From b9db0cad1df202865b1aa14476d948fd0990f4b8 Mon Sep 17 00:00:00 2001 From: mhenrhcsen Date: Wed, 18 Jun 2025 14:06:14 +0200 Subject: [PATCH] Enhance README.md with updated layout and content, including a new introduction, improved visual elements, and detailed sections on latest updates, features, quick start guide, and documentation. This update aims to provide a more engaging and informative experience for users. --- README.md | 461 ++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 360 insertions(+), 101 deletions(-) diff --git a/README.md b/README.md index ef5523898..b119f6e12 100644 --- a/README.md +++ b/README.md @@ -1,124 +1,383 @@ -

- - - - Axolotl - -

+
+ + + + Axolotl + + +

🧠 Streamlined AI Model Post-Training

+

+ A powerful, flexible tool designed to streamline post-training for various AI models with enterprise-grade features and optimizations. +

+
-

- GitHub License - tests - codecov - Releases -
- contributors - GitHub Repo stars -
- discord - twitter -
- tests-nightly - multigpu-semi-weekly tests -

+
+ + + + + + + + + +
+ GitHub License + + GitHub Repo stars +
+ tests + + + codecov + +
+
+
+ + contributors + + + Releases + +
-## πŸŽ‰ Latest Updates +
+ + discord + + + twitter + +
-- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl! -- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more! -- 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version! -- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning. -- 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own! -- 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try. -- 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun! -- 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See [docs](https://docs.axolotl.ai/docs/reward_modelling.html). +--- -## ✨ Overview +
+

+ πŸŽ‰ Latest Updates +

+ +
+
+ + πŸ“… 2025/06: Magistral Support Added + +

+ Magistral with mistral-common tokenizer support has been added to Axolotl. + + See examples β†’ + +

+
+ +
+ + πŸ“… 2025/05: Quantization Aware Training (QAT) + +

+ QAT support has been added to Axolotl. + + Explore the docs β†’ + +

+
+ +
+ + πŸ“… 2025/04: Llama 4 Support + +

+ Llama 4 support has been added in Axolotl. + + See examples β†’ + +

+
+ +
+ + πŸ“… 2025/03: Sequence Parallelism & Multimodal Support + +
+

+ β€’ Sequence Parallelism (SP) for scaling context length - + + Blog + | + + Docs + +

+

+ β€’ (Beta) Multimodal models fine-tuning - + + Check docs β†’ + +

+
+
+ +
+ + πŸ“… 2025/02: LoRA Optimizations & GRPO Support + +
+

+ β€’ LoRA optimizations for better memory usage and speed - + + Docs β†’ + +

+

+ β€’ GRPO support added - + + Blog + | + + Example + +

+
+
+ +
+ + πŸ“… 2025/01: Reward Modelling Support + +

+ Reward Modelling / Process Reward Modelling fine-tuning support added. + + See docs β†’ + +

+
+
+
-Axolotl is a tool designed to streamline post-training for various AI models. +--- -Features: +
+

+ ✨ What Makes Axolotl Special +

+ +
+
+

πŸš€ Multiple Model Support

+

+ Train LLaMA, Mistral, Mixtral, Pythia, and more. Full compatibility with HuggingFace transformers causal language models. +

+
+ +
+

🎯 Advanced Training Methods

+

+ Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling. +

+
+ +
+

βš™οΈ Easy Configuration

+

+ Reuse a single YAML file across dataset preprocessing, training, evaluation, quantization, and inference. +

+
+ +
+

⚑ Performance Optimizations

+

+ Multipacking, Flash Attention, Xformers, Flex Attention, Liger Kernel, Sequence Parallelism, and Multi-GPU training. +

+
+ +
+

πŸ“Š Flexible Dataset Handling

+

+ Load from local files, HuggingFace datasets, and cloud storage (S3, Azure, GCP, OCI). +

+
+ +
+

☁️ Cloud Ready

+

+ Pre-built Docker images and PyPI packages for seamless deployment on cloud platforms and local hardware. +

+
+
+
-- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models. -- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM). -- **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference. -- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more! -- **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets. -- **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware. +--- - - -## πŸš€ Quick Start - -**Requirements**: - -- NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU -- Python 3.11 -- PyTorch β‰₯2.5.1 - -### Installation - -```bash +
+

+ πŸš€ Quick Start +

+ +
+

πŸ“‹ Requirements

+
    +
  • GPU: NVIDIA GPU (Ampere or newer for bf16 and Flash Attention) or AMD GPU
    • +
  • Python: 3.11+
    • +
  • PyTorch: β‰₯2.5.1
    • +
+
+ +
+

πŸ’Ύ Installation

+
+ 
# Install dependencies
 pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
+
+# Install Axolotl with Flash Attention and DeepSpeed
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
-# Download example axolotl configs, deepspeed configs
+# Download examples and configs
 axolotl fetch examples
-axolotl fetch deepspeed_configs  # OPTIONAL
-```
-
-Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
-
-### Your First Fine-tune
-
-```bash
-# Fetch axolotl examples
+axolotl fetch deepspeed_configs  # OPTIONAL
+
+

+ Other installation methods available in our documentation +

+
+ +
+

🎯 Your First Fine-tune

+
+ 
# Fetch examples
 axolotl fetch examples
 
-# Or, specify a custom path
+# Or specify custom path
 axolotl fetch examples --dest path/to/folder
 
-# Train a model using LoRA
-axolotl train examples/llama-3/lora-1b.yml
-```
+# Start training with LoRA
+axolotl train examples/llama-3/lora-1b.yml
+
+

+ That's it! Check our Getting Started Guide for detailed walkthrough +

+
+
-That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough. +--- +
+

+ πŸ“š Documentation Hub +

+ +
+ +
+

πŸ”§ Installation Options

+

Detailed setup instructions for different environments

+
+ + + +
+

βš™οΈ Configuration Guide

+

Full configuration options and examples

+
+ + + +
+

πŸ“Š Dataset Loading

+

Loading datasets from various sources

+
+ + + +
+

πŸ“‹ Dataset Guide

+

Supported formats and usage instructions

+
+ + + +
+

πŸ–₯️ Multi-GPU Training

+

Scale your training across multiple GPUs

+
+ + + +
+

🌐 Multi-Node Training

+

Distributed training across multiple machines

+
+ + + +
+

πŸ“¦ Multipacking

+

Efficient batch packing for training

+
+ + + +
+

πŸ” API Reference

+

Auto-generated code documentation

+
+ + + +
+

❓ FAQ

+

Frequently asked questions

+
+ +
+
-## πŸ“š Documentation +--- -- [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments -- [Configuration Guide](https://docs.axolotl.ai/docs/config.html) - Full configuration options and examples -- [Dataset Loading](https://docs.axolotl.ai/docs/dataset_loading.html) - Loading datasets from various sources -- [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them -- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html) -- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html) -- [Multipacking](https://docs.axolotl.ai/docs/multipack.html) -- [API Reference](https://docs.axolotl.ai/docs/api/) - Auto-generated code documentation -- [FAQ](https://docs.axolotl.ai/docs/faq.html) - Frequently asked questions +
+

+ 🀝 Getting Help +

+ +
+
+
πŸ’¬
+

Community Support

+

Join thousands of developers in our Discord

+ Join Discord +
+ +
+
πŸ“–
+

Examples

+

Browse our comprehensive examples

+ View Examples +
+ +
+
πŸ”§
+

Debugging

+

Troubleshooting and debugging guide

+ Debug Guide +
+ +
+
βœ‰οΈ
+

Enterprise Support

+

Need dedicated support? Contact us

+ Contact Us +
+
+
-## 🀝 Getting Help +--- -- Join our [Discord community](https://discord.gg/HhrNrHJPRb) for support -- Check out our [Examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/) directory -- Read our [Debugging Guide](https://docs.axolotl.ai/docs/debugging.html) -- Need dedicated support? Please contact [βœ‰οΈwing@axolotl.ai](mailto:wing@axolotl.ai) for options - -## 🌟 Contributing - -Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details. - -## ❀️ Sponsors - -Thank you to our sponsors who help make Axolotl possible: - -- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl) - Modal lets you run -jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale, -fine-tune large language models, run protein folding simulations, and much more. - -Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai) - -## πŸ“œ License - -This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. +
+

+ 🌟 Contributing +

+ +
+

+ We welcome contributions from the community! Whether it's bug fixes, \ No newline at end of file