Awesome-LLM-Post-training
A resource library of tutorials, surveys, and guidance on large language model (LLM) post-training methods.
Product Details
Awesome-LLM-Post-training is a resource library focused on large language model (LLM) post-training methods. It provides an in-depth look at post-LLM training, including tutorials, surveys, and guides. This resource library is based on the paper "LLM Post-Training: A Deep Dive into Reasoning Large Language Models" and aims to help researchers and developers better understand and apply LLM post-training technology. This resource library is free and open and suitable for academic research and industrial applications.
Main Features
How to Use
Target Users
This resource library is suitable for scholars and developers engaged in natural language processing and artificial intelligence research, as well as professionals interested in post-training of large language models. It provides researchers with a wealth of research papers and code implementations to help them quickly understand and apply the latest post-training technology; it provides developers with practical frameworks and tools to quickly implement and optimize the inference capabilities of LLM in actual projects.
Examples
Researchers can use the papers and code in this repository to quickly start research on post-LLM training.
Developers can use the framework and tools to apply post-training technology to actual natural language processing projects to improve model performance.
Students can learn the basic concepts and techniques of post-LLM training by reading tutorials and guides, laying a foundation for future research and development.
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