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Awesome LLM Resources
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Awesome LLM Resources
AI tools website directoryAI models#Large Language Models#Resource Aggregation#Natural Language Processing#Machine Learning#Data ScienceStandard PicksPaid
Overview :
awesome-LLM-resources is a platform that aggregates global resources for large language models (LLMs), offering a range of tools and resources from data acquisition and fine-tuning to inference, evaluation, and real-world applications. Its significance lies in providing researchers and developers with a comprehensive resource library to facilitate the efficient development and optimization of their language models. Maintained by Wang Rongsheng, the platform is continuously updated, providing robust support for the advancement of the LLM field.
Target Users :
The target audience includes researchers in natural language processing, machine learning engineers, data scientists, and developers interested in large language models. These resources can assist them in quickly acquiring necessary data, selecting appropriate fine-tuning frameworks, enhancing model inference efficiency, accurately evaluating model performance, and ultimately applying models to real-world problems.
Total Visits： 628
Top Region： US(100.00%)
Website Views ： 57.4K
Use Cases
Researchers clean and enrich text datasets using the AutoLabel tool.
Developers utilize the LLaMA-Factory framework to fine-tune models for specific tasks.
Businesses evaluate the performance of different language models through the CompassArena platform, selecting the most suitable model for deployment in their products.
Features
Offers methods for large-scale data acquisition and processing, such as tools like AutoLabel and LabelLLM.
Aggregates various fine-tuning frameworks and libraries, including LLaMA-Factory and unsloth.
Includes multiple inference engines and libraries, such as ollama and Open WebUI.
Provides tools and platforms for evaluating language model performance, such as lm-evaluation-harness and opencompass.
Summarizes practical application cases and experience platforms, like LMSYS Chatbot Arena and CompassArena.
Offers resources and tools related to RAG (Retrieval-Augmented Generation), such as AnythingLLM and MaxKB.
Aggregates agent and proxy frameworks based on LLM, such as AutoGen and CrewAI.
Provides LLM tools and platforms related to search and information retrieval, such as OpenSearch GPT and MindSearch.
How to Use
1. Visit the awesome-LLM-resources website to browse various resources and tools.
2. Select the appropriate data acquisition, fine-tuning, inference, or evaluation tools based on your needs.
3. Click on the links of the tools you're interested in to view detailed descriptions and usage instructions.
4. If fine-tuning a model, choose a suitable fine-tuning framework and follow the provided guidelines.
5. Use inference engines to deploy the model, adjusting parameters as needed to optimize performance.
6. Utilize evaluation tools to test model performance, ensuring the model meets expected outcomes.
7. Apply the model to real-world problems, such as chatbots, text classification, etc.
8. Share your experiences and improvement suggestions through community forums to collaboratively advance LLM technology.
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