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Awesome LLM Post Training
Overview :
Awesome-LLM-Post-training is a repository focusing on large language model (LLM) post-training methods. It provides in-depth research on LLM post-training, including tutorials, surveys, and guides. This repository 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 techniques. This repository is freely available and suitable for both academic research and industrial applications.
Target Users :
This repository is suitable for scholars, developers, and professionals engaged in natural language processing, artificial intelligence research, and those interested in large language model post-training. It provides researchers with abundant research papers and code implementations, helping them quickly understand and apply the latest post-training techniques; it provides developers with practical frameworks and tools to facilitate the rapid implementation and optimization of LLM inference capabilities in real-world projects.
Use Cases
Researchers can use the papers and code in this repository to quickly conduct research on LLM post-training.
Developers can use the frameworks and tools to apply post-training techniques to real-world natural language processing projects to improve model performance.
Students can learn the basic concepts and techniques of LLM post-training by reading tutorials and guides, laying the foundation for future research and development.
Features
Provides the latest research papers and resources on LLM post-training.
Includes detailed surveys and tutorials to help users get started quickly.
Provides code implementations and frameworks for various LLM post-training methods.
Supports experiments with various language models and post-training techniques.
Provides rich benchmark tests and application scenarios to verify post-training effects.
Supports community contributions; users can submit their own research and code.
Provides detailed documentation and tutorials to help beginners get started quickly.
How to Use
1. Visit the project homepage and browse the README file for a project overview.
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2. Select relevant papers, code, or tutorial resources based on your needs.
3. If you need to use the code, clone the repository locally and install and configure it according to the instructions in the documentation.
4. Use the provided frameworks and tools to conduct experiments and verify post-training effects.
5. If you have new research results or code, you can submit a Pull Request to contribute to the project.
6. Participate in community discussions and exchange experiences with other researchers and developers.
7. Utilize the provided benchmark tests and application scenarios to evaluate and optimize your own post-training methods.
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