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🧑‍🚀 全世界最好的中文LLM资料总结
https://github.com/WangRongsheng/awesome-LLM-resourses
List: awesome-LLM-resourses
awesome-list book course large-language-models llama llm rag retrieval-augmented-generation webui
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🧑‍🚀 全世界最好的中文LLM资料总结
Host: GitHub
URL: https://github.com/WangRongsheng/awesome-LLM-resourses
Owner: WangRongsheng
Created: 2024-04-19T03:31:13.000Z (7 months ago)
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Last Pushed: 2024-04-22T11:38:38.000Z (7 months ago)
Last Synced: 2024-04-22T12:54:21.645Z (7 months ago)
Topics: awesome-list, book, course, large-language-models, llama, llm, rag, retrieval-augmented-generation, webui
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README

        ![](./assets/logo2.png)

全世界最好的中文大语言模型资源汇总 持续更新




  

  

  

  

 
   

## 数据

1. [AotoLabel](https://github.com/refuel-ai/autolabel): Label, clean and enrich text datasets with LLMs.

2. [LabelLLM](https://github.com/opendatalab/LabelLLM): The Open-Source Data Annotation Platform.

3. [data-juicer](https://github.com/modelscope/data-juicer): A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs!

4. [OmniParser](https://github.com/jf-tech/omniparser): a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.

5. [MinerU](https://github.com/opendatalab/MinerU): MinerU is a one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.

6. [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit): A Comprehensive Toolkit for High-Quality PDF Content Extraction.

## 微调

1. [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory): Unify Efficient Fine-Tuning of 100+ LLMs.

2. [unsloth](https://github.com/unslothai/unsloth): 2-5X faster 80% less memory LLM finetuning.

3. [TRL](https://huggingface.co/docs/trl/index): Transformer Reinforcement Learning.

4. [Firefly](https://github.com/yangjianxin1/Firefly): Firefly: 大模型训练工具，支持训练数十种大模型

5. [Xtuner](https://github.com/InternLM/xtuner): An efficient, flexible and full-featured toolkit for fine-tuning large models.

6. [torchtune](https://github.com/pytorch/torchtune): A Native-PyTorch Library for LLM Fine-tuning.

7. [Swift](https://github.com/modelscope/swift): Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs.

8. [AutoTrain](https://huggingface.co/autotrain): A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models.

9. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO).

10. [Ludwig](https://github.com/ludwig-ai/ludwig): Low-code framework for building custom LLMs, neural networks, and other AI models.

11. [mistral-finetune](https://github.com/mistralai/mistral-finetune): A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models.

12. [aikit](https://github.com/sozercan/aikit): Fine-tune, build, and deploy open-source LLMs easily!

13. [H2O-LLMStudio](https://github.com/h2oai/h2o-llmstudio): H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs.

14. [LitGPT](https://github.com/Lightning-AI/litgpt): Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.

15. [LLMBox](https://github.com/RUCAIBox/LLMBox): A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.

16. [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP): Easy-to-use and powerful NLP and LLM library.

17. [workbench-llamafactory](https://github.com/NVIDIA/workbench-llamafactory): This is an NVIDIA AI Workbench example project that demonstrates an end-to-end model development workflow using Llamafactory.

18. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral).

19. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory): A Framework of Small-scale Large Multimodal Models.

20. [LLM-Foundry](https://github.com/mosaicml/llm-foundry): LLM training code for Databricks foundation models.

21. [lmms-finetune](https://github.com/zjysteven/lmms-finetune): A unified codebase for finetuning (full, lora) large multimodal models, supporting llava-1.5, qwen-vl, llava-interleave, llava-next-video, phi3-v etc.

## 推理

1. [ollama](https://github.com/ollama/ollama): Get up and running with Llama 3, Mistral, Gemma, and other large language models.

2. [Open WebUI](https://github.com/open-webui/open-webui): User-friendly WebUI for LLMs (Formerly Ollama WebUI).

3. [Text Generation WebUI](https://github.com/oobabooga/text-generation-webui): A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

4. [Xinference](https://github.com/xorbitsai/inference): A powerful and versatile library designed to serve language, speech recognition, and multimodal models.

5. [LangChain](https://github.com/langchain-ai/langchain): Build context-aware reasoning applications.

6. [LlamaIndex](https://github.com/run-llama/llama_index): A data framework for your LLM applications.

7. [lobe-chat](https://github.com/lobehub/lobe-chat): an open-source, modern-design LLMs/AI chat framework. Supports Multi AI Providers, Multi-Modals (Vision/TTS) and plugin system.

8. [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM): TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.

9. [vllm](https://github.com/vllm-project/vllm): A high-throughput and memory-efficient inference and serving engine for LLMs.

10. [LlamaChat](https://github.com/alexrozanski/LlamaChat): Chat with your favourite LLaMA models in a native macOS app.

11. [NVIDIA ChatRTX](https://www.nvidia.com/en-us/ai-on-rtx/chatrtx/): ChatRTX is a demo app that lets you personalize a GPT large language model (LLM) connected to your own content—docs, notes, or other data.

12. [LM Studio](https://lmstudio.ai/): Discover, download, and run local LLMs.

13. [chat-with-mlx](https://github.com/qnguyen3/chat-with-mlx): Chat with your data natively on Apple Silicon using MLX Framework.

14. [LLM Pricing](https://llmpricecheck.com/): Quickly Find the Perfect Large Language Models (LLM) API for Your Budget! Use Our Free Tool for Instant Access to the Latest Prices from Top Providers.

15. [Open Interpreter](https://github.com/OpenInterpreter/open-interpreter): A natural language interface for computers.

16. [Chat-ollama](https://github.com/sugarforever/chat-ollama): An open source chatbot based on LLMs. It supports a wide range of language models, and knowledge base management.

17. [chat-ui](https://github.com/huggingface/chat-ui): Open source codebase powering the HuggingChat app.

18. [MemGPT](https://github.com/cpacker/MemGPT): Create LLM agents with long-term memory and custom tools.

19. [koboldcpp](https://github.com/LostRuins/koboldcpp): A simple one-file way to run various GGML and GGUF models with KoboldAI's UI.

20. [LLMFarm](https://github.com/guinmoon/LLMFarm): llama and other large language models on iOS and MacOS offline using GGML library.

21. [enchanted](https://github.com/AugustDev/enchanted): Enchanted is iOS and macOS app for chatting with private self hosted language models such as Llama2, Mistral or Vicuna using Ollama.

22. [Flowise](https://github.com/FlowiseAI/Flowise): Drag & drop UI to build your customized LLM flow.

23. [Jan](https://github.com/janhq/jan): Jan is an open source alternative to ChatGPT that runs 100% offline on your computer. Multiple engine support (llama.cpp, TensorRT-LLM).

24. [LMDeploy](https://github.com/InternLM/lmdeploy): LMDeploy is a toolkit for compressing, deploying, and serving LLMs.

25. [RouteLLM](https://github.com/lm-sys/RouteLLM): A framework for serving and evaluating LLM routers - save LLM costs without compromising quality!

26. [MInference](https://github.com/microsoft/MInference): About

To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.

27. [Mem0](https://github.com/mem0ai/mem0): The memory layer for Personalized AI.

28. [SGLang](https://github.com/sgl-project/sglang): SGLang is yet another fast serving framework for large language models and vision language models.

## 评估

1. [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness): A framework for few-shot evaluation of language models.

2. [opencompass](https://github.com/open-compass/opencompass): OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

3. [llm-comparator](https://github.com/PAIR-code/llm-comparator): LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed.

## 体验

1. [LMSYS Chatbot Arena: Benchmarking LLMs in the Wild](https://arena.lmsys.org/)

2. [CompassArena 司南大模型竞技场](https://modelscope.cn/studios/opencompass/CompassArena/summary)

3. [琅琊榜](https://langyb.com/)

4. [Huggingface Spaces](https://huggingface.co/spaces)

5. [WiseModel Spaces](https://wisemodel.cn/spaces)

6. [Poe](https://poe.com/)

7. [林哥的大模型野榜](https://lyihub.com/)

## RAG

1. [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm): The all-in-one AI app for any LLM with full RAG and AI Agent capabilites.

2. [MaxKB](https://github.com/1Panel-dev/MaxKB): 基于 LLM 大语言模型的知识库问答系统。开箱即用，支持快速嵌入到第三方业务系统

3. [RAGFlow](https://github.com/infiniflow/ragflow): An open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

4. [Dify](https://github.com/langgenius/dify): An open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.

5. [FastGPT](https://github.com/labring/FastGPT): A knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization.

6. [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat): 基于 Langchain 与 ChatGLM 等不同大语言模型的本地知识库问答

7. [QAnything](https://github.com/netease-youdao/QAnything): Question and Answer based on Anything.

8. [Quivr](https://github.com/QuivrHQ/quivr): A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.

9. [RAG-GPT](https://github.com/open-kf/rag-gpt): RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.

10. [Verba](https://github.com/weaviate/Verba): Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.

11. [FlashRAG](https://github.com/RUC-NLPIR/FlashRAG): A Python Toolkit for Efficient RAG Research.

12. [GraphRAG](https://github.com/microsoft/graphrag): A modular graph-based Retrieval-Augmented Generation (RAG) system.

13. [LightRAG](https://github.com/SylphAI-Inc/LightRAG): LightRAG helps developers with both building and optimizing Retriever-Agent-Generator pipelines.

14. [GraphRAG-Ollama-UI](https://github.com/severian42/GraphRAG-Ollama-UI): GraphRAG using Ollama with Gradio UI and Extra Features.

15. [MindSearch](https://github.com/InternLM/MindSearch): An LLM-based Multi-agent Framework of Web Search Engine similar to Perplexity.ai Pro and SearchGPT.

## Agents

1. [AutoGen](https://github.com/microsoft/autogen): AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. [AutoGen AIStudio](https://autogen-studio.com/)

2. [CrewAI](https://github.com/joaomdmoura/crewAI): Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

3. [Coze](https://www.coze.com/)

4. [AgentGPT](https://github.com/reworkd/AgentGPT): Assemble, configure, and deploy autonomous AI Agents in your browser.

5. [XAgent](https://github.com/OpenBMB/XAgent): An Autonomous LLM Agent for Complex Task Solving.

6. [MobileAgent](https://github.com/X-PLUG/MobileAgent): The Powerful Mobile Device Operation Assistant Family.

7. [Lagent](https://github.com/InternLM/lagent): A lightweight framework for building LLM-based agents.

8. [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent): Agent framework and applications built upon Qwen2, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.

9. [LinkAI](https://link-ai.tech/portal): 一站式 AI 智能体搭建平台

10. [Baidu APPBuilder](https://appbuilder.cloud.baidu.com/)

11. [agentUniverse](https://github.com/alipay/agentUniverse): agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications. Furthermore, through the community, they can exchange and share practices of patterns across different domains.

12. [LazyLLM](https://github.com/LazyAGI/LazyLLM): 低代码构建多Agent大模型应用的开发工具

13. [AgentScope](https://github.com/modelscope/agentscope): Start building LLM-empowered multi-agent applications in an easier way.

14. [MoA](https://github.com/togethercomputer/MoA): Mixture of Agents (MoA) is a novel approach that leverages the collective strengths of multiple LLMs to enhance performance, achieving state-of-the-art results.

15. [Agently](https://github.com/Maplemx/Agently): AI Agent Application Development Framework.

16. [OmAgent](https://github.com/om-ai-lab/OmAgent): A multimodal agent framework for solving complex tasks.

17. [Tribe](https://github.com/StreetLamb/tribe): No code tool to rapidly build and coordinate multi-agent teams.

18. [CAMEL](https://github.com/camel-ai/camel): Finding the Scaling Law of Agents. A multi-agent framework.

19. [PraisonAI](https://github.com/MervinPraison/PraisonAI/): PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.

20. [IoA](https://github.com/openbmb/ioa): An open-source framework for collaborative AI agents, enabling diverse, distributed agents to team up and tackle complex tasks through internet-like connectivity.

21. [llama-agentic-system ](https://github.com/meta-llama/llama-agentic-system): Agentic components of the Llama Stack APIs.

22. [Agent Zero](https://github.com/frdel/agent-zero): Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it.

23. [Agents](https://github.com/aiwaves-cn/agents): An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents.

24. [AgentScope](https://github.com/modelscope/agentscope): Start building LLM-empowered multi-agent applications in an easier way.

## 搜索

1. [OpenSearch GPT](https://github.com/supermemoryai/opensearch-ai): SearchGPT / Perplexity clone, but personalised for you.

2. [MindSearch](https://github.com/InternLM/MindSearch): An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT).

3. [nanoPerplexityAI](https://github.com/Yusuke710/nanoPerplexityAI): The simplest open-source implementation of perplexity.ai.

## 书籍

1. [《大规模语言模型：从理论到实践》](https://intro-llm.github.io/)

2. [《大语言模型》](https://llmbook-zh.github.io/)

3. [《动手学大模型Dive into LLMs》](https://github.com/Lordog/dive-into-llms)

4. [《动手做AI Agent》](https://book.douban.com/subject/36884058/)

5. [《Build a Large Language Model (From Scratch)》](https://github.com/rasbt/LLMs-from-scratch)

6. [《多模态大模型》](https://github.com/HCPLab-SYSU/Book-of-MLM)

7. [《Generative AI Handbook: A Roadmap for Learning Resources》](https://genai-handbook.github.io/)

8. [《Understanding Deep Learning》](https://udlbook.github.io/udlbook/)

## 课程

1. [斯坦福 CS224N: Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)

2. [吴恩达: Generative AI for Everyone](https://www.deeplearning.ai/courses/generative-ai-for-everyone/)

3. [吴恩达: LLM series of courses](https://learn.deeplearning.ai/)

4. [ACL 2023 Tutorial: Retrieval-based Language Models and Applications](https://acl2023-retrieval-lm.github.io/)

5. [llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.](https://github.com/mlabonne/llm-course)

6. [微软: Generative AI for Beginners](https://github.com/microsoft/generative-ai-for-beginners)

7. [微软: State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A)

8. [HuggingFace NLP Course](https://huggingface.co/learn/nlp-course/chapter1/1)

9. [清华 NLP 刘知远团队大模型公开课](https://www.bilibili.com/video/BV1UG411p7zv/?vd_source=c739db1ebdd361d47af5a0b8497417db)

10. [斯坦福 CS25: Transformers United V4](https://web.stanford.edu/class/cs25/)

11. [斯坦福 CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/)

12. [普林斯顿 COS 597G (Fall 2022): Understanding Large Language Models](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/)

13. [约翰霍普金斯 CS 601.471/671 NLP: Self-supervised Models](https://self-supervised.cs.jhu.edu/sp2023/index.html)

14. [李宏毅 GenAI课程](https://www.youtube.com/watch?v=yiY4nPOzJEg&list=PLJV_el3uVTsOePyfmkfivYZ7Rqr2nMk3W)

15. [openai-cookbook](https://github.com/openai/openai-cookbook): Examples and guides for using the OpenAI API.

16. [Hands on llms](https://github.com/iusztinpaul/hands-on-llms): Learn about LLM, LLMOps, and vector DBS for free by designing, training, and deploying a real-time financial advisor LLM system.

17. [滑铁卢大学 CS 886: Recent Advances on Foundation Models](https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/)

18. [Mistral: Getting Started with Mistral](https://www.deeplearning.ai/short-courses/getting-started-with-mistral/)

19. [斯坦福 CS25: Transformers United V4](https://web.stanford.edu/class/cs25/)

20. [Coursera: Chatgpt 应用提示工程](https://www.coursera.org/learn/prompt-engineering)

21. [LangGPT](https://github.com/langgptai/LangGPT): Empowering everyone to become a prompt expert!

22. [mistralai-cookbook](https://github.com/mistralai/cookbook)

23. [Introduction to Generative AI 2024 Spring](https://speech.ee.ntu.edu.tw/~hylee/genai/2024-spring.php)

24. [build nanoGPT](https://github.com/karpathy/build-nanogpt): Video+code lecture on building nanoGPT from scratch.

25. [LLM101n](https://github.com/karpathy/LLM101n): Let's build a Storyteller.

26. [Knowledge Graphs for RAG](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/)

27. [LLMs From Scratch (Datawhale Version)](https://github.com/datawhalechina/llms-from-scratch-cn)

28. [OpenRAG](https://openrag.notion.site/Open-RAG-c41b2a4dcdea4527a7c1cd998e763595)

29. [通往AGI之路](https://waytoagi.feishu.cn/wiki/QPe5w5g7UisbEkkow8XcDmOpn8e)

## 教程

1. [动手学大模型应用开发](https://datawhalechina.github.io/llm-universe/#/)

2. [AI开发者频道](https://techdiylife.github.io/blog/blog_list.html)

3. [B站：五里墩茶社](https://space.bilibili.com/615957867/?spm_id_from=333.999.0.0)

4. [B站：木羽Cheney](https://space.bilibili.com/3537113897241540/?spm_id_from=333.999.0.0)

5. [YTB：AI Anytime](https://www.youtube.com/channel/UC-zVytOQB62OwMhKRi0TDvg)

6. [B站：漆妮妮](https://space.bilibili.com/1262370256/?spm_id_from=333.999.0.0)

7. [Prompt Engineering Guide](https://www.promptingguide.ai/)

8. [YTB: AI超元域](https://www.youtube.com/@AIsuperdomain)

9. [B站：TechBeat人工智能社区](https://space.bilibili.com/209732435)

10. [B站：黄益贺](https://space.bilibili.com/322961825)

11. [B站：深度学习自然语言处理](https://space.bilibili.com/507524288)

## Tips

1. [What We Learned from a Year of Building with LLMs (Part I)](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/)

2. [What We Learned from a Year of Building with LLMs (Part II)](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/)

3. [What We Learned from a Year of Building with LLMs (Part III): Strategy](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/)

4. [轻松入门大语言模型（LLM）](https://www.bilibili.com/video/BV1pF4m1V7FB/?spm_id_from=333.999.0.0&vd_source=c739db1ebdd361d47af5a0b8497417db)

[![Forkers repo roster for @WangRongsheng/awesome-LLM-resourses](https://reporoster.com/forks/WangRongsheng/awesome-LLM-resourses)](https://github.com/WangRongsheng/awesome-LLM-resourses/network/members)

[![Stargazers repo roster for @WangRongsheng/awesome-LLM-resourses](https://reporoster.com/stars/WangRongsheng/awesome-LLM-resourses)](https://github.com/WangRongsheng/awesome-LLM-resourses/stargazers)