ChatLearn - A flexible, easy-to-use, and efficient large-scale Alignmant 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 of large language models (LLMs). ChatLearn provides multiple alignment training methods such as RLHF, DPO, OnlineDPO, and GRPO, and supports user-defined model execution flows, making customized training very convenient.

ChatLearn's main functions

• Multiple training modesSupports multiple training modes such as RLHF, DPO, OnlineDPO, and GRPO.
• Programming InterfaceIt provides an easy-to-use programming interface, allowing users to build models by encapsulating a few functions.
• Resource schedulingIt has a flexible resource scheduling mechanism that can allocate resources according to the model's computational needs, memory and communication characteristics, and supports exclusive use of the model or resource sharing.
• Distributed acceleration engineSupports multiple distributed computing backends.
• Parallel strategyIt supports configuring different parallel strategies for different models to maximize training efficiency.

ChatLearn's project website

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

How to use ChatLearn

• Environment and code preparationPrepare your environment by referring to the mirror preparation suggestions in the ChatLearn official documentation.
• Data preparationPrepare the corresponding training data according to the training type (such as SFT, Reward, RLHF, etc.) and format the data according to the guidelines in the document.
• Distributed executionTraining is performed on the Alibaba Cloud PAI DLC environment, using PAI DLC to create tasks; in other environments, environment variables need to be configured, such as... MASTER_ADDR,MASTER_PORT,WORLD_SIZE etc., to support distributed execution.
• Start trainingEnd-to-end training based on a specific model (such as the Llama model).
• Monitoring and evaluationUse the tools and interfaces provided by ChatLearn to monitor the training process and evaluate model performance.

ChatLearn application scenarios

• Training of automated dialogue systemsUsed to train automated dialogue systems like ChatGPT and improve their conversational capabilities.
• Multi-model computation and data interactionIt supports computation and data interaction for multiple large models and is suitable for complex training paradigms, such as RLHF.
• Custom training processUsers can customize the model's execution flow according to their own needs to achieve personalized training strategies.
• Resource scheduling and optimizationIt provides a flexible resource scheduling mechanism, optimizes resource allocation and parallel scheduling strategies, and improves training efficiency.