e2e tests
-        run: |
-          pytest tests/e2e/

axolotl/.gitignore

@@ -1,167 +0,0 @@
-**/axolotl.egg-info
-configs
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
-
-# WandB
-# wandb creates a folder to store logs for training runs
-wandb

axolotl/.isort.cfg

@@ -1,3 +0,0 @@
-[settings]
-profile=black
-known_third_party=wandb

axolotl/.mypy.ini

@@ -1,48 +0,0 @@
-[mypy]
-
-exclude = venv
-
-[mypy-alpaca_lora_4bit.*]
-ignore_missing_imports = True
-
-[mypy-axolotl.monkeypatch.*]
-ignore_errors = True
-
-[mypy-axolotl.models.mixtral.*]
-ignore_errors = True
-
-[mypy-axolotl.models.phi.*]
-ignore_errors = True
-
-[mypy-flash_attn.*]
-ignore_missing_imports = True
-
-[mypy-huggingface_hub]
-ignore_missing_imports = True
-
-[mypy-transformers.*]
-ignore_missing_imports = True
-
-[mypy-peft]
-ignore_missing_imports = True
-
-[mypy-wandb]
-ignore_missing_imports = True
-
-[mypy-bitsandbytes]
-ignore_missing_imports = True
-
-[mypy-datasets]
-ignore_missing_imports = True
-
-[mypy-fire]
-ignore_missing_imports = True
-
-[mypy-setuptools]
-ignore_missing_imports = True
-
-[mypy-addict]
-ignore_missing_imports = True
-
-[mypy-xformers.*]
-ignore_missing_imports = True

axolotl/.pre-commit-config.yaml

@@ -1,42 +0,0 @@
-default_language_version:
-    python: python3
-
-repos:
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-    -   id: check-yaml
-    -   id: end-of-file-fixer
-    -   id: trailing-whitespace
--   repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-    -   id: black
--   repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
--   repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
-    hooks:
-    - id: flake8
--   repo: https://github.com/PyCQA/pylint
-    rev: v2.17.4
-    hooks:
-    - id: pylint
--   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.3.0
-    hooks:
-    - id: mypy
-      additional_dependencies:
-        [
-            'types-PyYAML',
-        ]
--   repo: https://github.com/PyCQA/bandit
-    rev: 1.7.5
-    hooks:
-    -   id: bandit
-        args: [
-            '--ini',
-            '.bandit',
-        ]

axolotl/.pylintrc

@@ -1,14 +0,0 @@
-[MASTER]
-init-hook="from pylint.config import find_pylintrc; import os, sys; sys.path.append(os.path.dirname(find_pylintrc()))"
-
-[TYPECHECK]
-
-# List of members which are set dynamically and missed by Pylint inference
-# system, and so shouldn't trigger E1101 when accessed.
-generated-members=numpy.*, torch.*
-
-
-[pylint.messages_control]
-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,

axolotl/FAQS.md

@@ -1,7 +0,0 @@
-# FAQs
-
-- Can you train StableLM with this? Yes, but only with a single GPU atm. Multi GPU support is coming soon! Just waiting on this [PR](https://github.com/huggingface/transformers/pull/22874)
-- Will this work with Deepspeed? That's still a WIP, but setting `export ACCELERATE_USE_DEEPSPEED=true` should work in some cases
-- `Error invalid argument at line 359 in file /workspace/bitsandbytes/csrc/pythonInterface.c`
-`/arrow/cpp/src/arrow/filesystem/s3fs.cc:2598:  arrow::fs::FinalizeS3 was not called even though S3 was initialized.`
-This could lead to a segmentation fault at exit. Try reinstalling bitsandbytes and transformers from source.

axolotl/LICENSE

@@ -1,202 +0,0 @@
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-
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-
-      "Legal Entity" shall mean the union of the acting entity and all
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-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
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-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
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axolotl/README.md

@@ -1,1132 +0,0 @@
-# Axolotl
-
-Axolotl is a tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures.
-
-Features:
-- Train various Huggingface models such as llama, pythia, falcon, mpt
-- Supports fullfinetune, lora, qlora, relora, and gptq
-- Customize configurations using a simple yaml file or CLI overwrite
-- Load different dataset formats, use custom formats, or bring your own tokenized datasets
-- Integrated with xformer, flash attention, rope scaling, and multipacking
-- Works with single GPU or multiple GPUs via FSDP or Deepspeed
-- Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb
-- And more!
-
-
-<table>
-<tr>
-<td>
-
-## Table of Contents
-- [Introduction](#axolotl)
-- [Supported Features](#axolotl-supports)
-- [Quickstart](#quickstart-)
-- [Installation](#installation)
-  - [Docker](#docker)
-  - [Conda/Pip venv](#condapip-venv)
-  - [Runpod](#runpod)
-  - [LambdaLabs](#lambdalabs)
-  - [Windows](#windows)
-  - [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
-- [Dataset](#dataset)
-  - [How to Add Custom Prompts](#how-to-add-custom-prompts)
-  - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
-- [Config](#config)
-  - [Train](#train)
-  - [Inference](#inference)
-  - [Merge LORA to Base](#merge-lora-to-base)
-  - [Special Tokens](#special-tokens)
-- [Common Errors](#common-errors-)
-  - [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
-- [Need Help?](#need-help-)
-- [Badge](#badge-)
-- [Community Showcase](#community-showcase)
-- [Contributing](#contributing-)
-
-</td>
-<td>
-
-<div align="center">
-  <img src="image/axolotl.png" alt="axolotl" width="160">
-  <div>
-    <p>
-      <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
-    </p>
-    <p>
-      Go ahead and Axolotl questions!!
-    </p>
-    <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>
-
-</td>
-</tr>
-</table>
-
-## Axolotl supports
-
-|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
-|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
-| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
-| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
-| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
-| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
-| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-
-
-## Quickstart ⚡
-
-Get started with Axolotl in just a few steps! This quickstart guide will walk you through setting up and running a basic fine-tuning task.
-
-**Requirements**: Python >=3.9 and Pytorch >=2.0.
-
-`pip3 install "axolotl[flash-attn,deepspeed] @ git+https://github.com/OpenAccess-AI-Collective/axolotl"`
-
-### For developers
-```bash
-git clone https://github.com/OpenAccess-AI-Collective/axolotl
-cd axolotl
-
-pip3 install packaging
-pip3 install -e '.[flash-attn,deepspeed]'
-```
-
-### Usage
-```bash
-# finetune lora
-accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
-
-# inference
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./lora-out"
-
-# gradio
-accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
-    --lora_model_dir="./lora-out" --gradio
-```
-
-## Installation
-
-### Environment
-
-#### Docker
-  ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
-  ```
-
-  Or run on the current files for development:
-
-  ```sh
-  docker compose up -d
-  ```
-
-  <details>
-
-  <summary>Docker advanced</summary>
-
-  A more powerful Docker command to run would be this:
-
-  ```bash
-  docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=volume,src=axolotl,target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface winglian/axolotl:main-py3.10-cu118-2.0.1
-  ```
-
-  It additionally:
-  * Prevents memory issues when running e.g. deepspeed (e.g. you could hit SIGBUS/signal 7 error) through `--ipc` and `--ulimit` args.
-  * Persists the downloaded HF data (models etc.) and your modifications to axolotl code through `--mount`/`-v` args.
-  * The `--name` argument simply makes it easier to refer to the container in vscode (`Dev Containers: Attach to Running Container...`) or in your terminal.
-  * The `--privileged` flag gives all capabilities to the container.
-  * The `--shm-size 10g` argument increases the shared memory size. Use this if you see `exitcode: -7` errors using deepspeed.
-
-  [More information on nvidia website](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem)
-
-  </details>
-
-#### Conda/Pip venv
-  1. Install python >=**3.9**
-
-  2. Install pytorch stable https://pytorch.org/get-started/locally/
-
-  3. Install Axolotl along with python dependencies
-        ```bash
-        pip3 install packaging
-        pip3 install -e '.[flash-attn,deepspeed]'
-        ```
-  4. (Optional) Login to Huggingface to use gated models/datasets.
-        ```bash
-        huggingface-cli login
-        ```
-        Get the token at huggingface.co/settings/tokens
-
-#### Runpod
-
-Use `winglian/axolotl-runpod:main-latest` or use this [direct link](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-
-#### LambdaLabs
-  <details>
-
-  <summary>Click to Expand</summary>
-
-  1. Install python
-  ```bash
-  sudo apt update
-  sudo apt install -y python3.9
-
-  sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
-  sudo update-alternatives --config python # pick 3.9 if given option
-  python -V # should be 3.9
-
-  ```
-
-  2. Install pip
-  ```bash
-  wget https://bootstrap.pypa.io/get-pip.py
-  python get-pip.py
-  ```
-
-  3. Install torch
-  ```bash
-  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
-  ```
-
-  4. Axolotl
-  ```bash
-  git clone https://github.com/OpenAccess-AI-Collective/axolotl
-  cd axolotl
-
-  pip3 install packaging
-  pip3 install -e '.[flash-attn,deepspeed]'
-  pip3 install protobuf==3.20.3
-  pip3 install -U --ignore-installed requests Pillow psutil scipy
-  ```
-
-  5. Set path
-  ```bash
-  export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
-  ```
-  </details>
-
-#### Windows
-Please use WSL or Docker!
-
-
-#### Launching on public clouds via SkyPilot
-To launch on GPU instances (both on-demand and spot instances) on 7+ clouds (GCP, AWS, Azure, OCI, and more), you can use [SkyPilot](https://skypilot.readthedocs.io/en/latest/index.html):
-```bash
-pip install "skypilot-nightly[gcp,aws,azure,oci,lambda,kubernetes,ibm,scp]"  # choose your clouds
-sky check
-```
-Get the [example YAMLs](https://github.com/skypilot-org/skypilot/tree/master/llm/axolotl) of using Axolotl to finetune `mistralai/Mistral-7B-v0.1`:
-```
-git clone https://github.com/skypilot-org/skypilot.git
-cd skypilot/llm/axolotl
-```
-Use one command to launch:
-```bash
-# On-demand
-HF_TOKEN=xx sky launch axolotl.yaml --env HF_TOKEN
-
-# Managed spot (auto-recovery on preemption)
-HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKEN --env BUCKET
-```
-
-
-### Dataset
-
-Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
-Have dataset(s) in one of the following format (JSONL recommended):
-
-- `alpaca`: instruction; input(optional)
-  ```json
-  {"instruction": "...", "input": "...", "output": "..."}
-  ```
-- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: `system` to override default system prompt)
-  ```json
-  {"conversations": [{"from": "...", "value": "..."}]}
-  ```
-- `llama-2`: the json is the same format as `sharegpt` above, with the following config (see the [config section](#config) for more details)
-    ```yml
-    datasets:
-      - path: <your-path>
-        type: sharegpt
-        conversation: llama-2
-    ```
-- `completion`: raw corpus
-  ```json
-  {"text": "..."}
-  ```
-
-<details>
-
-<summary>See other formats</summary>
-
-- `jeopardy`: question and answer
-  ```json
-  {"question": "...", "category": "...", "answer": "..."}
-  ```
-- `oasst`: instruction
-  ```json
-  {"INSTRUCTION": "...", "RESPONSE": "..."}
-  ```
-- `gpteacher`: instruction; input(optional)
-  ```json
-  {"instruction": "...", "input": "...", "response": "..."}
-  ```
-- `reflection`: instruction with reflect; input(optional)
-  ```json
-  {"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
-  ```
-- `explainchoice`: question, choices, (solution OR explanation)
-  ```json
-  {"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
-  ```
-- `concisechoice`: question, choices, (solution OR explanation)
-  ```json
-  {"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
-  ```
-- `summarizetldr`: article and summary
-  ```json
-  {"article": "...", "summary": "..."}
-  ```
-- `alpaca_chat`: basic instruct for alpaca chat
-  ```json
-  {"instruction": "...", "input": "...", "response": "..."}
-  ```
-- `alpaca_chat.load_qa`: question and answer for alpaca chat
-  ```json
-  {"question": "...", "answer": "..."}
-  ```
-- `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers
-  ```json
-  {"instruction": "...", "input": "...", "response": "..."}
-  ```
-- `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai
-  ```json
-  {"message_1": "...", "message_2": "..."}
-  ```
-- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
-  ```json
-  {"system_prompt": "...", "question": "...", "response": "..."}
-  ```
-- `context_qa`: in context question answering from an article
-  ```json
-  {"article": "...", "question": "...", "answer": "..."}
-  ```
-- `context_qa.load_v2`: in context question answering (alternate)
-  ```json
-  {"context": "...", "question": "...", "answer": "..."}
-  ```
-- `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
-  ```json
-  {"article": "...", "unanswerable_question": "..."}
-  ```
-- `creative_acr.load_answer`: instruction and revision
-  ```json
-  {"instruction": "...", "revision": "..."}
-  ```
-- `creative_acr.load_critique`: critique
-  ```json
-  {"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
-  ```
-- `creative_acr.load_revise`: critique and revise
-  ```json
-  {"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
-  ```
-- `pygmalion`: pygmalion
-  ```json
-  {"conversations": [{"role": "...", "value": "..."}]}
-  ```
-- `metharme`: instruction, adds additional eos tokens
-  ```json
-  {"prompt": "...", "generation": "..."}
-  ```
-- `sharegpt.load_role`: conversations where `role` is used instead of `from`
-  ```json
-  {"conversations": [{"role": "...", "value": "..."}]}
-  ```
-- `sharegpt.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
-  ```json
-  {"conversations": [{"from": "...", "value": "..."}]}
-  ```
-- `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
-  ```json
-  {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
-  ```
-
-</details>
-
-#### How to add custom prompts
-
-For a dataset that is preprocessed for instruction purposes:
-
-```json
-{"instruction": "...", "output": "..."}
-```
-
-You can use this example in your YAML config:
-
-```yaml
-datasets:
-  - path: repo
-    type:
-      system_prompt: ""
-      field_system: system
-      format: "[INST] {instruction} [/INST]"
-      no_input_format: "[INST] {instruction} [/INST]"
-```
-
-#### How to use your custom pretokenized dataset
-
-- Do not pass a `type:`
-- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
-
-
-### Config
-
-See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
-
-- model
-  ```yaml
-  base_model: ./llama-7b-hf # local or huggingface repo
-  ```
-  Note: The code will load the right architecture.
-
-- dataset
-  ```yaml
-  sequence_len: 2048 # max token length for prompt
-
-  # huggingface repo
-  datasets:
-    - path: vicgalle/alpaca-gpt4
-      type: alpaca # format from earlier
-
-  # huggingface repo with specific configuration/subset
-  datasets:
-    - path: EleutherAI/pile
-      name: enron_emails
-      type: completion # format from earlier
-      field: text # Optional[str] default: text, field to use for completion data
-
-  # huggingface repo with multiple named configurations/subsets
-  datasets:
-    - path: bigcode/commitpackft
-      name:
-        - ruby
-        - python
-        - typescript
-      type: ... # unimplemented custom format
-
-  # fastchat conversation
-  # See 'conversation' options: https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-  datasets:
-    - path: ...
-      type: sharegpt
-      conversation: chatml
-
-  # local
-  datasets:
-    - path: data.jsonl # or json
-      ds_type: json # see other options below
-      type: alpaca
-
-  # dataset with splits, but no train split
-  dataset:
-    - path: knowrohit07/know_sql
-      type: context_qa.load_v2
-      train_on_split: validation
-
-  # loading from s3 or gcs
-  # s3 creds will be loaded from the system default and gcs only supports public access
-  dataset:
-    - path: s3://path_to_ds # Accepts folder with arrow/parquet or file path like above. Supports s3, gcs.
-      ...
-  ```
-
-- loading
-  ```yaml
-  load_in_4bit: true
-  load_in_8bit: true
-  bf16: true # require >=ampere
-  fp16: true
-  tf32: true # require >=ampere
-  bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
-  float16: true # use instead of fp16 when you don't want AMP
-  ```
-  Note: Repo does not do 4-bit quantization.
-
-- lora
-  ```yaml
-  adapter: lora # qlora or leave blank for full finetune
-  lora_r: 8
-  lora_alpha: 16
-  lora_dropout: 0.05
-  lora_target_modules:
-    - q_proj
-    - v_proj
-  ```
-
-<details>
-
-<summary>All yaml options (click me)</summary>
-
-```yaml
-# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
-# This can also be a relative path to a model on disk
-base_model: ./llama-7b-hf
-# You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
-base_model_ignore_patterns:
-# If the base_model repo on hf hub doesn't include configuration .json files,
-# You can set that here, or leave this empty to default to base_model
-base_model_config: ./llama-7b-hf
-# You can specify to choose a specific model revision from huggingface hub
-model_revision:
-# Optional tokenizer configuration override in case you want to use a different tokenizer
-# than the one defined in the base model
-tokenizer_config:
-# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
-model_type: AutoModelForCausalLM
-# Corresponding tokenizer for the model AutoTokenizer is a good choice
-tokenizer_type: AutoTokenizer
-# Trust remote code for untrusted source
-trust_remote_code:
-# use_fast option for tokenizer loading from_pretrained, default to True
-tokenizer_use_fast:
-# Whether to use the legacy tokenizer setting, defaults to True
-tokenizer_legacy:
-# Resize the model embeddings when new tokens are added to multiples of 32
-# This is reported to improve training speed on some models
-resize_token_embeddings_to_32x:
-
-# Used to identify which the model is based on
-is_falcon_derived_model:
-is_llama_derived_model:
-# Please note that if you set this to true, `padding_side` will be set to "left" by default
-is_mistral_derived_model:
-is_qwen_derived_model:
-
-# optional overrides to the base model configuration
-model_config:
-  # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-  rope_scaling:
-    type: # linear | dynamic
-    factor: # float
-
-
-# Whether you are training a 4-bit GPTQ quantized model
-gptq: true
-gptq_groupsize: 128 # group size
-gptq_model_v1: false # v1 or v2
-
-# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
-load_in_8bit: true
-# Use bitsandbytes 4 bit
-load_in_4bit:
-
-# Use CUDA bf16
-bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
-# Use CUDA fp16
-fp16: true
-# Use CUDA tf32
-tf32: true # require >=ampere
-
-# No AMP (automatic mixed precision)
-bfloat16: true # require >=ampere
-float16: true
-
-# A list of one or more datasets to finetune the model with
-datasets:
-  # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
-  - path: vicgalle/alpaca-gpt4
-  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
-    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
-    ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
-    data_files: # Optional[str] path to source data files
-    shards: # Optional[int] number of shards to split data into
-    name: # Optional[str] name of dataset configuration to load
-    train_on_split: train # Optional[str] name of dataset split to load from
-
-    # Optional[str] fastchat conversation type, only used with type: sharegpt
-    conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    field_human: # Optional[str]. Human key to use for conversation.
-    field_model: # Optional[str]. Assistant key to use for conversation.
-
-  # Custom user prompt
-  - path: repo
-    type:
-      # The below are defaults. only set what's needed.
-      system_prompt: ""
-      system_format: "{system}"
-      field_system: system
-      field_instruction: instruction
-      field_input: input
-      field_output: output
-
-      # Customizable to be single line or multi-line
-      # 'format' can include {input}
-      format: |-
-        User: {instruction} {input}
-        Assistant:
-      # 'no_input_format' cannot include {input}
-      no_input_format: "{instruction} "
-
-      # For `completion` datsets only, uses the provided field instead of `text` column
-      field:
-
-# Axolotl attempts to save the dataset as an arrow after packing the data together so
-# subsequent training attempts load faster, relative path
-dataset_prepared_path: data/last_run_prepared
-# Push prepared dataset to hub
-push_dataset_to_hub: # repo path
-# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
-# if not set.
-dataset_processes: # defaults to os.cpu_count() if not set
-# push checkpoints to hub
-hub_model_id: # repo path to push finetuned model
-# how to push checkpoints to hub
-# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
-hub_strategy:
-# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
-# Required to be true when used in combination with `push_dataset_to_hub`
-hf_use_auth_token: # boolean
-# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
-val_set_size: 0.04
-# Num shards for whole dataset
-dataset_shard_num:
-# Index of shard to use for whole dataset
-dataset_shard_idx:
-
-# The maximum length of an input to train with, this should typically be less than 2048
-# as most models have a token/context limit of 2048
-sequence_len: 2048
-# Pad inputs so each step uses constant sized buffers
-# This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
-pad_to_sequence_len:
-# Max sequence length to concatenate training samples together up to
-# Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-# FutureWarning: This will soon be DEPRECATED
-max_packed_sequence_len: 1024
-# Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
-sample_packing:
-# Set to 'false' if getting errors during eval with sample_packing on.
-eval_sample_packing:
-# You can set these packing optimizations AFTER starting a training at least once.
-# The trainer will provide recommended values for these values.
-sample_packing_eff_est:
-total_num_tokens:
-
-# Passed through to transformers when loading the model when launched without accelerate
-# Use `sequential` when training w/ model parallelism to limit memory
-device_map:
-# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
-max_memory:
-
-# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
-adapter: lora
-# If you already have a lora model trained that you want to load, put that here.
-# This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
-lora_model_dir:
-
-# LoRA hyperparameters
-# For more details about the following options, see:
-# https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-  - q_proj
-  - v_proj
-#  - k_proj
-#  - o_proj
-#  - gate_proj
-#  - down_proj
-#  - up_proj
-lora_target_linear: # If true, will target all linear layers
-
-# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
-# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
-# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
-# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
-lora_modules_to_save:
-#  - embed_tokens
-#  - lm_head
-
-# Once you complete training, the model will be saved to the following directory.
-# If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
-# Make sure `lora_model_dir` points to this directory if you want to use the trained model.
-lora_out_dir:
-lora_fan_in_fan_out: false
-
-# ReLoRA configuration
-# Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
-relora_steps: # Number of steps per ReLoRA restart
-relora_warmup_steps: # Number of per-restart warmup steps
-relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
-
-# wandb configuration if you're using it
-# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
-wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-wandb_project: # Your wandb project name
-wandb_entity: # A wandb Team name if using a Team
-wandb_watch:
-wandb_name: # Set the name of your wandb run
-wandb_run_id: # Set the ID of your wandb run
-wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
-
-# Where to save the full-finetuned model to
-output_dir: ./completed-model
-
-# Whether to use torch.compile and which backend to use
-torch_compile:  # bool
-torch_compile_backend:  # Optional[str]
-
-# Training hyperparameters
-
-# If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
-gradient_accumulation_steps: 1
-# The number of samples to include in each batch. This is the number of samples sent to each GPU.
-micro_batch_size: 2
-eval_batch_size:
-num_epochs: 4
-warmup_steps: 100  # cannot use with warmup_ratio
-warmup_ratio: 0.05  # cannot use with warmup_steps
-learning_rate: 0.00003
-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_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
-# Maximum number of iterations to train for. It precedes num_epochs which means that
-# if both are set, num_epochs will not be guaranteed.
-# e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
-max_steps:
-
-eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
-eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-
-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)
-
-# Save model as safetensors (require safetensors package)
-save_safetensors:
-
-# Whether to mask out or include the human's prompt from the training labels
-train_on_inputs: false
-# Group similarly sized data to minimize padding.
-# May be slower to start, as it must download and sort the entire dataset.
-# Note that training loss may have an oscillating pattern with this enabled.
-group_by_length: false
-
-# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
-gradient_checkpointing: false
-
-# Stop training after this many evaluation losses have increased in a row
-# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
-early_stopping_patience: 3
-
-# Specify a scheduler and kwargs to use with the optimizer
-lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
-lr_scheduler_kwargs:
-
-# For one_cycle optim
-lr_div_factor: # Learning rate div factor
-
-# For log_sweep optim
-log_sweep_min_lr:
-log_sweep_max_lr:
-
-# Specify optimizer
-# Valid values are driven by the Transformers OptimizerNames class, see:
-# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
-#
-# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
-# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
-# in the examples/ for your model and fine-tuning use case.
-#
-# Valid values for 'optimizer' include:
-# - adamw_hf
-# - adamw_torch
-# - adamw_torch_fused
-# - adamw_torch_xla
-# - adamw_apex_fused
-# - adafactor
-# - adamw_anyprecision
-# - sgd
-# - adagrad
-# - adamw_bnb_8bit
-# - lion_8bit
-# - lion_32bit
-# - paged_adamw_32bit
-# - paged_adamw_8bit
-# - paged_lion_32bit
-# - paged_lion_8bit
-optimizer:
-# Specify weight decay
-weight_decay:
-# adamw hyperparams
-adam_beta1:
-adam_beta2:
-adam_epsilon:
-# Gradient clipping max norm
-max_grad_norm:
-
-# Augmentation techniques
-# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
-# currently only supported on Llama and Mistral
-neftune_noise_alpha:
-
-# Whether to bettertransformers
-flash_optimum:
-# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
-xformers_attention:
-# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
-flash_attention:
-flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
-flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
-flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
-flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
-# Whether to use scaled-dot-product attention
-# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
-sdp_attention:
-
-# Resume from a specific checkpoint dir
-resume_from_checkpoint:
-# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
-# Be careful with this being turned on between different models.
-auto_resume_from_checkpoints: false
-
-# Don't mess with this, it's here for accelerate and torchrun
-local_rank:
-
-# Add or change special tokens.
-# If you add tokens here, you don't need to add them to the `tokens` list.
-special_tokens:
-  # bos_token: "<s>"
-  # eos_token: "</s>"
-  # unk_token: "<unk>"
-
-# Add extra tokens.
-tokens:
-
-# FSDP
-fsdp:
-fsdp_config:
-
-# Deepspeed config path. e.g., deepspeed/zero3.json
-deepspeed:
-
-# Advanced DDP Arguments
-ddp_timeout:
-ddp_bucket_cap_mb:
-ddp_broadcast_buffers:
-
-# Path to torch distx for optim 'adamw_anyprecision'
-torchdistx_path:
-
-# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
-pretraining_dataset:
-
-# Debug mode
-debug:
-
-# Seed
-seed:
-
-# Allow overwrite yml config using from cli
-strict:
-```
-
-</details>
-
-<details>
-<summary> Understanding of batch size and gradient accumulation steps </summary>
-<br/>
-Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.
-
-This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:
-
-1. **Memory Consumption with Batch Size**: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.
-
-2. **Gradient Accumulation**: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.
-
-**Example 1:**
-Micro batch size: 3
-Gradient accumulation steps: 2
-Number of GPUs: 3
-Total batch size = 3 * 2 * 3 = 18
-
-```
-| GPU 1          | GPU 2          | GPU 3          |
-|----------------|----------------|----------------|
-| S1, S2, S3     | S4, S5, S6     | S7, S8, S9     |
-| e1, e2, e3     | e4, e5, e6     | e7, e8, e9     |
-|----------------|----------------|----------------|
-| → (accumulate) | → (accumulate) | → (accumulate) |
-|----------------|----------------|----------------|
-| S10, S11, S12  | S13, S14, S15  | S16, S17, S18  |
-| e10, e11, e12  | e13, e14, e15  | e16, e17, e18  |
-|----------------|----------------|----------------|
-| → (apply)      | → (apply)      | → (apply)      |
-
-Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
-Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18
-
-Weight update for w1:
-w1_new = w1_old - learning rate x (Total gradient for w1 / 18)
-```
-
-**Example 2:**
-Micro batch size: 2
-Gradient accumulation steps: 1
-Number of GPUs: 3
-Total batch size = 2 * 1 * 3 = 6
-
-```
-| GPU 1     | GPU 2     | GPU 3     |
-|-----------|-----------|-----------|
-| S1, S2    | S3, S4    | S5, S6    |
-| e1, e2    | e3, e4    | e5, e6    |
-|-----------|-----------|-----------|
-| → (apply) | → (apply) | → (apply) |
-
-Accumulated gradient for the weight w1 (considering all GPUs):
-Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6
-
-Weight update for w1:
-w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
-```
-
-</details>
-
-### Train
-
-Run
-```bash
-accelerate launch -m axolotl.cli.train your_config.yml
-```
-
-#### Preprocess dataset
-
-You can optionally pre-tokenize dataset with the following before finetuning.
-This is recommended for large datasets.
-
-- Set `push_dataset_to_hub: hf_user/repo` to push it to Huggingface.
-- Use `--debug` to see preprocessed examples.
-
-```bash
-python -m axolotl.cli.preprocess your_config.yml
-```
-
-#### Multi-GPU
-
-Below are the options available in axolotl for training with multiple GPUs. Note that DeepSpeed
-is the recommended multi-GPU option currently because FSDP may experience
-[loss instability](https://github.com/huggingface/transformers/issues/26498).
-
-##### DeepSpeed
-
-Deepspeed is an optimization suite for multi-gpu systems allowing you to train much larger models than you
-might typically be able to fit into your GPU's VRAM. More information about the various optimization types
-for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed#what-is-integrated
-
-We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
-
-```yaml
-deepspeed: deepspeed/zero1.json
-```
-
-```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
-```
-
-##### FSDP
-
-- llama FSDP
-```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-```
-
-##### Weights & Biases Logging
-
-Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
-
-- wandb options
-```yaml
-wandb_mode:
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-```
-
-##### Special Tokens
-
-It is important to have special tokens like delimiters, end-of-sequence, beginning-of-sequence in your tokenizer's vocabulary.  This will help you avoid tokenization issues and help your model train better.  You can do this in axolotl like this:
-
-```yml
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
-tokens: # these are delimiters
-  - "<|im_start|>"
-  - "<|im_end|>"
-```
-
-When you include these tokens in your axolotl config, axolotl adds these tokens to the tokenizer's vocabulary.
-
-### Inference Playground
-
-Axolotl allows you to load your model in an interactive terminal playground for quick experimentation.
-The config file is the same config file used for training. 
-
-Pass the appropriate flag to the inference command, depending upon what kind of model was trained:
-
-- Pretrained LORA:
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --lora_model_dir="./lora-output-dir"
-  ```
-- Full weights finetune:
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --base_model="./completed-model"
-  ```
-- Full weights finetune w/ a prompt from a text file:
-  ```bash
-  cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
-    --base_model="./completed-model" --prompter=None --load_in_8bit=True
-  ```
--- With gradio hosting
-  ```bash
-  python -m axolotl.cli.inference examples/your_config.yml --gradio
-  ```
-
-Please use `--sample_packing False` if you have it on and receive the error similar to below:
-
-> RuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1
-
-### Merge LORA to base
-
-Add below flag to train command above
-
-```bash
-python3 -m axolotl.cli.merge_lora examples/your_config.yml --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
-```
-
-If you run out of CUDA memory, you can try to merge in system RAM with
-
-```bash
-CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...
-```
-
-## Common Errors 🧰
-
-See also the [FAQ's](./docs/faq.md).
-
-> If you encounter a 'Cuda out of memory' error, it means your GPU ran out of memory during the training process. Here's how to resolve it:
-
-Please reduce any below
-  - `micro_batch_size`
-  - `eval_batch_size`
-  - `gradient_accumulation_steps`
-  - `sequence_len`
-
-If it does not help, try running without deepspeed and without accelerate (replace "accelerate launch" with "python") in the command.
-
-Using adamw_bnb_8bit might also save you some memory.
-
-> `failed (exitcode: -9)`
-
-Usually means your system has run out of system memory.
-Similarly, you should consider reducing the same settings as when you run out of VRAM.
-Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.
-
-> RuntimeError: expected scalar type Float but found Half
-
-Try set `fp16: true`
-
-> NotImplementedError: No operator found for `memory_efficient_attention_forward` ...
-
-Try to turn off xformers.
-
-> accelerate config missing
-
-It's safe to ignore it.
-
-> NCCL Timeouts during training
-
-See the [NCCL](docs/nccl.md) guide.
-
-
-### Tokenization Mismatch b/w Inference & Training
-
-For many formats, Axolotl constructs prompts by concatenating token ids _after_ tokenizing strings.  The reason for concatenating token ids rather than operating on strings is to maintain precise accounting for attention masks.
-
-If you decode a prompt constructed by axolotl, you might see spaces between tokens (or lack thereof) that you do not expect, especially around delimiters and special tokens.  When you are starting out with a new format, you should always do the following:
-
-1. Materialize some data using `python -m axolotl.cli.preprocess your_config.yml --debug`, and then decode the first few rows with your model's tokenizer.
-2. During inference, right before you pass a tensor of token ids to your model, decode these tokens back into a string.
-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 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/05_tokenizer_gotchas.html) for a concrete example.
-
-## Need help? 🙋♂️
-
-Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
-
-## Badge ❤🏷️
-
-Building something cool with Axolotl? Consider adding a badge to your model card.
-
-```markdown
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-```
-
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-
-## Community Showcase
-
-Check out some of the projects and models that have been built using Axolotl! Have a model you'd like to add to our Community Showcase? Open a PR with your model.
-
-Open Access AI Collective
-- [Minotaur 13b](https://huggingface.co/openaccess-ai-collective/minotaur-13b)
-- [Manticore 13b](https://huggingface.co/openaccess-ai-collective/manticore-13b)
-- [Hippogriff 30b](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)
-
-PocketDoc Labs
-- [Dan's PersonalityEngine 13b LoRA](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b-LoRA)
-
-## Contributing 🤝
-
-Please read the [contributing guide](./.github/CONTRIBUTING.md)
-
-Bugs? Please check the [open issues](https://github.com/OpenAccess-AI-Collective/axolotl/issues/bug) else create a new Issue.
-
-PRs are **greatly welcome**!
-
-Please run below to setup env
-```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
-pre-commit install
-
-# test
-pytest tests/
-```

axolotl/TODO.md

@@ -1,10 +0,0 @@
-# todo list
-
-- [] Validation of parameters for combinations that won't work
-
-
-
-## things that are known not to work
-
-- FSDP offload and gradient_checkpointing - https://github.com/pytorch/pytorch/issues/82203
-- adamw_bnb_8bit doesn't play well with FSDP offload

axolotl/deepspeed/zero1.json

@@ -1,31 +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
-  },
-  "optimizer": {
-    "type": "AdamW",
-    "params": {
-      "lr": "auto",
-      "betas": "auto",
-      "eps": "auto",
-      "weight_decay": "auto"
-    }
-  },
-  "gradient_accumulation_steps": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

axolotl/deepspeed/zero2.json

@@ -1,35 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 2,
-    "offload_optimizer": {
-      "device": "cpu"
-    },
-    "contiguous_gradients": true,
-    "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
-  },
-  "optimizer": {
-    "type": "AdamW",
-    "params": {
-      "lr": "auto",
-      "betas": "auto",
-      "eps": "auto",
-      "weight_decay": "auto"
-    }
-  },
-  "gradient_accumulation_steps": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

axolotl/deepspeed/zero3.json

@@ -1,39 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 3,
-    "overlap_comm": true,
-    "contiguous_gradients": true,
-    "sub_group_size": 0,
-    "reduce_bucket_size": "auto",
-    "stage3_prefetch_bucket_size": "auto",
-    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": 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
-  },
-  "optimizer": {
-    "type": "AdamW",
-    "params": {
-      "lr": "auto",
-      "betas": "auto",
-      "eps": "auto",
-      "weight_decay": "auto"
-    }
-  },
-  "gradient_accumulation_steps": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

axolotl/deepspeed/zero3_bf16.json

@@ -1,39 +0,0 @@
-{
-  "zero_optimization": {
-    "stage": 3,
-    "overlap_comm": true,
-    "contiguous_gradients": true,
-    "sub_group_size": 0,
-    "reduce_bucket_size": "auto",
-    "stage3_prefetch_bucket_size": "auto",
-    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
-  },
-  "bf16": {
-    "enabled": true
-  },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "optimizer": {
-    "type": "AdamW",
-    "params": {
-      "lr": "auto",
-      "betas": "auto",
-      "eps": "auto",
-      "weight_decay": "auto"
-    }
-  },
-  "gradient_accumulation_steps": "auto",
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "wall_clock_breakdown": false
-}

axolotl/docker-compose.yaml

@@ -1,25 +0,0 @@
-# version: '3.8'
-services:
-  axolotl:
-    build:
-      context: .
-      dockerfile: ./docker/Dockerfile
-    volumes:
-      - .:/workspace/axolotl
-      - ~/.cache/huggingface/:/root/.cache/huggingface/
-    # set environment variables
-    environment:
-      # Set environment variables
-      - GIT_AUTHOR_NAME=${GIT_AUTHOR_NAME}
-      - GIT_AUTHOR_EMAIL=${GIT_AUTHOR_EMAIL}
-      - GIT_COMMITTER_NAME=${GIT_COMMITTER_NAME}
-      - GIT_COMMITTER_EMAIL=${GIT_COMMITTER_EMAIL}
-      - WANDB_API_KEY=${WANDB_API_KEY}
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              # count: 1
-              capabilities: [gpu]
-    command: tail -f /dev/null

axolotl/docker/Dockerfile

@@ -1,36 +0,0 @@
-ARG BASE_TAG=main-base
-FROM winglian/axolotl-base:$BASE_TAG
-
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
-ARG AXOLOTL_EXTRAS=""
-ARG CUDA="118"
-ENV BNB_CUDA_VERSION=$CUDA
-ARG PYTORCH_VERSION="2.0.1"
-
-ENV PYTORCH_VERSION=$PYTORCH_VERSION
-
-RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
-
-WORKDIR /workspace
-
-RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
-
-WORKDIR /workspace/axolotl
-
-# If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,$AXOLOTL_EXTRAS]; \
-    else \
-        pip install -e .[deepspeed,flash-attn]; \
-    fi
-
-# So we can test the Docker image
-RUN pip install pytest
-
-# fix so that git fetch/pull from remote works
-RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
-
-# helper for huggingface-login cli
-RUN git config --global credential.helper store

axolotl/docker/Dockerfile-base

@@ -1,37 +0,0 @@
-ARG CUDA_VERSION="11.8.0"
-ARG CUDNN_VERSION="8"
-ARG UBUNTU_VERSION="22.04"
-ARG MAX_JOBS=4
-
-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION as base-builder
-
-ENV PATH="/root/miniconda3/bin:${PATH}"
-
-ARG PYTHON_VERSION="3.9"
-ARG PYTORCH_VERSION="2.0.1"
-ARG CUDA="118"
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
-
-ENV PYTHON_VERSION=$PYTHON_VERSION
-ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
-
-RUN apt-get update \
-    && 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 \
-    && bash Miniconda3-latest-Linux-x86_64.sh -b \
-    && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
-
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
-
-WORKDIR /workspace
-
-RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} deepspeed-kernels --extra-index-url https://download.pytorch.org/whl/cu$CUDA
-
-RUN git lfs install --skip-repo && \
-    pip3 install awscli && \
-    # The base image ships with `pydantic==1.8.2` which is not working
-    pip3 install -U --no-cache-dir pydantic==1.10.10

axolotl/docker/Dockerfile-runpod

@@ -1,19 +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"
-
-COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
-
-RUN apt install --yes --no-install-recommends openssh-server tmux && \
-    mkdir -p ~/.ssh && \
-    chmod 700 ~/.ssh && \
-    printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \
-    chmod +x /workspace/axolotl/scripts/runpod-entrypoint.sh && \
-    chmod +x /root/runpod-entrypoint.sh
-
-ENTRYPOINT ["/root/runpod-entrypoint.sh"]
-CMD ["sleep", "infinity"]

axolotl/docs/faq.md

@@ -1,18 +0,0 @@
-# Axolotl FAQ's
-
-
-> The trainer stopped and hasn't progressed in several minutes.
-
-Usually an issue with the GPU's communicating with each other. See the [NCCL doc](../docs/nccl.md)
-
-> Exitcode -9
-
-This usually happens when you run out of system RAM.
-
-> Exitcode -7 while using deepspeed
-
-Try upgrading deepspeed w: `pip install -U deepspeed`
-
-> AttributeError: 'DummyOptim' object has no attribute 'step'
-
-You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.

axolotl/docs/multi-node.md

@@ -1,45 +0,0 @@
-# Multi Node
-
-You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:
-
-~/.cache/huggingface/accelerate/default_config.yaml
-```yaml
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-machine_rank: 0 # Set to 0 for the main machine, increment by one for other machines
-main_process_ip: 10.0.0.4 # Set to main machine's IP
-main_process_port: 5000
-main_training_function: main
-mixed_precision: bf16
-num_machines: 2 # Change to the number of machines
-num_processes: 4 # That's the total number of GPUs, (for example: if you have 2 machines with 4 GPU, put 8)
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
-```
-
-Configure your model to use FSDP with for example:
-```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
-fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
-```
-
-## Machine configuration
-
-On each machine you need a copy of Axolotl, we suggest using the same commit to ensure compatibility.
-
-You will also need to have the same configuration file for your model on each machine.
-
-On the main machine only, make sure the port you set as `main_process_port` is open in TCP and reachable by other machines.
-
-All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.

axolotl/docs/multipack.md

@@ -1,51 +0,0 @@
-# Multipack
-
-4k context, bsz =4,
-each character represents 256 tokens
-X represents a padding token
-
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A ]
-   B B B B B B ]
-   C C C C C C C ]
-   D D D D ]]
-
-[[ E E E E E E E E ]
- [ F F F F ]
- [ G G G ]
- [ H H H H ]]
-
-[[ I I I ]
- [ J J J ]
- [ K K K K K]
- [ L L L ]]
-```
-
-after padding to longest input in each step
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A ]
-   B B B B B B X X X X X X ]
-   C C C C C C C X X X X ]
-   D D D D X X X X X X X ]]
-
-[[ E E E E E E E E ]
- [ F F F F X X X X ]
- [ G G G X X X X X ]
- [ H H H H X X X X ]]
-
-[[ I I I X X ]
- [ J J J X X ]
- [ K K K K K ]
- [ L L L X X ]]
-```
-
-w packing ( note it's the same effective number of tokens per step, but a true bsz of 1)
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A B B B B B
-   B C C C C C C C D D D D E E E E
-   E E E E F F F F F G G G H H H H
-   I I I J J J J K K K K K L L L X ]]
-```

axolotl/docs/nccl.md

@@ -1,46 +0,0 @@
-# NCCL
-
-NVIDIA NCCL is a library to facilitate and optimize multi-GPU communication operations, such as broadcast, all-gather, reduce, all-reduce, etc. Broadly, NCCL configuration is highly environment-specific and is configured via several [environment variables](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html). A common NCCL-related problem occurs when a long-running operation times out causing the training process to abort:
-
-```text
-Watchdog caught collective operation timeout: WorkNCCL(SeqNum=42, OpType=ALLGATHER, Timeout(ms)=1800000) ran for 1806948 milliseconds before timing out.
-```
-
-Often, this timeout will happen after 30 minutes (the default setting) and is accompanied by below-average power consumption with near 100% GPU utilization before the error is raised. Nvidia recommends [disabling PCI access control services (ACS)](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#pci-access-control-services-acs) as a possible solution if this is available to you.
-
-Forcing cross-GPU communication via [NVLink](https://en.wikipedia.org/wiki/NVLink) may help without increasing timeouts. To verify that your configuration is leveraging NVLink run the following command:
-
-```shell
-nvidia-smi nvlink --status
-```
-
-To force NCCL to use NVLink, simply set this in the environment:
-
-```shell
-export NCCL_P2P_LEVEL=NVL
-```
-
-If NVLink is not available in your environment there are other options for ``NCCL_P2P_LEVEL`` in the table below:
-
-| NCCL_P2P_LEVEL | Description |
-| -------------- | ----------- |
-| PIX | P2P data transfers through no more than a single PCIe bridge. Faster data transfer rates vs to paths involving multiple bridges, but slower compared to direct GPU-to-GPU communication. |
-| PXB | P2P data transfers through multiple PCIe bridges but not going through the PCIe Host Bridge; this path involves a complex routing process, potentially incurring a moderate level of latency. |
-| PHB | P2P data transfers occur over the PCIe and through a PCIe Host Bridge, typically involving the CPU, which can facilitate direct memory access but might introduce additional latency compared to more direct paths (ex PIX, NVL) |
-
-To validate that acceptable data transfer speeds exist for your training job, running [NCCL Tests](https://github.com/NVIDIA/nccl-tests/blob/master/README.md) can help pinpoint bottlenecks, for example:
-
-```shell
-./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3
-```
-
-It can be useful when debugging NCCL communication timeouts to activate additional logging in both PyTorch and NCCL:
-
-```shell
-export NCCL_DEBUG=INFO
-export NCCL_DEBUG_SUBSYS=ALL
-export TORCH_DISTRIBUTED_DEBUG=INFO
-export TORCHELASTIC_ERROR_FILE=/PATH/TO/torcherror.log
-```
-
-Finally, if you believe your training job needs more time you can increase the timeout past 30 minutes by setting the ``ddp_timeout`` value in the Axolotl configuration. See [PyTorch init_process_group](https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for documentation on this value.

axolotl/examples/cerebras/btlm-ft.yml

@@ -1,89 +0,0 @@
-base_model: cerebras/btlm-3b-8k-base
-model_type: AutoModelForCausalLM
-tokenizer_type: GPT2Tokenizer
-trust_remote_code: true
-tokenizer_use_fast: true
-tokenizer_legacy: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-push_dataset_to_hub:
-hf_use_auth_token: true
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_prepared_run
-val_set_size: 0.05
-
-adapter:
-lora_model_dir:
-sequence_len: 2048
-max_packed_sequence_len:
-sample_packing: false
-sample_packing_eff_est:
-sample_packing_seq_len_multiplier:
-total_num_tokens:
-
-lora_r:
-lora_alpha:
-lora_dropout:
-lora_target_modules:
-lora_target_linear:
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-output_dir: btlm-out
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch
-adam_beta2: 0.95
-adam_eps: 0.000000001
-max_grad_norm: 1.0
-
-torchdistx_path:
-lr_scheduler: cosine
-lr_quadratic_warmup: true
-learning_rate: 0.000085
-train_on_inputs: true
-group_by_length: false
-bf16: true
-fp16: false
-tf32: true
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-
-xformers_attention:
-flash_attention: true
-sdp_attention:
-flash_optimum:
-
-gptq_groupsize:
-gptq_model_v1:
-
-warmup_steps: 32
-evals_per_epoch: 4
-saves_per_epoch: 1
-save_total_limit:
-
-debug:
-deepspeed:
-weight_decay: 0.1
-special_tokens:
-  pad_token: "<|endoftext|>"
-fsdp:
-#  - full_shard
-#  - auto_wrap
-fsdp_config:
-#  fsdp_state_dict_type: FULL_STATE_DICT
-#  fsdp_transformer_layer_cls_to_wrap: BTLMBlock

axolotl/examples/cerebras/qlora.yml

@@ -1,60 +0,0 @@
-base_model: cerebras/Cerebras-GPT-1.3B
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-push_dataset_to_hub:
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-adapter: qlora
-lora_model_dir:
-sequence_len: 2048
-max_packed_sequence_len: 2048
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - c_fc
-  - c_attn
-  - c_proj
-lora_target_linear:
-lora_fan_in_fan_out:
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-output_dir: ./qlora-out
-batch_size: 4
-micro_batch_size: 4
-num_epochs: 2
-optimizer: paged_adamw_8bit
-torchdistx_path:
-lr_scheduler: cosine
-learning_rate: 0.0002
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: true
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention: true
-flash_attention:
-gptq_groupsize:
-gptq_model_v1:
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.1
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: "<|endoftext|>"

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-13b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-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: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-13b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-34b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-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: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-34b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,67 +0,0 @@
-base_model: codellama/CodeLlama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 4096
-sample_packing: true
-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: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"

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

@@ -1,69 +0,0 @@
-base_model: codellama/CodeLlama-7b-hf
-model_type: LlamaForCausalLM
-tokenizer_type: CodeLlamaTokenizer
-is_llama_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: paged_adamw_32bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 4
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"