https://github.com/WangRongsheng/awesome-LLM-resourses

🧑‍🚀 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 (3 months ago)
• Default Branch: main
• Last Pushed: 2024-04-22T11:38:38.000Z (3 months ago)
• Last Synced: 2024-04-22T12:54:21.645Z (3 months ago)
• Topics: awesome-list, book, course, large-language-models, llama, llm, rag, retrieval-augmented-generation, webui
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  • Readme: README.md

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• ultimate-awesome - awesome-LLM-resourses - 🧑‍🚀 全世界最好的中文LLM资料总结. (Programming Language Lists / Python Lists)

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全世界最好的中文大语言模型资源汇总 持续更新




  

  

  

  

 
   


## 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.

## 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.

## 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.

## 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.

## 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)

## 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/)

## 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/)

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