awesome-LLM-resourses

awesome-LLM-resourses is a platform that summarizes global large language model (LLM) resources, providing a series of resources and tools from data acquisition, fine-tuning, reasoning, evaluation to practical applications. Its importance lies in providing researchers and developers with a comprehensive resource library so that they can develop and optimize their own language models more efficiently. The platform is maintained by Wang Rongsheng and is continuously updated, providing strong support for the development of the LLM field.

Demand population:

"The target audience is researchers in the field of natural language processing, machine learning engineers, data scientists, and developers interested in large language models. These resources can help them quickly obtain the data they need, select the right fine-tuning framework, improve the inference efficiency of the model, accurately evaluate the model's performance, and ultimately apply the model to real-world problems."

Example of usage scenarios:

Researchers use AutoLabel tools to clean and enrich text datasets

Developers use the LLaMA-Factory framework to fine-tune the model to suit specific tasks

Enterprises evaluate the performance of different language models through the CompassArena platform, select the most suitable model to deploy to the product

Product Features:

Provide large-scale data acquisition and processing methods, such as AutoLabel, LabelLLM and other tools

A variety of fine-tuning frameworks and libraries are summarized, such as LLaMA-Factory, unsloth, etc.

Contains a variety of inference engines and libraries, such as ollama, Open WebUI, etc.

Provide tools and platforms for evaluating the performance of language models, such as lm-evaluation-harness, opencompass, etc.

Summary of practical application cases and experience platforms, such as LMSYS Chatbot Arena, CompassArena, etc.

Provide resources and tools related to RAG (Retrieval-Augmented Generation), such as AnythingLLM, MaxKB, etc.

A summary of LLM-based agent and proxy frameworks, such as AutoGen, CrewAI, etc.

Provide LLM tools and platforms related to search and information retrieval, such as OpenSearch GPT, MindSearch, etc.

Tutorials for use:

1. Visit the awesome-LLM-resourses website to browse different resources and tools

2. Select the corresponding data acquisition, fine-tuning, reasoning or evaluation tool according to your needs

3. Click on the tool link of interest to view detailed introduction and instructions for use

4. If you need to fine-tune the model, select the appropriate fine-tune framework and follow the guide

5. Deploy the model using the inference engine and adjust the parameters as needed to optimize performance

6. Use evaluation tools to test the model's performance to ensure that the model achieves the expected results

7. Apply the model to practical problems, such as chatbots, text classification, etc.

8. Share usage experience and improvement suggestions through communities and forums to jointly promote the development of LLM technology