This repository provides Pai-Megatron-Patch, a toolkit for efficiently training and predicting Large Language Models (LLMs) and Vision-Language Models (VLMs) using the Megatron framework. It targets developers seeking to optimize GPU utilization for large-scale models, offering accelerated training techniques and broad model compatibility.

How It Works

Pai-Megatron-Patch applies a "patch" philosophy, extending Megatron-LM's capabilities without invasive source code modifications. This approach ensures compatibility with Megatron-LM upgrades. It includes a model library with implementations of popular LLMs, bidirectional weight converters for Huggingface and Megatron formats, and supports FP8 acceleration via Flash Attention 2.0 and Transformer Engine.

Quick Start & Requirements

• Installation and usage details are available via the "Quick Start" link in the README.
• Requires a robust GPU environment, likely with CUDA support, for large-scale training. Specific model training may have additional dependencies.
• Refer to the official documentation for detailed setup and examples.

Highlighted Details

• Supports a wide range of LLMs including Llama, Qwen, Mistral, DeepSeek, and more.
• Facilitates bidirectional weight conversion between Huggingface and Megatron formats.
• Offers FP8 training acceleration with Flash Attention 2.0 and Transformer Engine.
• Includes PPO training workflows for reinforcement learning.

Maintenance & Community

• Developed by Alibaba Cloud's Machine Learning Platform (PAI) algorithm team.
• Contact information via DingTalk QR code is provided.

Licensing & Compatibility

• Licensed under the Apache License (Version 2.0).
• May contain code from other repositories under different open-source licenses; consult the NOTICE file.

Limitations & Caveats

• Some features are marked as experimental, such as distributed checkpoint conversion.
• The README indicates specific model support via links, suggesting a modular or evolving integration strategy.