Pai-Megatron-Patch accelerates the training of PyTorch-based Transformer models using a suite of optimization techniques layered on top of Megatron-LM — without modifying its source code.
Best for:
ML engineers running distributed Large Language Model (LLM) training on PAI-Lingjun AI Computing Service
Teams that need to switch between Hugging Face and Megatron-LM model weights
Practitioners adding reinforcement learning (PPO) to their LLM training pipeline
What you get:
A non-invasive patch that stays compatible with upstream Megatron-LM updates
End-to-end workflows covering pre-training, supervised fine-tuning (SFT), and offline inference
A model library with popular open-source LLMs, bidirectional weight conversion, and PPO training support
About Pai-Megatron-Patch
Pai-Megatron-Patch is developed by the algorithm team of Alibaba Cloud's Platform for AI (PAI). It is a companion toolkit for PAI-Lingjun AI Computing Service, providing Megatron-LM-based workflows for training and offline validation of mainstream open-source LLMs.
The toolkit covers the full LLM workflow: efficient distributed training, Supervised Instruction Fine-tuning (SFT), and offline inference and validation.
How it works
Pai-Megatron-Patch extends Megatron-LM by applying a patch rather than modifying its source code directly. This non-invasive design lets you build an independent LLM training workflow with additional capabilities, while the core Megatron-LM library stays untouched and compatible with future upstream updates.
Components
|
Component |
Description |
|
Model library |
Popular open-source LLMs: Baichuan, Bloom, ChatGLM, Falcon, Galactica, GLM, Llama, Qwen, and StarCoder |
|
Tokenizers |
Tokenizers for supported models |
|
Model conversion tools |
Bidirectional conversion between Hugging Face and Megatron-LM model weights — load Hugging Face weights for pre-training or fine-tuning, then export back to Hugging Face format for evaluation and inference |
|
Reinforcement learning |
PPO training workflows using SFT and reward model (RM) models |
|
Offline text generation |
Offline text generation capabilities for validation |
|
Examples and tools |
Ready-to-run examples for LLM training and inference |
Ecosystem position
Pai-Megatron-Patch sits between PAI-Lingjun and Megatron-LM, and integrates with the Hugging Face model ecosystem:
Built on: Megatron-LM (core training framework)
Runs on: PAI-Lingjun AI Computing Service
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Compatible with: Hugging Face model weights (bidirectional conversion)
Get started
Follow this workflow to set up and use Pai-Megatron-Patch: