ChatLearn - A flexible, easy-to-use, and efficient large-scale Alignment training framework launched by Alibaba Cloud

What is ChatLearn

ChatLearn is a flexible, easy-to-use, and efficient large-scale Alignment training framework launched by Alibaba Cloud. ChatLearn is designed to support Alignment training for large language models (LLMs). ChatLearn provides multiple Alignment training methods such as RLHF, DPO, OnlineDPO, and GRPO, and supports user-defined model execution workflows, making customized training very convenient.

Main features of ChatLearn

• Multiple training modes: Supports multiple training modes such as RLHF, DPO, OnlineDPO, and GRPO.
• Programming interface: Provides an easy-to-use programming interface that allows users to build models by encapsulating a few functions.
• Resource scheduling: Has a flexible resource scheduling mechanism that can allocate resources according to the model's computational requirements, GPU memory, and communication characteristics, and supports exclusive model use or resource sharing.
• Distributed acceleration engine: Supports multiple distributed computing backends.
• Parallel strategies: Supports configuring different parallel strategies for different models to maximize training efficiency.

ChatLearn project website

• Project official website：chatlearn.readthedocs.io
• GitHub repository: https://github.com/alibaba/ChatLearn

How to use ChatLearn

• Environment and code preparation: Prepare the environment, referring to the image preparation recommendations in the official ChatLearn documentation.
• Data preparation: According to the training type (such as SFT, Reward, RLHF, etc.), prepare the corresponding training data and format the data according to the guidelines in the documentation.
• Distributed execution: For training on the Alibaba Cloud PAI DLC environment, use PAI DLC to create jobs; in other environments, you need to configure environment variables such as MASTER_ADDR、MASTER_PORT、WORLD_SIZE etc., to support distributed execution.
• Start training: End-to-end training based on a specific model (such as the Llama model).
• Monitoring and evaluation: Use the tools and interfaces provided by ChatLearn to monitor the training process and evaluate model performance.

Application scenarios of ChatLearn

• Automated dialogue system training: Used to train automated dialogue systems like ChatGPT to improve their dialogue capabilities.
• Multi-model computation and data interaction: Supports computation and data interaction for multiple large models, suitable for complex training paradigms such as RLHF.
• Custom training workflows: Users can customize the model execution workflow according to their own needs to implement personalized training strategies.
• Resource scheduling and optimization: Provides a flexible resource scheduling mechanism, optimizes resource allocation and parallel scheduling strategies, and improves training efficiency.