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<p align="center">全世界最好的大语言模型资源汇总 持续更新</p>

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> [!TIP]
> 如果您对**医疗数据集/大模型/多模态/评估相关资源感兴趣**！请访问我们的 🤗 [Awesome-AI4Med](https://github.com/FreedomIntelligence/Awesome-AI4Med) !

---

#### Contents

- [推荐 Suggestion](#推荐-Suggestion) 🌟
- [数据 Data](#数据-Data)
- [微调 Fine-Tuning](#微调-Fine-Tuning) 🌟
- [Agentic RL](#Agentic-RL) 🌟
- [推理 Inference](#推理-Inference)
- [评估 Evaluation](#评估-Evaluation)
- [体验 Usage](#体验-Usage)
- [知识库 RAG](#知识库-RAG)
- [智能体 Agents](#智能体-Agents)
- [研究 Research](#研究-Research) 🔥
- [代码 Coding](#代码-Coding)
- [视频 Video](#视频-Video) 🌟
- [图片 Image](#图片-Image) 🌟
- [搜索 Search](#搜索-Search)
- [语音 Speech](#语音-Speech) 🌟
- [龙虾 OpenClaw](#龙虾-OpenClaw) 🔥
- [统一模型 Unified Model](#统一模型-Unified-Model) 🌟
- [书籍 Book](#书籍-Book)
- [课程 Course](#课程-Course)
- [教程 Tutorial](#教程-Tutorial)
- [论文 Paper](#论文-Paper)
- [社区 Community](#社区-Community)
- [模型上下文协议 MCP](#模型上下文协议-MCP)
- [技能 Skills](#技能-Skills) 🔥
- [推理 Open o1](#推理-Open-o1)
- [推理 Open o3](#推理-Open-o3)
- [小语言模型 Small Language Model](#小语言模型-Small-Language-Model) 🌟
- [小多模态模型 Small Vision Language Model](#小多模态模型-Small-Vision-Language-Model) 🌟
- [技巧 Tips](#技巧-tips)

![](https://camo.githubusercontent.com/2722992d519a722218f896d5f5231d49f337aaff4514e78bd59ac935334e916a/68747470733a2f2f692e696d6775722e636f6d2f77617856496d762e706e67)

## 推荐 Suggestion

#### Podcast

- [谷歌AI的14年、Gemini翻身之战，与视觉理解模型：专访DeepMind前核心科学家Andrew Dai｜Neolabs特辑](https://www.youtube.com/watch?v=cqW_VWYbIcU)
- [140. 对姚顺宇的4小时访谈：请允许我小疯一下！在Anthropic和Gemini训模型、技术预测、英雄主义已过去](https://www.youtube.com/watch?v=Gk_KUg3qED0)
- [张驰: A Year Inside ByteDance's AI Lab](https://changche.substack.com/p/a-year-inside-bytedances-ai-lab)
- [Luo Fuli: OpenClaw, Agent Frameworks — The AI Paradigm Has Already Changed Dramatically!](https://www.youtube.com/watch?v=V9eI-t3TApE)
- [A 7-hour marathon interview with Saining Xie: World Models, AMI Labs, Yann LeCun, Fei-Fei Li, and 42](https://www.youtube.com/watch?v=rIwgZWzUKm8)
- [翁家翌：OpenAI，GPT，强化学习，Infra，后训练，天授，tuixue，开源，CMU，清华｜WhynotTV Podcast](https://www.bilibili.com/video/BV1darmBcE4A?vd_source=c739db1ebdd361d47af5a0b8497417db)
- [Lovart 创始人陈冕×罗永浩！且让我大闹一场，然后悄然离去](https://www.bilibili.com/video/BV14eiQBmEbN/?spm_id_from=333.1387.upload.video_card.click)
- [MiniMax 创始人闫俊杰×罗永浩！大山并非无法翻越](https://www.bilibili.com/video/BV11NmtBzE36/?spm_id_from=333.1387.upload.video_card.click&vd_source=c739db1ebdd361d47af5a0b8497417db)
- [影视飓风TIM×罗永浩！用影像打开世界的梦想家](https://www.bilibili.com/video/BV1B5xkzPEhx/?spm_id_from=333.1387.upload.video_card.click&vd_source=c739db1ebdd361d47af5a0b8497417db)
- [129. 全球大模型第一股的上市访谈，和智谱CEO张鹏聊：敢问路在何方？](https://www.youtube.com/watch?v=9zSMTUUEfmU&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=2)
- [128. Manus决定出售前最后的访谈：啊，这奇幻的2025年漂流啊…](https://www.youtube.com/watch?v=MW-ezf2RhVg&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=3)
- [122. 朱啸虎现实主义故事的第三次连载：人工智能的盛筵与泡泡](https://www.youtube.com/watch?v=wK0-m3rKgZ0&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=9)
- [119. Kimi Linear、Minimax M2？和杨松琳考古算法变种史，并预演未来架构改进方案](https://www.youtube.com/watch?v=858HR43pegk&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=12&t=1070s)
- [118. 对李想的第二次3小时访谈：CEO大模型、MoE、梁文锋、VLA、能量、记忆、对抗人性、亲密关系、人类的智慧](https://www.youtube.com/watch?v=RxXVq7-sJzM&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=13)
- [115. 对OpenAI姚顺雨3小时访谈：6年Agent研究、人与系统、吞噬的边界、既单极又多元的世界](https://www.youtube.com/watch?v=gQgKkUsx5q0&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=16)
- [113. 和杨植麟时隔1年的对话：K2、Agentic LLM、缸中之脑和“站在无限的开端”](https://www.youtube.com/watch?v=ouG6jrkECrc&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=18)

## 数据 Data

> [!NOTE]
> 
> 此处命名为`数据`，但这里并没有提供具体数据集，而是提供了处理获取大规模数据的方法


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.
7. [Parsera](https://github.com/raznem/parsera): Lightweight library for scraping web-sites with LLMs.
8. [Sparrow](https://github.com/katanaml/sparrow): Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images.
9. [Docling](https://github.com/DS4SD/docling): Get your documents ready for gen AI.
10. [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0): OCR Model.
11. [LLM Decontaminator](https://github.com/lm-sys/llm-decontaminator): Rethinking Benchmark and Contamination for Language Models with Rephrased Samples.
12. [DataTrove](https://github.com/huggingface/datatrove): DataTrove is a library to process, filter and deduplicate text data at a very large scale.
13. [llm-swarm](https://github.com/huggingface/llm-swarm/tree/main/examples/textbooks): Generate large synthetic datasets like [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia).
14. [Distilabel](https://github.com/argilla-io/distilabel): Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
15. [Common-Crawl-Pipeline-Creator](https://huggingface.co/spaces/lhoestq/Common-Crawl-Pipeline-Creator): The Common Crawl Pipeline Creator.
16. [Tabled](https://github.com/VikParuchuri/tabled): Detect and extract tables to markdown and csv.
17. [Zerox](https://github.com/getomni-ai/zerox): Zero shot pdf OCR with gpt-4o-mini.
18. [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO): Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception.
19. [TensorZero](https://github.com/tensorzero/tensorzero): make LLMs improve through experience.
20. [Promptwright](https://github.com/StacklokLabs/promptwright): Generate large synthetic data using a local LLM.
21. [pdf-extract-api](https://github.com/CatchTheTornado/pdf-extract-api): Document (PDF) extraction and parse API using state of the art modern OCRs + Ollama supported models.
22. [pdf2htmlEX](https://github.com/pdf2htmlEX/pdf2htmlEX): Convert PDF to HTML without losing text or format.
23. [Extractous](https://github.com/yobix-ai/extractous): Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
24. [MegaParse](https://github.com/QuivrHQ/MegaParse): File Parser optimised for LLM Ingestion with no loss.
25. [MarkItDown](https://github.com/microsoft/markitdown): Python tool for converting files and office documents to Markdown.
26. [datasketch](https://github.com/ekzhu/datasketch): datasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy.
27. [semhash](https://github.com/MinishLab/semhash): lightweight and flexible tool for deduplicating datasets using semantic similarity.
28. [ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2): a 1.5B parameter language model that converts raw HTML into beautifully formatted markdown or JSON.
29. [Bespoke Curator](https://github.com/bespokelabsai/curator): Data Curation for Post-Training & Structured Data Extraction.
30. [LangKit](https://github.com/whylabs/langkit): An open-source toolkit for monitoring Large Language Models (LLMs). Extracts signals from prompts & responses, ensuring safety & security.
31. [Curator](https://github.com/bespokelabsai/curator): Synthetic Data curation for post-training and structured data extraction.
32. [olmOCR](https://github.com/allenai/olmocr): A toolkit for training language models to work with PDF documents in the wild.
33. [Easy Dataset (`🔥`)](https://github.com/ConardLi/easy-dataset): A powerful tool for creating fine-tuning datasets for LLM.
34. [BabelDOC](https://github.com/funstory-ai/BabelDOC): PDF scientific paper translation and bilingual comparison library.
35. [Dolphin](https://github.com/bytedance/Dolphin): Document Image Parsing via Heterogeneous Anchor Prompting.
36. [EasyDistill](https://github.com/modelscope/easydistill): Easy Knowledge Distillation for Large Language Models.
37. [ContextGem](https://github.com/shcherbak-ai/contextgem): a free, open-source LLM framework that makes it radically easier to extract structured data and insights from documents.
38. [OCRFlux](https://github.com/chatdoc-com/OCRFlux): a lightweight yet powerful multimodal toolkit that significantly advances PDF-to-Markdown conversion, excelling in complex layout handling, complicated table parsing and cross-page content merging.
39. [DataFlow](https://github.com/OpenDCAI/DataFlow): Easy Data Preparation with latest LLMs-based Operators and Pipelines.
40. [DatasetLoom (`multimodal`)](https://github.com/599yongyang/DatasetLoom): 一个面向多模态大模型训练的智能数据集构建与评估平台.
41. [Logics-Parsing](https://github.com/alibaba/Logics-Parsing)
42. [DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
43. [PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL)
44. [Chandra](https://github.com/datalab-to/chandra): a highly accurate OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
45. [HunyuanOCR](https://github.com/Tencent-Hunyuan/HunyuanOCR): a leading end-to-end OCR expert VLM powered by Hunyuan's native multimodal architecture.
46. [DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2): Visual Causal Flow.
47. [PaddleOCR-VL-1.5 (`🔥`)](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5): Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.
48. [GLM-OCR](https://huggingface.co/zai-org/GLM-OCR): a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture.

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## 微调 Fine-Tuning

1. [LLaMA-Factory (`🔥`)](https://github.com/hiyouga/LLaMA-Factory): Unify Efficient Fine-Tuning of 100+ LLMs.
2. [360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory): Unify Efficient Fine-Tuning of 100+ LLMs. (add Sequence Parallelism for supporting long context training)
4. [unsloth (`🔥`)](https://github.com/unslothai/unsloth): 2-5X faster 80% less memory LLM finetuning.
5. [TRL](https://huggingface.co/docs/trl/index): Transformer Reinforcement Learning.
6. [Firefly](https://github.com/yangjianxin1/Firefly): Firefly: 大模型训练工具，支持训练数十种大模型
7. [Xtuner](https://github.com/InternLM/xtuner): An efficient, flexible and full-featured toolkit for fine-tuning large models.
8. [torchtune](https://github.com/pytorch/torchtune): A Native-PyTorch Library for LLM Fine-tuning.
9. [Swift](https://github.com/modelscope/swift): Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs.
10. [AutoTrain](https://huggingface.co/autotrain): A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models.
11. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO).
12. [Ludwig](https://github.com/ludwig-ai/ludwig): Low-code framework for building custom LLMs, neural networks, and other AI models.
13. [mistral-finetune](https://github.com/mistralai/mistral-finetune): A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models.
14. [aikit](https://github.com/sozercan/aikit): Fine-tune, build, and deploy open-source LLMs easily!
15. [H2O-LLMStudio](https://github.com/h2oai/h2o-llmstudio): H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs.
16. [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.
17. [LLMBox](https://github.com/RUCAIBox/LLMBox): A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.
18. [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP): Easy-to-use and powerful NLP and LLM library.
19. [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.
20. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral).
21. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory): A Framework of Small-scale Large Multimodal Models.
22. [LLM-Foundry](https://github.com/mosaicml/llm-foundry): LLM training code for Databricks foundation models.
23. [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.
24. [Simplifine](https://github.com/simplifine-llm/Simplifine): Simplifine lets you invoke LLM finetuning with just one line of code using any Hugging Face dataset or model.
25. [Transformer Lab](https://github.com/transformerlab/transformerlab-app): Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
26. [Liger-Kernel](https://github.com/linkedin/Liger-Kernel): Efficient Triton Kernels for LLM Training.
27. [ChatLearn](https://github.com/alibaba/ChatLearn): A flexible and efficient training framework for large-scale alignment.
28. [nanotron](https://github.com/huggingface/nanotron): Minimalistic large language model 3D-parallelism training.
29. [Proxy Tuning](https://github.com/alisawuffles/proxy-tuning): Tuning Language Models by Proxy.
30. [Effective LLM Alignment](https://github.com/VikhrModels/effective_llm_alignment/): Effective LLM Alignment Toolkit.
31. [Autotrain-advanced](https://github.com/huggingface/autotrain-advanced)
32. [Meta Lingua](https://github.com/facebookresearch/lingua): a lean, efficient, and easy-to-hack codebase to research LLMs.
33. [Vision-LLM Alignemnt](https://github.com/NiuTrans/Vision-LLM-Alignment): This repository contains the code for SFT, RLHF, and DPO, designed for vision-based LLMs, including the LLaVA models and the LLaMA-3.2-vision models.
34. [finetune-Qwen2-VL](https://github.com/zhangfaen/finetune-Qwen2-VL): Quick Start for Fine-tuning or continue pre-train Qwen2-VL Model.
35. [Online-RLHF](https://github.com/RLHFlow/Online-RLHF): A recipe for online RLHF and online iterative DPO.
36. [InternEvo](https://github.com/InternLM/InternEvo): an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
37. [veRL (`🔥`)](https://github.com/volcengine/verl): Volcano Engine Reinforcement Learning for LLM.
38. [Axolotl](https://axolotl-ai-cloud.github.io/axolotl/): Axolotl is designed to work with YAML config files that contain everything you need to preprocess a dataset, train or fine-tune a model, run model inference or evaluation, and much more.
39. [Oumi](https://github.com/oumi-ai/oumi): Everything you need to build state-of-the-art foundation models, end-to-end.
40. [Kiln](https://github.com/Kiln-AI/Kiln): The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.
41. [DeepSeek-671B-SFT-Guide](https://github.com/ScienceOne-AI/DeepSeek-671B-SFT-Guide): An open-source solution for full parameter fine-tuning of DeepSeek-V3/R1 671B, including complete code and scripts from training to inference, as well as some practical experiences and conclusions.
42. [MLX-VLM](https://github.com/Blaizzy/mlx-vlm): MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
43. [RL-Factory](https://github.com/Simple-Efficient/RL-Factory): Train your Agent model via our easy and efficient framework.
44. [RM-Gallery](https://github.com/modelscope/RM-Gallery): A One-Stop Reward Model Platform.
45. [ART](https://github.com/OpenPipe/ART): rain multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training.
46. [LMMs-Engine](https://github.com/EvolvingLMMs-Lab/lmms-engine): A simple, any-to-any modality framework for pretraining and finetuning. Lean, flexible, and built for research.
47. [dLLM](https://github.com/ZHZisZZ/dllm): a library that unifies the training and evaluation of diffusion language models, bringing transparency and reproducibility to the entire development pipeline. `diffusion`
48. [Miles](https://github.com/radixark/miles): an enterprise-facing reinforcement learning framework for large-scale MoE post-training and production workloads.
49. [Skills](https://github.com/NVIDIA-NeMo/Skills): a collection of pipelines to improve "skills" of large language models (LLMs).
50. [Twinkle](https://github.com/modelscope/twinkle): a lightweight, client-server training framework engineered with modular, high-cohesion interfaces.
51. [NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel): Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support.
52. [VeOmni](https://github.com/ByteDance-Seed/VeOmni): Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo.

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## Agentic RL

- veRL (`🔥`): https://github.com/volcengine/verl
- AReaL: https://github.com/inclusionAI/AReaL
- slime (`🔥`): https://github.com/THUDM/slime
- Agent Lightning: https://github.com/microsoft/agent-lightning

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## 推理 Inference

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.
29. [AirLLM](https://github.com/lyogavin/airllm): AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning. And you can run 405B Llama3.1 on 8GB vram now.
30. [LLMHub](https://github.com/jmather/llmhub): LLMHub is a lightweight management platform designed to streamline the operation and interaction with various language models (LLMs).
31. [YuanChat](https://github.com/IEIT-Yuan/YuanChat)
32. [LiteLLM (`🔥`)](https://github.com/BerriAI/litellm): Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
33. [GuideLLM](https://github.com/neuralmagic/guidellm): GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs).
34. [LLM-Engines](https://github.com/jdf-prog/LLM-Engines): A unified inference engine for large language models (LLMs) including open-source models (VLLM, SGLang, Together) and commercial models (OpenAI, Mistral, Claude).
35. [OARC](https://github.com/Leoleojames1/ollama_agent_roll_cage): ollama_agent_roll_cage (OARC) is a local python agent fusing ollama llm's with Coqui-TTS speech models, Keras classifiers, Llava vision, Whisper recognition, and more to create a unified chatbot agent for local, custom automation.
36. [g1](https://github.com/bklieger-groq/g1): Using Llama-3.1 70b on Groq to create o1-like reasoning chains.
37. [MemoryScope](https://github.com/modelscope/MemoryScope): MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
38. [OpenLLM](https://github.com/bentoml/OpenLLM): Run any open-source LLMs, such as Llama 3.1, Gemma, as OpenAI compatible API endpoint in the cloud.
39. [Infinity](https://github.com/infiniflow/infinity): The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text.
40. [optillm](https://github.com/codelion/optillm): an OpenAI API compatible optimizing inference proxy which implements several state-of-the-art techniques that can improve the accuracy and performance of LLMs.
41. [LLaMA Box](https://github.com/gpustack/llama-box): LLM inference server implementation based on llama.cpp.
42. [ZhiLight](https://github.com/zhihu/ZhiLight): A highly optimized inference acceleration engine for Llama and its variants.
43. [DashInfer](https://github.com/modelscope/dash-infer): DashInfer is a native LLM inference engine aiming to deliver industry-leading performance atop various hardware architectures.
44. [LocalAI](https://github.com/mudler/LocalAI): The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required.
45. [ktransformers](https://github.com/kvcache-ai/ktransformers): A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations.
46. [SkyPilot](https://github.com/skypilot-org/skypilot): Run AI and batch jobs on any infra (Kubernetes or 14+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
47. [Chitu](https://github.com/thu-pacman/chitu): High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.
48. [TokenSwift](https://github.com/bigai-nlco/TokenSwift): From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation.
49. [Cherry Studio (`🔥`)](https://github.com/CherryHQ/cherry-studio): a desktop client that supports for multiple LLM providers, available on Windows, Mac and Linux.
50. [Shimmy](https://github.com/Michael-A-Kuykendall/shimmy): Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary.
51. [LlamaBarn](https://github.com/ggml-org/LlamaBarn): Run local LLMs on your Mac with a simple menu bar app.
52. [Parallax](https://github.com/GradientHQ/parallax): a distributed model serving framework that lets you build your own AI cluster anywhere.
53. [xLLM](https://github.com/jd-opensource/xllm): A high-performance inference engine for LLMs, optimized for diverse AI accelerators.


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## 评估 Evaluation

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.
4. [EvalScope (`🔥`)](https://github.com/modelscope/evalscope)
5. [Weave](https://weave-docs.wandb.ai/guides/core-types/evaluations): A lightweight toolkit for tracking and evaluating LLM applications.
6. [MixEval](https://github.com/Psycoy/MixEval/): Deriving Wisdom of the Crowd from LLM Benchmark Mixtures.
7. [Evaluation guidebook](https://github.com/huggingface/evaluation-guidebook): If you've ever wondered how to make sure an LLM performs well on your specific task, this guide is for you!
8. [Ollama Benchmark](https://github.com/aidatatools/ollama-benchmark): LLM Benchmark for Throughput via Ollama (Local LLMs).
9. [VLMEvalKit](https://github.com/open-compass/VLMEvalKit): Open-source evaluation toolkit of large vision-language models (LVLMs), support ~100 VLMs, 40+ benchmarks.
10. [AGI-Eval](https://agi-eval.cn/mvp/home)
11. [EvalScope](https://github.com/modelscope/evalscope): A streamlined and customizable framework for efficient large model evaluation and performance benchmarking.
12. [DeepEval](https://github.com/confident-ai/deepeval): a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems.
13. [Lighteval](https://github.com/huggingface/lighteval): Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends.
14. [QwQ/eval](https://github.com/QwenLM/QwQ/tree/main/eval): QwQ is the reasoning model series developed by Qwen team, Alibaba Cloud.
15. [Evalchemy](https://github.com/mlfoundations/evalchemy): A unified and easy-to-use toolkit for evaluating post-trained language models.
16. [MathArena](https://github.com/eth-sri/matharena): Evaluation of LLMs on latest math competitions.
17. [YourBench](https://github.com/huggingface/yourbench): A Dynamic Benchmark Generation Framework.
18. [MedEvalKit](https://github.com/alibaba-damo-academy/MedEvalKit): A Unified Medical Evaluation Framework.
19. [OpenJudge](https://github.com/modelscope/OpenJudge): A Unified Framework for Holistic Evaluation and Quality Rewards.

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## 体验 Usage

1. [LM Arena](https://lmarena.ai/zh)
2. [Design Arena](https://www.designarena.ai/)

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## 知识库 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. [nano-GraphRAG](https://github.com/gusye1234/nano-graphrag): A simple, easy-to-hack GraphRAG implementation.
16. [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques): This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
17. [ragas](https://github.com/explodinggradients/ragas): Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.
18. [kotaemon](https://github.com/Cinnamon/kotaemon): An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
19. [RAGapp](https://github.com/ragapp/ragapp): The easiest way to use Agentic RAG in any enterprise.
20. [TurboRAG](https://github.com/MooreThreads/TurboRAG): Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text.
21. [LightRAG](https://github.com/HKUDS/LightRAG): Simple and Fast Retrieval-Augmented Generation.
22. [TEN](https://github.com/TEN-framework/ten_framework): the Next-Gen AI-Agent Framework, the world's first truly real-time multimodal AI agent framework.
23. [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG): RAG AutoML tool for automatically finding an optimal RAG pipeline for your data.
24. [KAG](https://github.com/OpenSPG/KAG): KAG is a knowledge-enhanced generation framework based on OpenSPG engine, which is used to build knowledge-enhanced rigorous decision-making and information retrieval knowledge services.
25. [Fast-GraphRAG](https://github.com/circlemind-ai/fast-graphrag): RAG that intelligently adapts to your use case, data, and queries.
26. [Tiny-GraphRAG](https://github.com/limafang/tiny-graphrag)
27. [DB-GPT GraphRAG](https://github.com/eosphoros-ai/DB-GPT/tree/main/dbgpt/storage/knowledge_graph): DB-GPT GraphRAG integrates both triplet-based knowledge graphs and document structure graphs while leveraging community and document retrieval mechanisms to enhance RAG capabilities, achieving comparable performance while consuming only 50% of the tokens required by Microsoft's GraphRAG. Refer to the DB-GPT [Graph RAG User Manual](http://docs.dbgpt.cn/docs/cookbook/rag/graph_rag_app_develop/) for details.
28. [Chonkie](https://github.com/bhavnicksm/chonkie): The no-nonsense RAG chunking library that's lightweight, lightning-fast, and ready to CHONK your texts.
29. [RAGLite](https://github.com/superlinear-ai/raglite): RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite.
30. [KAG](https://github.com/OpenSPG/KAG): KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs.
31. [CAG](https://github.com/hhhuang/CAG): CAG leverages the extended context windows of modern large language models (LLMs) by preloading all relevant resources into the model’s context and caching its runtime parameters.
32. [MiniRAG](https://github.com/HKUDS/MiniRAG): an extremely simple retrieval-augmented generation framework that enables small models to achieve good RAG performance through heterogeneous graph indexing and lightweight topology-enhanced retrieval.
33. [XRAG](https://github.com/DocAILab/XRAG): a benchmarking framework designed to evaluate the foundational components of advanced Retrieval-Augmented Generation (RAG) systems.
34. [Rankify](https://github.com/DataScienceUIBK/rankify): A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation.
35. [RAG-Anything](https://github.com/HKUDS/RAG-Anything): All-in-One RAG System.

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## 智能体 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. [AgentField](https://github.com/Agent-Field/agentfield): Open-source control plane for building and operating AI agents like APIs at scale, with routing, memory, observability, identity, auth, and policy controls.
15. [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): First LLM multi-agent framework and an open-source community dedicated to finding the scaling law of agents.
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.
25. [FastAgency](https://github.com/airtai/fastagency): The fastest way to bring multi-agent workflows to production.
26. [Swarm](https://github.com/openai/swarm): Framework for building, orchestrating and deploying multi-agent systems. Managed by OpenAI Solutions team. Experimental framework.
27. [Agent-S](https://github.com/simular-ai/Agent-S): an open agentic framework that uses computers like a human.
28. [PydanticAI](https://github.com/pydantic/pydantic-ai): Agent Framework / shim to use Pydantic with LLMs.
29. [Agentarium](https://github.com/Thytu/Agentarium): open-source framework for creating and managing simulations populated with AI-powered agents.
30. [smolagents](https://github.com/huggingface/smolagents): a barebones library for agents. Agents write python code to call tools and orchestrate other agents.
31. [Cooragent](https://github.com/LeapLabTHU/cooragent): Cooragent is an AI agent collaboration community.
32. [Agno](https://github.com/agno-agi/agno): Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
33. [Suna](https://github.com/kortix-ai/suna): Open Source Generalist AI Agent.
34. [rowboat](https://github.com/rowboatlabs/rowboat): Let AI build multi-agent workflows for you in minutes.
35. [EvoAgentX](https://github.com/EvoAgentX/EvoAgentX): Building a Self-Evolving Ecosystem of AI Agents.
36. [ii-agent](https://github.com/Intelligent-Internet/ii-agent): a new open-source framework to build and deploy intelligent agents.
37. [OWL](https://github.com/camel-ai/owl): Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation.
38. [OpenManus](https://github.com/FoundationAgents/OpenManus): No fortress, purely open ground. OpenManus is Coming.
39. [JoyAgent-JDGenie](https://github.com/jd-opensource/joyagent-jdgenie): 业界首个开源高完成度轻量化通用多智能体产品.
40. [coze-studio](https://github.com/coze-dev/coze-studio): An AI agent development platform with all-in-one visual tools, simplifying agent creation, debugging, and deployment like never before.
41. [OxyGent](https://github.com/jd-opensource/OxyGent): An advanced Python framework that empowers developers to quickly build production-ready intelligent systems.
42. [LazyCraft](https://github.com/LazyAGI/LazyCraft): LazyCraft 是一个基于 LazyLLM 构建的 AI Agent 应用开发与管理平台，旨在协助开发者以 低门槛、低成本 快速构建和发布大模型应用。
43. [OpenAgents](https://github.com/openagents-org/openagents): AI Agent Networks for Open Collaboration.
44. [SandBox](https://github.com/agent-infra/sandbox): All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.
45. [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze): First agentic LLM for autonomous data science, supporting specific data tasks (data preparation, analysis, modeling, visualization, and insight) and data-oriented deep research (produce analyst-grade research reports).
46. [Astron Agent](https://github.com/iflytek/astron-agent): Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
47. [Youtu-Agent](https://github.com/TencentCloudADP/youtu-agent): A simple yet powerful agent framework that delivers with open-source models.
48. [MiroThinker](https://github.com/MiroMindAI/MiroThinker): an open-source search agent model, built for tool-augmented reasoning and real-world information seeking, aiming to match the deep research experience of OpenAI Deep Research and Gemini Deep Research.
49. [Nexent](https://github.com/ModelEngine-Group/nexent): A zero-code platform for auto-generating agents — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want.
50. [Yunjue-Agent](https://github.com/YunjueTech/Yunjue-Agent): A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks.
51. [Hindsight](https://github.com/vectorize-io/hindsight): State-of-the-art long-term memory for AI agents by Vectorize. Open-source, self-hostable, with integrations for LangChain, CrewAI, LlamaIndex, MCP, and more.
52. [AgentsMesh](https://github.com/AgentsMesh/AgentsMesh): The AI Agent Workforce Platform. Self-hostable multi-agent orchestration with remote AI workstations (AgentPods), PTY sandbox + git worktree isolation, channels-based agent collaboration, built-in Kanban, and per-pod MCP server. Supports Claude Code, Codex CLI, Gemini CLI, Aider, OpenCode.

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## 研究 Research

#### 写作

- PaperDebugger: https://github.com/PaperDebugger/PaperDebugger
- Chat Overleaf: https://github.com/anuin-cat/chat-overleaf
- 文智云助手: https://overleaf.top/
- LiteWrite: https://litewrite.ai/
- Prism: https://openai.com/zh-Hans-CN/prism/
- claude-prism: https://github.com/delibae/claude-prism

#### 审稿

- PaperReview: https://paperreview.ai/
- aiXiv: https://aixiv.science/
- OpenJudge Review: https://openjudge.me/paper_review

#### PPT

- PPTAgent: https://github.com/icip-cas/PPTAgent
- Paper PPT Agent: https://github.com/CRui5in/paper-ppt-agent
- PPT Master: https://github.com/hugohe3/ppt-master
- LandPPT: https://github.com/sligter/LandPPT
- Kami: https://github.com/tw93/kami
- beautiful-html-templates: https://github.com/zarazhangrui/beautiful-html-templates
- guizang-ppt-skill: https://github.com/op7418/guizang-ppt-skill

#### 其他

- Paper2Video: https://github.com/showlab/Paper2Video
- Paper2Poster: https://github.com/Paper2Poster/Paper2Poster
- AutoPR: https://github.com/irgolic/AutoPR
- Auto-Slides: https://github.com/Westlake-AGI-Lab/Auto-Slides
- EvoPresent: https://github.com/eric-ai-lab/EvoPresent
- Paper2All: https://github.com/YuhangChen1/Paper2All
- AutoPage: https://github.com/AutoLab-SAI-SJTU/AutoPage
- pdf2video: https://github.com/DangJin/pdf2video
- Idea2Paper: https://github.com/AgentAlphaAGI/Idea2Paper
- PaperX: https://github.com/yutao1024/PaperX
- figures4papers: https://github.com/ChenLiu-1996/figures4papers
- PaperBanana: https://github.com/dwzhu-pku/PaperBanana
- PaperBanana-Pro: https://github.com/elpsykongloo/PaperBanana-Pro
- AutoFigure: https://github.com/ResearAI/AutoFigure
  - FigureWeave: https://github.com/Krisocer/FigureWeave
  - EditDeck: https://github.com/Morgensonne/EditDeck
- AutoFigure-Edit: https://github.com/ResearAI/AutoFigure-Edit
- Academic Figure Generator: https://github.com/LigphiDonk/academic-figure-generator
- PaperFit: https://github.com/OpenRaiser/PaperFit

#### 全自动科研

- EvoScientist: https://github.com/EvoScientist/EvoScientist
- Auto-claude-code-research-in-sleep: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
- ArgusBot: https://github.com/waltstephen/ArgusBot
- Station: https://github.com/dualverse-ai/station
- Dr.Claw: https://github.com/OpenLAIR/dr-claw
- Redigg: https://github.com/redigg/redigg
- AutoResearchClaw: https://github.com/aiming-lab/AutoResearchClaw
- NanoResearch: https://github.com/OpenRaiser/NanoResearch
- ScienceClaw: https://github.com/AgentTeam-TaichuAI/ScienceClaw
- EurekaClaw: https://github.com/EurekaClaw/EurekaClaw
- Claude-scholar: https://github.com/Galaxy-Dawn/claude-scholar/
- claude-scientific-skills: https://github.com/K-Dense-AI/claude-scientific-skills
- K-Dense BYOK: https://github.com/K-Dense-AI/k-dense-byok
- latex-paper-skills: https://github.com/yunshenwuchuxun/latex-paper-skills
- NeuriCo: https://github.com/ChicagoHAI/NeuriCo
- AutoResearch : https://github.com/karpathy/autoresearch
- RD-Agent : https://github.com/microsoft/RD-Agent
- DeepScientist : https://github.com/ResearAI/DeepScientist
- Deep Researcher Agent: https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7
- academic-research-skills: https://github.com/Imbad0202/academic-research-skills
- Supervisor-Skills: https://github.com/HKUSTDial/Supervisor-Skills

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## 代码 Coding

1. [Cloi CLI](https://github.com/cloi-ai/cloi): Local debugging agent that runs in your terminal.
2. [Devin](https://devin.ai/)
3. [v0](https://v0.dev/)
4. [Blot.new](https://bolt.new/)
5. [cursor](https://www.cursor.com/)
6. [Windsurf](https://codeium.com/windsurf)
7. [cline](https://github.com/cline/cline)
8. [Trae](https://www.trae.ai/)
9. [MGX](https://mgx.dev/)
10. [Roo Code](https://github.com/RooCodeInc/Roo-Code)
11. [Kilo Code](https://github.com/Kilo-Org/kilocode)
12. [AugmentCode](https://www.augmentcode.com/)
13. [Claude Code (`🔥`)](https://github.com/anthropics/claude-code)
14. [Claude Code (`🔥`)](https://github.com/anthropics/claude-code) + [Happy Coder](https://github.com/slopus/happy) / [CodePilot](https://github.com/op7418/CodePilot) / [cc-connect](https://github.com/chenhg5/cc-connect)
15. [Gemini CLI](https://github.com/google-gemini/gemini-cli)
16. [Serena](https://github.com/oraios/serena)
17. [Claudia](https://github.com/getAsterisk/claudia)
18. [OpenCode](https://github.com/opencode-ai/opencode)
19. [Kiro](https://kiro.dev/)
20. [CodeBuddy](https://copilot.tencent.com/)
21. [Kiro](https://kiro.dev/)
22. [CodeX (`🔥`)](https://github.com/openai/codex)
23. [Kimi-CLI](https://github.com/MoonshotAI/kimi-cli)
24. [opencode](https://github.com/anomalyco/opencode)
25. [Multica](https://github.com/multica-ai/multica)


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## 视频 Video

#### 模型

> [!NOTE]
> 🤝[Awesome-Video-Diffusion](https://github.com/showlab/Awesome-Video-Diffusion)

1. [HunyuanVideo](https://github.com/Tencent/HunyuanVideo)
2. [CogVideo](https://github.com/THUDM/CogVideo)
3. [Wan2.1](https://github.com/Wan-Video/Wan2.1)
4. [Open-Sora](https://github.com/hpcaitech/Open-Sora)
5. [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan)
6. [LTX-Video](https://github.com/Lightricks/LTX-Video)
7. [Step-Video-T2V](https://github.com/stepfun-ai/Step-Video-T2V)
8. [Step1X-Edit](https://github.com/stepfun-ai/Step1X-Edit) `Editing`
9. [Wan2.1-VACE](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) `Editing`
10. [ICEdit](https://github.com/River-Zhang/ICEdit) `Editing`
11. [mochi-1-preview](https://huggingface.co/genmo/mochi-1-preview)
12. [Wan2.1-Fun](https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17)
13. [Wan2.1-FLF2V](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P) `首尾帧`
14. [MAGI-1](https://github.com/SandAI-org/MAGI-1) `自回归模型`
15. [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2)
16. [FramePack](https://github.com/lllyasviel/FramePack)
17. [Pusa-VidGen](https://github.com/Yaofang-Liu/Pusa-VidGen)
18. [Wan2.2](https://github.com/Wan-Video/Wan2.2)
19. [MoGA](https://arxiv.org/pdf/2510.18692) `长视频`
20. [LongCat-Video](https://huggingface.co/meituan-longcat/LongCat-Video)
21. [HunyuanVideo-1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
22. [LTX-2](https://huggingface.co/Lightricks/LTX-2)
    - [Training](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-trainer/README.md)
23. [daVinci-MagiHuman](https://huggingface.co/GAIR/daVinci-MagiHuman)
24. [LongLive](https://github.com/NVlabs/LongLive)

#### 编辑

1. Wan2.1-VACE-14B: https://huggingface.co/Wan-AI/Wan2.1-VACE-14B
2. Ditto: https://github.com/EzioBy/Ditto

#### 训练

- https://github.com/hao-ai-lab/FastVideo
- https://github.com/tdrussell/diffusion-pipe
- https://github.com/VideoVerses/VideoTuna
- (`🔥`) https://github.com/modelscope/DiffSynth-Studio
- https://github.com/huggingface/diffusers
- https://github.com/kohya-ss/musubi-tuner
- https://github.com/spacepxl/HunyuanVideo-Training
- https://github.com/Tele-AI/TeleTron
- https://github.com/Yaofang-Liu/Mochi-Full-Finetuner
- https://github.com/bghira/SimpleTuner
- https://github.com/X-GenGroup/Flow-Factory
- https://github.com/shengshu-ai/minWM `world model`

#### 推理

- https://github.com/ModelTC/LightX2V
- https://github.com/thu-ml/TurboDiffusion

#### 实用工具

- [PySceneDetect](https://github.com/Breakthrough/PySceneDetect): Python and OpenCV-based scene cut/transition detection program & library.
- [DOVER](https://github.com/VQAssessment/DOVER): Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives.

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## 图片 Image

#### 生成

- [awesome-nano-banana](https://github.com/JimmyLv/awesome-nano-banana)
- [Awesome-Nano-Banana-images](https://github.com/PicoTrex/Awesome-Nano-Banana-images)
- HunyuanImage-3.0：https://github.com/Tencent-Hunyuan/HunyuanImage-3.0
- Seedream 4.0：https://arxiv.org/abs/2509.20427
- LongCat-Image：https://huggingface.co/meituan-longcat/LongCat-Image
- Z-Image-Turbo：https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
  - https://huggingface.co/inclusionAI/TwinFlow
- Qwen-Image：https://huggingface.co/Qwen/Qwen-Image
- Qwen-Image-2512：https://huggingface.co/Qwen/Qwen-Image-2512
- Z-Image：https://huggingface.co/Tongyi-MAI/Z-Image
- ERNIE-Image: https://huggingface.co/baidu/ERNIE-Image
  - ERNIE-Image-Turbo: https://huggingface.co/Baidu/ERNIE-Image-Turbo
- Nucleus-Image: https://huggingface.co/NucleusAI/Nucleus-Image
- HiDream-O1-Image: https://huggingface.co/HiDream-ai/HiDream-O1-Image

#### 编辑

- ChronoEdit-14B: https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers
- Eigen-Banana-Qwen-Image-Edit: https://huggingface.co/eigen-ai-labs/eigen-banana-qwen-image-edit
- Qwen-Image-Edit-2509: https://huggingface.co/Qwen/Qwen-Image-Edit-2509
  - Upscale: https://huggingface.co/vafipas663/Qwen-Edit-2509-Upscale-LoRA
  - Multiple-angles: https://huggingface.co/dx8152/Qwen-Edit-2509-Multiple-angles
  - Multi-Angle-Lighting: https://huggingface.co/dx8152/Qwen-Edit-2509-Multi-Angle-Lighting
- LongCat-Image-Edit: https://huggingface.co/meituan-longcat/LongCat-Image-Edit
- Qwen-Image-Edit-2511: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
  - Qwen-Image-Edit-2511-Upscale2K: https://huggingface.co/valiantcat/Qwen-Image-Edit-2511-Upscale2K
  - Qwen-Image-Edit-2511-Multiple-Angles-LoRA: https://huggingface.co/fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA
- FireRed-Image-Edit: https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0
- JoyAI-Image-Edit: https://huggingface.co/jdopensource/JoyAI-Image-Edit

#### 统一

- GLM-Image: https://huggingface.co/zai-org/GLM-Image
- https://huggingface.co/black-forest-labs/FLUX.2-klein-4B
  - https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
- DreamLite: https://github.com/ByteVisionLab/DreamLite
- SenseNova-U1: https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-SFT

#### 训练

- Ostris：https://github.com/ostris/ai-toolkit
- FlymyAI：https://github.com/FlyMyAI/flymyai-lora-trainer
- Nitro-T：https://github.com/AMD-AGI/Nitro-T
- (`🔥`) DiffSynth-Studio：https://github.com/modelscope/DiffSynth-Studio
- Musubi Tuner: https://github.com/kohya-ss/musubi-tuner
- SimpleTuner: https://github.com/bghira/SimpleTuner
- MS Training: https://www.modelscope.cn/aigc/modelTraining
- Finetune HunyuanImage-3.0: https://github.com/PhotonAISG/hunyuan-image3-finetune
- OneTrainer: https://github.com/Nerogar/OneTrainer
- Finetune LongCat-Image and Edit: https://github.com/meituan-longcat/LongCat-Image/tree/main/train_examples
- https://github.com/X-GenGroup/Flow-Factory

#### 评估

- ULMEvalKit：https://github.com/ULMEvalKit/ULMEvalKit

#### 推理

- TypemovieInfer: https://github.com/typemovie/TypemovieInfer

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## 搜索 Search

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.
4. [curiosity](https://github.com/jank/curiosity): Try to build a Perplexity-like user experience.
5. [MiniPerplx](https://github.com/zaidmukaddam/miniperplx): A minimalistic AI-powered search engine that helps you find information on the internet.

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## 语音 Speech

#### TTS

1. SpeechGPT-2.0-preview: https://github.com/OpenMOSS/SpeechGPT-2.0-preview
2. Moss-TTSD：https://github.com/OpenMOSS/MOSS-TTSD
3. Index-TTS：https://github.com/index-tts/index-tts
4. MegaTTS3：https://github.com/bytedance/MegaTTS3
5. F5-TTS：https://github.com/SWivid/F5-TTS
6. GPT-SoVITS：https://github.com/RVC-Boss/GPT-SoVITS
7. CosyVoice：https://github.com/FunAudioLLM/CosyVoice
8. Spark-TTS：https://github.com/SparkAudio/Spark-TTS
9. OpenVoice：https://github.com/myshell-ai/OpenVoice
10. Dia：https://github.com/nari-labs/dia
11. ChatTTS：https://github.com/2noise/ChatTTS
12. Fish Speech：https://github.com/fishaudio/fish-speech
13. Edge-TTS：https://github.com/rany2/edge-tts
14. Bark：https://github.com/suno-ai/bark
15. kokoro: https://github.com/hexgrad/kokoro
16. Higgs Audio V2: https://github.com/boson-ai/higgs-audio 【[Training](https://github.com/JimmyMa99/train-higgs-audio)】
17. KittenTTS: https://github.com/KittenML/KittenTTS
18. ZipVoice: https://github.com/k2-fsa/ZipVoice
19. VyvoTTS: https://github.com/Vyvo-Labs/VyvoTTS
20. VibeVoice: https://github.com/microsoft/VibeVoice
21. Index-TTS-2: https://huggingface.co/IndexTeam/IndexTTS-2
22. FireRedTTS2: https://github.com/FireRedTeam/FireRedTTS2
23. VoxCPM: https://github.com/OpenBMB/VoxCPM/
24. Neutts-Air: https://github.com/neuphonic/neutts-air
25. Maya1: https://huggingface.co/maya-research/maya1
26. VibeVoice: https://huggingface.co/collections/microsoft/vibevoice
27. GLM-TTS: https://github.com/zai-org/GLM-TTS
28. Fun-CosyVoice3: https://huggingface.co/FunAudioLLM/Fun-CosyVoice3-0.5B-2512
29. Qwen3-TTS:https://huggingface.co/collections/Qwen/qwen3-tts
30. Ming-Omni-TTS: https://github.com/inclusionAI/Ming-omni-tts
31. VoxCPM2: https://huggingface.co/openbmb/VoxCPM2
32. OmniVoice: https://github.com/k2-fsa/OmniVoice
33. MOSS-TTS-v1.5: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5

#### STT/ASR

1. Kyutai: https://github.com/kyutai-labs/delayed-streams-modeling
2. Whisper: https://github.com/openai/whisper
3. Audio Flamingo 3: https://huggingface.co/nvidia/audio-flamingo-3
4. Voxtral: https://huggingface.co/mistralai/Voxtral-Mini-3B-2507
5. Step-Audio2: https://github.com/stepfun-ai/Step-Audio2
6. SoulX-Podcast: https://huggingface.co/collections/Soul-AILab/soulx-podcast
7. Omnilingual ASR: https://github.com/facebookresearch/omnilingual-asr
8. Fun-ASR: https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512
9. VibeVoice-ASR: https://huggingface.co/microsoft/VibeVoice-ASR
10. Qwen3-ASR: https://github.com/QwenLM/Qwen3-ASR
11. Mega-ASR: https://github.com/xzf-thu/Mega-ASR

#### Voice Interaction

1. Fun-Audio-Chat: https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B
2. Chroma 1.0: https://huggingface.co/FlashLabs/Chroma-4B

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## 龙虾 OpenClaw

1. MultiUserClaw: https://github.com/johnson7788/MultiUserClaw
2. ClawManager: https://github.com/Yuan-lab-LLM/ClawManager
3. Qclaw: https://github.com/qiuzhi2046/Qclaw
4. NEXU: https://github.com/nexu-io/nexu
5. OpenHanako: https://github.com/liliMozi/openhanako

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## 统一模型 Unified Model

> 现在统一模型已经从`理解+生成`变成`理解+生成+编辑`

- Emu-2：https://arxiv.org/abs/2312.13286
- Emu-3：https://arxiv.org/abs/2409.18869
- Emu-1：https://arxiv.org/abs/2307.05222
- Janus：https://github.com/deepseek-ai/Janus
- Janus-Pro：http://arxiv.org/abs/2508.05954
- show-o：https://arxiv.org/abs/2408.12528
- Any-GPT：https://arxiv.org/abs/2402.12226
- Next-GPT：https://arxiv.org/pdf/2309.05519.pdf
- CoDi：https://arxiv.org/abs/2305.11846
- Seed-X：https://arxiv.org/abs/2404.14396
- Dream-LLM：https://arxiv.org/abs/2309.11499
- Chameleon：https://arxiv.org/abs/2405.09818
- Spider：https://arxiv.org/abs/2411.09439
- MedViLaM：https://arxiv.org/abs/2409.19684
- VITRON：https://github.com/SkyworkAI/Vitron
- TokenFlow：https://github.com/ByteFlow-AI/TokenFlow
- OneDiffusion：https://github.com/lehduong/OneDiffusion
- MetaMorph: https://arxiv.org/abs/2412.14164
- LlamaFusion：https://arxiv.org/abs/2412.15188
- InstructSeg：https://arxiv.org/abs/2412.14006
- VILA-U：https://arxiv.org/abs/2409.04429
- Ullava: https://github.com/OPPOMKLab/u-LLaVA
- ILLUME: https://arxiv.org/abs/2412.06673
- Vitron:https://arxiv.org/abs/2412.19806
- SynerGen-VL：https://arxiv.org/abs/2412.09604
- Align Anything：https://arxiv.org/abs/2412.15838
- Mico：https://arxiv.org/abs/2406.09412
- OneLLM:https://arxiv.org/abs/2312.03700
- X-VILA:https://arxiv.org/abs/2405.19335
- OLA：https://arxiv.org/abs/2502.04328
- Transfusion: https://arxiv.org/abs/2408.11039
- JanusFlow: https://arxiv.org/abs/2411.07975
- HealthGPT：https://arxiv.org/abs/2502.09838 `Medical`
- BAGEL：https://arxiv.org/abs/2505.14683
- Qwen2.5-Omni：https://arxiv.org/abs/2503.20215
- X2I：https://arxiv.org/abs/2503.06134
- Bifrost-1：https://arxiv.org/abs/2508.05954
- OmniGen2：https://arxiv.org/abs/2506.18871
- UniPic：https://github.com/SkyworkAI/UniPic
- VeOmni：https://github.com/ByteDance-Seed/VeOmni `Training`
- NextStep-1：https://arxiv.org/abs/2508.10711
- UniUGG: https://arxiv.org/abs/2508.11952 `3D`
- Omni-Video：https://arxiv.org/abs/2507.06119
- OneCAT：https://arxiv.org/abs/2509.03498
- Lumina-DiMOO：https://github.com/Alpha-VLLM/Lumina-DiMOO
- UAE：https://github.com/PKU-YuanGroup/UAE
- RecA：https://arxiv.org/abs/2509.07295
- UniLM：https://arxiv.org/abs/1905.03197
- Hyper-Bagel：https://arxiv.org/abs/2509.18824
- Ming-UniVision：https://arxiv.org/abs/2510.06590
- EditVerse：https://arxiv.org/abs/2509.20360
- LightBagel: https://arxiv.org/abs/2510.22946
- DreamLLM: https://arxiv.org/abs/2309.11499
- X-Omni: https://arxiv.org/abs/2507.22058
- Ming-flash-omni-Preview: https://huggingface.co/inclusionAI/Ming-flash-omni-Preview
- Omni-View: https://arxiv.org/abs/2511.07222
- NExT-OMNI: https://arxiv.org/abs/2510.13721
- Uni-MoE-2.0-Omni: https://arxiv.org/abs/2511.12609
- LongCat-Flash-Omni: https://huggingface.co/meituan-longcat/LongCat-Flash-Omni
- ShapeLLM-Omni: https://arxiv.org/abs/2506.01853
- UniGen-1.5: https://arxiv.org/abs/2511.14760
- Jodi: https://arxiv.org/abs/2505.19084
- UniModel: https://arxiv.org/abs/2511.16917
- TUNA: https://arxiv.org/abs/2512.02014
- HBridge: https://arxiv.org/abs/2511.20520
- EMMA: https://arxiv.org/abs/2512.04810
- OpenOmni: https://github.com/RainBowLuoCS/OpenOmni
- Ming-Flash-Omni: https://arxiv.org/abs/2510.24821
- STAR: https://github.com/mm-mvr/star
- InternVL-U: https://github.com/OpenGVLab/InternVL-U
- LongCat-Next: https://github.com/meituan-longcat/LongCat-Next
- SenseNova-U1: https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-SFT
- TUNA-2: https://github.com/facebookresearch/tuna-2
- Lance: https://github.com/bytedance/Lance

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## 书籍 Book

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/)
9. [《Illustrated book to learn about Transformers & LLMs》](https://www.reddit.com/r/MachineLearning/comments/1ew1hws/p_illustrated_book_to_learn_about_transformers/)
10. [《Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG》](https://www.amazon.com/Building-LLMs-Production-Reliability-Fine-Tuning/dp/B0D4FFPFW8?crid=7OAXELUKGJE4&dib=eyJ2IjoiMSJ9.Qr3e3VSH8LSo_j1M7sV7GfS01q_W1LDYd2uGlvGJ8CW-t4DTlng6bSeOlZBryhp6HJN5K1HqWMVVgabU2wz2i9yLpy_AuaZN-raAEbenKx2NHtzZA3A4k-N7GpnldF1baCarA_V1CRF-aCdc9_3WSX7SaEzmpyDv22TTyltcKT74HAb2KiQqBGLhQS3cEAnzChcqGa1Xp-XhbMnplVwT7xZLApE3tGLhDOgi5GmSi9w.8SY_4NBEkm68YF4GwhDnz0r81ZB1d8jr-gK9IMJE5AE&dib_tag=se&keywords=building+llms+for+production&qid=1716376414&sprefix=building+llms+for+production,aps,101&sr=8-1&linkCode=sl1&tag=whatsai06-20&linkId=ee102fda07a0eb51710fcdd8b8d20c28&language=en_US&ref_=as_li_ss_tl)
11. [《大型语言模型实战指南：应用实践与场景落地》](https://github.com/liucongg/LLMsBook)
12. [《Hands-On Large Language Models》](https://github.com/handsOnLLM/Hands-On-Large-Language-Models)
13. [《自然语言处理：大模型理论与实践》](https://nlp-book.swufenlp.group/)
14. [《动手学强化学习》](https://hrl.boyuai.com/)
15. [《面向开发者的LLM入门教程》](https://datawhalechina.github.io/llm-cookbook/#/)
16. [《大模型基础》](https://github.com/ZJU-LLMs/Foundations-of-LLMs)
17. [Taming LLMs: A Practical Guide to LLM Pitfalls with Open Source Software ](https://www.tamingllms.com/)
18. [Foundations of Large Language Models](https://arxiv.org/abs/2501.09223)
19. [Textbook on reinforcement learning from human feedback](https://github.com/natolambert/rlhf-book)
20. [《大模型算法：强化学习、微调与对齐》](https://book.douban.com/subject/37331056/)
21. [《The Smol Training Playbook: The Secrets to Building World-Class LLMs》](https://github.com/WangRongsheng/awesome-LLM-resources/blob/main/books/the-smol-training-playbook-the-secrets-to-building-world-class-llms.pdf)
22. [《从零开始构建智能体》——从零开始的智能体原理与实践教程](https://github.com/datawhalechina/hello-agents)
23. [《Hands-On Modern RL》](https://github.com/walkinglabs/hands-on-modern-rl)

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## 课程 Course

> [LLM Resources Hub](https://llmresourceshub.vercel.app/)

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)
30. [Andrej Karpathy - Neural Networks: Zero to Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
31. [Interactive visualization of Transformer](https://poloclub.github.io/transformer-explainer/)
32. [andysingal/llm-course](https://github.com/andysingal/llm-course)
33. [LM-class](https://lm-class.org/lectures)
34. [Google Advanced: Generative AI for Developers Learning Path](https://www.cloudskillsboost.google/paths/183)
35. [Anthropics：Prompt Engineering Interactive Tutorial](https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P)
36. [LLMsBook](https://github.com/liucongg/LLMsBook)
37. [Large Language Model Agents](https://llmagents-learning.org/f24)
38. [Cohere LLM University](https://cohere.com/llmu)
39. [LLMs and Transformers](https://www.ambujtewari.com/LLM-fall2024/)
40. [Smol Vision](https://github.com/merveenoyan/smol-vision): Recipes for shrinking, optimizing, customizing cutting edge vision models.
41. [Multimodal RAG: Chat with Videos](https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/)
42. [LLMs Interview Note](https://github.com/wdndev/llm_interview_note)
43. [RAG++ : From POC to production](https://www.wandb.courses/courses/rag-in-production): Advanced RAG course.
44. [Weights & Biases AI Academy](https://www.wandb.courses/pages/w-b-courses): Finetuning, building with LLMs, Structured outputs and more LLM courses.
45. [Prompt Engineering & AI tutorials & Resources](https://promptengineering.org/)
46. [Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer](https://www.youtube.com/watch?v=sVcwVQRHIc8)
47. [LLM Evaluation: A Complete Course](https://www.comet.com/site/llm-course/)
48. [HuggingFace Learn](https://huggingface.co/learn)
49. [Andrej Karpathy: Deep Dive into LLMs like ChatGPT](https://www.youtube.com/watch?v=7xTGNNLPyMI)
50. [LLM技术科普](https://github.com/karminski/one-small-step)
51. [CS25: Transformers United V5](https://web.stanford.edu/class/cs25/)
52. [RAG_Techniques](https://github.com/NirDiamant/RAG_Techniques): This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
53. [100+ LLM & RL Algorithm Maps | 原创 LLM / RL 100+原理图](https://github.com/changyeyu/LLM-RL-Visualized)
54. [Reinforcement Learning of Large Language Models](https://ernestryu.com/courses/RL-LLM.html)
55. [NanoChat](https://github.com/karpathy/nanochat): The best ChatGPT that $100 can buy.
56. [斯坦福CS146S: The Modern Software Developer](https://themodernsoftware.dev/)

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## 教程 Tutorial

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)
12. [LLM Visualization](https://bbycroft.net/llm)
13. [知乎: 原石人类](https://www.zhihu.com/people/zhang-shi-tou-88-98/posts)
14. [B站：小黑黑讲AI](https://space.bilibili.com/1963375439/?spm_id_from=333.999.0.0)
15. [B站：面壁的车辆工程师](https://space.bilibili.com/669720247/?spm_id_from=333.999.0.0)
16. [B站：AI老兵文哲](https://space.bilibili.com/472543316/?spm_id_from=333.999.0.0)
17. [Large Language Models (LLMs) with Colab notebooks](https://mlabonne.github.io/blog/)
18. [YTB：IBM Technology](https://www.youtube.com/@IBMTechnology)
19. [YTB: Unify Reading Paper Group](https://www.youtube.com/playlist?list=PLwNuX3xB_tv91QvDXlW2TjrLGHW51uMul)
20. [Chip Huyen](https://huyenchip.com/blog/)
21. [How Much VRAM](https://github.com/AlexBodner/How_Much_VRAM)
22. [Blog: 科学空间（苏剑林）](https://kexue.fm/)
23. [YTB: Hyung Won Chung](https://www.youtube.com/watch?v=dbo3kNKPaUA)
24. [Blog: Tejaswi kashyap](https://medium.com/@tejaswi_kashyap)
25. [Blog: 小昇的博客](https://xiaosheng.blog/)
26. [知乎: ybq](https://www.zhihu.com/people/ybq-29-32/posts)
27. [W&B articles](https://wandb.ai/fully-connected)
28. [Huggingface Blog](https://huggingface.co/blog/zh)
29. [Blog: GbyAI](https://gby.ai/)
30. [Blog: mlabonne](https://mlabonne.github.io/blog/)
31. [LLM-Action](https://github.com/liguodongiot/llm-action)
32. [Blog: Lil’Log (OponAI)](https://lilianweng.github.io/)
33. [B站: 毛玉仁](https://space.bilibili.com/3546823125895398)
34. [AI-Guide-and-Demos](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN)
35. [cnblog: 第七子](https://www.cnblogs.com/theseventhson)
36. [Implementation of all RAG techniques in a simpler way.](https://github.com/FareedKhan-dev/all-rag-techniques)
37. [Theoretical Machine Learning: A Handbook for Everyone](https://www.tengjiaye.com/mlbook.html)

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## 论文 Paper

> [!NOTE]
> 🤝[Huggingface Daily Papers](https://huggingface.co/papers)、[Cool Papers](https://papers.cool/)、[ML Papers Explained](https://github.com/dair-ai/ML-Papers-Explained)

1. [Hermes-3-Technical-Report](https://nousresearch.com/wp-content/uploads/2024/08/Hermes-3-Technical-Report.pdf)
2. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783)
3. [Qwen Technical Report](https://arxiv.org/abs/2309.16609)
4. [Qwen2 Technical Report](https://arxiv.org/abs/2407.10671)
5. [Qwen2-vl Technical Report](https://arxiv.org/abs/2409.12191)
6. [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://arxiv.org/abs/2401.02954)
7. [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434)
8. [Baichuan 2: Open Large-scale Language Models](https://arxiv.org/abs/2309.10305)
9. [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794)
10. [OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838)
11. [MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series](https://arxiv.org/abs/2405.19327)
12. [Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model](https://arxiv.org/abs/2404.04167)
13. [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219)
14. [Jamba-1.5: Hybrid Transformer-Mamba Models at Scale](https://arxiv.org/abs/2408.12570v1)
15. [Jamba: A Hybrid Transformer-Mamba Language Model](https://arxiv.org/abs/2403.19887)
16. [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644)
17. [Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models](https://arxiv.org/abs/2408.02085) `data`
18. [OLMoE: Open Mixture-of-Experts Language Models](https://arxiv.org/abs/2409.02060)
19. [Model Merging Paper](https://huggingface.co/collections/osanseviero/model-merging-65097893623330a3a51ead66)
20. [Baichuan-Omni Technical Report](https://arxiv.org/abs/2410.08565)
21. [1.5-Pints Technical Report: Pretraining in Days, Not Months – Your Language Model Thrives on Quality Data](https://arxiv.org/abs/2408.03506)
22. [Baichuan Alignment Technical Report](https://arxiv.org/abs/2410.14940v1)
23. [Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent](https://arxiv.org/abs/2411.02265)
24. [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models](https://arxiv.org/abs/2409.17146)
25. [TÜLU 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124)
26. [Phi-4 Technical Report](https://arxiv.org/abs/2412.08905)
27. [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://arxiv.org/abs/2412.05271)
28. [Qwen2.5 Technical Report](https://arxiv.org/abs/2412.15115)
29. [YuLan-Mini: An Open Data-efficient Language Model](https://arxiv.org/abs/2412.17743)
30. [An Introduction to Vision-Language Modeling](https://arxiv.org/abs/2405.17247)
31. [DeepSeek V3 Technical Report](https://github.com/WangRongsheng/awesome-LLM-resourses/blob/main/docs/DeepSeek_V3.pdf)
32. [2 OLMo 2 Furious](https://arxiv.org/abs/2501.00656)
33. [Yi-Lightning Technical Report](https://arxiv.org/abs/2412.01253)
34. [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://github.com/deepseek-ai/DeepSeek-R1)
35. [KIMI K1.5](https://github.com/WangRongsheng/awesome-LLM-resourses/blob/main/docs/Kimi_k1.5.pdf)
36. [Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models](https://arxiv.org/abs/2501.14818)
37. [Qwen2.5-VL Technical Report](https://arxiv.org/abs/2502.13923)
38. [Baichuan-M1: Pushing the Medical Capability of Large Language Models](https://arxiv.org/abs/2502.12671)
39. [Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining](https://arxiv.org/abs/2503.04715)
40. [SkyLadder: Better and Faster Pretraining via Context Window Scheduling](https://arxiv.org/abs/2503.15450)
41. [Qwen2.5-Omni technical report](https://github.com/QwenLM/Qwen2.5-Omni/blob/main/assets/Qwen2.5_Omni.pdf)
42. [Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs](https://arxiv.org/abs/2503.05139)
43. [Gemma 3 Technical Report](https://arxiv.org/abs/2503.19786)
44. [Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources](https://arxiv.org/abs/2504.00595)
45. [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://arxiv.org/abs/2504.07866)
46. [MiMo: Unlocking the Reasoning Potential of Language Model – From Pretraining to Posttraining](https://github.com/XiaomiMiMo/MiMo/blob/main/MiMo-7B-Technical-Report.pdf)
47. [Phi-4 Technical Report](https://arxiv.org/abs/2412.08905)
48. [Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)
49. [Qwen3 Technical Report](https://github.com/QwenLM/Qwen3/blob/main/Qwen3_Technical_Report.pdf)
50. [MiMo-VL Technical Report](https://arxiv.org/abs/2506.03569v1)
51. [ERNIE Technical Report](https://github.com/WangRongsheng/awesome-LLM-resources/blob/main/docs/ERNIE_Technical_Report_compressed.pdf)
52. [Kwai Keye-VL Technical Report](https://arxiv.org/abs/2507.01949)
53. [Kimi K2 Technical Report](https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf)
54. [KAT-V1: Kwai-AutoThink Technical Report](https://arxiv.org/abs/2507.08297v3)
55. [Step3](https://github.com/stepfun-ai/Step3)
56. [SAIL-VL2 Technical Report](https://arxiv.org/abs/2509.14033)
57. [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://arxiv.org/abs/2509.23661) [85M-Midtraining Data](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Mid-Training-85M) [22M Instruct Data](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data)
58. [Olmo3](https://www.datocms-assets.com/64837/1763646865-olmo_3_technical_report-1.pdf): Charting a path through the model flow to lead open-source AI. [Website](https://allenai.org/blog/olmo3)
59. [OpenMMReasoner](https://arxiv.org/abs/2511.16334)
60. [Qwen3-VL Technical Report](https://arxiv.org/pdf/2511.21631)

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## 社区 Community

1. [魔乐社区](https://modelers.cn/)
2. [HuggingFace](https://huggingface.co/)
3. [ModelScope](https://modelscope.cn/)
4. [WiseModel](https://www.wisemodel.cn/)
5. [OpenCSG](https://opencsg.com/)

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## 模型上下文协议 MCP

1. [MCP是啥？技术原理是什么？一个视频搞懂MCP的一切。Windows系统配置MCP，Cursor,Cline 使用MCP](https://www.youtube.com/watch?v=McNRkd5CxFY)
2. [MCP是什么？为啥是下一代AI标准？MCP原理+开发实战！在Cursor、Claude、Cline中使用MCP，让AI真正自动化！](https://www.youtube.com/watch?v=jGVsLeDxtQY)
3. [从零编写MCP并发布上线，超简单！手把手教程](https://www.youtube.com/watch?v=a3U-JrFkA9s)

MCP工具聚合：
1. [smithery.ai](https://smithery.ai/)
2. [mcp.so](https://mcp.so/)
3. [modelcontextprotocol/servers](https://github.com/modelcontextprotocol/servers)
4. [mcp.ad](https://mcp.ad/)
5. [pulsemcp.com](https://www.pulsemcp.com/)
6. [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers)
7. [glama.ai](https://glama.ai/mcp/servers)
8. [mcp.composio.dev](https://mcp.composio.dev/)
9. [awesome-mcp-list](https://github.com/MobinX/awesome-mcp-list)
10. [mcpo](https://github.com/open-webui/mcpo)
11. [FastMCP](https://github.com/jlowin/fastmcp)
12. [sharemcp.cn](https://sharemcp.cn/)
13. [mcpstore.co](https://mcpstore.co/)
14. [FastAPI-MCP](https://github.com/tadata-org/fastapi_mcp)
15. [modelscope/mcp](https://modelscope.cn/mcp)
16. [mcpm.sh](https://github.com/pathintegral-institute/mcpm.sh)

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## 技能 Skills

1. [Agent Skills (Claude Skills) 详细攻略，一期视频精通](https://www.bilibili.com/video/BV1HuiyBQE9G)
2. [OpenClaw](https://docs.openclaw.ai/zh-CN)


1. [awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills)
2. [Anthropics Skills](https://github.com/anthropics/skills)
3. [Skillsmp](https://skillsmp.com/)
4. [awesome-claude-skills](https://github.com/BehiSecc/awesome-claude-skills)
5. [ClawHub](https://clawhub.ai/)
6. [水产市场](https://openclawmp.cc/)
7. [Skills.Sh](https://skills.sh/)
8. [awesome-agent-skills](https://github.com/VoltAgent/awesome-agent-skills)
9. [llmbase](https://llmbase.ai/openclaw/)
10. [awesome-openclaw-skills](https://github.com/VoltAgent/awesome-openclaw-skills)
11. [claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills)
12. [LLMs-Universal-Life-Science-and-Clinical-Skills-](https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-)
13. [SkillHub](https://skillhub.tencent.com/#categories)
14. [LabClaw](https://github.com/wu-yc/LabClaw)
15. [Modelscope Skills](https://modelscope.cn/skills)
16. [Agent Skill](https://agentskill.sh/)
17. [mmx-cli](https://github.com/MiniMax-AI/cli)

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## 推理 Open o1

> [!NOTE]
> 
> 开放的技术是我们永恒的追求

1. https://github.com/atfortes/Awesome-LLM-Reasoning
2. https://github.com/hijkzzz/Awesome-LLM-Strawberry
3. https://github.com/wjn1996/Awesome-LLM-Reasoning-Openai-o1-Survey
4. https://github.com/srush/awesome-o1
5. https://github.com/open-thought/system-2-research
6. https://github.com/ninehills/blog/issues/121
7. https://github.com/OpenSource-O1/Open-O1
8. https://github.com/GAIR-NLP/O1-Journey
9. https://github.com/marlaman/show-me
10. https://github.com/bklieger-groq/g1
11. https://github.com/Jaimboh/Llamaberry-Chain-of-Thought-Reasoning-in-AI
12. https://github.com/pseudotensor/open-strawberry
13. https://huggingface.co/collections/peakji/steiner-preview-6712c6987110ce932a44e9a6
14. https://github.com/SimpleBerry/LLaMA-O1
15. https://huggingface.co/collections/Skywork/skywork-o1-open-67453df58e12f6c3934738d0
16. https://huggingface.co/collections/Qwen/qwq-674762b79b75eac01735070a
17. https://github.com/SkyworkAI/skywork-o1-prm-inference
18. https://github.com/RifleZhang/LLaVA-Reasoner-DPO
19. https://github.com/ADaM-BJTU
20. https://github.com/ADaM-BJTU/OpenRFT
21. https://github.com/RUCAIBox/Slow_Thinking_with_LLMs
22. https://github.com/richards199999/Thinking-Claude
23. https://huggingface.co/AGI-0/Art-v0-3B
24. https://huggingface.co/deepseek-ai/DeepSeek-R1
25. https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero
26. https://github.com/huggingface/open-r1
27. https://github.com/hkust-nlp/simpleRL-reason
28. https://github.com/Jiayi-Pan/TinyZero
29. https://github.com/baichuan-inc/Baichuan-M1-14B
30. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal
31. https://github.com/open-thoughts/open-thoughts
32. Mini-R1: https://www.philschmid.de/mini-deepseek-r1
33. LLaMA-Berry: https://arxiv.org/abs/2410.02884
34. MCTS-DPO: https://arxiv.org/abs/2405.00451
35. OpenR: https://github.com/openreasoner/openr
36. https://arxiv.org/abs/2410.02725
37. LLaVA-o1: https://arxiv.org/abs/2411.10440
38. Marco-o1: https://arxiv.org/abs/2411.14405
39. OpenAI o1 report: https://openai.com/index/deliberative-alignment
40. DRT-o1: https://github.com/krystalan/DRT-o1
41. Virgo：https://arxiv.org/abs/2501.01904
42. HuatuoGPT-o1：https://arxiv.org/abs/2412.18925
43. o1 roadmap：https://arxiv.org/abs/2412.14135
44. Mulberry：https://arxiv.org/abs/2412.18319
45. https://arxiv.org/abs/2412.09413
46. https://arxiv.org/abs/2501.02497
47. Search-o1:https://arxiv.org/abs/2501.05366v1
48. https://arxiv.org/abs/2501.18585
49. https://github.com/simplescaling/s1
50. https://github.com/Deep-Agent/R1-V
51. https://github.com/StarRing2022/R1-Nature
52. https://github.com/Unakar/Logic-RL
53. https://github.com/datawhalechina/unlock-deepseek
54. https://github.com/GAIR-NLP/LIMO
55. https://github.com/Zeyi-Lin/easy-r1
56. https://github.com/jackfsuia/nanoRLHF/tree/main/examples/r1-v0
57. https://github.com/FanqingM/R1-Multimodal-Journey
58. https://github.com/dhcode-cpp/X-R1
59. https://github.com/agentica-project/deepscaler
60. https://github.com/ZihanWang314/RAGEN
61. https://github.com/sail-sg/oat-zero
62. https://github.com/TideDra/lmm-r1
63. https://github.com/FlagAI-Open/OpenSeek
64. https://github.com/SwanHubX/ascend_r1_turtorial
65. https://github.com/om-ai-lab/VLM-R1
66. https://github.com/wizardlancet/diagnosis_zero
67. https://github.com/lsdefine/simple_GRPO
68. https://github.com/brendanhogan/DeepSeekRL-Extended
69. https://github.com/Wang-Xiaodong1899/Open-R1-Video
70. https://github.com/lsdefine/simple_GRPO
71. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero
72. https://github.com/lucasjinreal/Namo-R1
73. https://github.com/hiyouga/EasyR1
74. https://github.com/Fancy-MLLM/R1-Onevision
75. https://github.com/tulerfeng/Video-R1
76. https://huggingface.co/qihoo360/TinyR1-32B-Preview
77. https://github.com/facebookresearch/swe-rl
78. https://github.com/turningpoint-ai/VisualThinker-R1-Zero
79. https://github.com/yuyq96/R1-Vision
80. https://github.com/sungatetop/deepseek-r1-vision
81. https://huggingface.co/qihoo360/Light-R1-32B
82. https://github.com/Liuziyu77/Visual-RFT
83. https://github.com/Mohammadjafari80/GSM8K-RLVR
84. https://github.com/ModalMinds/MM-EUREKA
85. https://github.com/joey00072/nanoGRPO
86. https://github.com/PeterGriffinJin/Search-R1
87. https://openi.pcl.ac.cn/PCL-Reasoner/GRPO-Training-Suite
88. https://github.com/dvlab-research/Seg-Zero
89. https://github.com/HumanMLLM/R1-Omni
90. https://github.com/OpenManus/OpenManus-RL
91. https://arxiv.org/pdf/2503.07536
92. https://github.com/Osilly/Vision-R1
93. https://github.com/LengSicong/MMR1
94. https://github.com/phonism/CP-Zero
95. https://github.com/SkyworkAI/Skywork-R1V
96. https://arxiv.org/abs/2503.13939v1
97. https://github.com/0russwest0/Agent-R1
98. https://github.com/MetabrainAGI/Awaker2.5-R1
99. https://github.com/LG-AI-EXAONE/EXAONE-Deep
100. https://github.com/qiufengqijun/open-r1-reprod
101. https://github.com/SUFE-AIFLM-Lab/Fin-R1
102. https://github.com/sail-sg/understand-r1-zero
103. https://github.com/baibizhe/Efficient-R1-VLLM
104. https://github.com/hkust-nlp/simpleRL-reason
105. https://arxiv.org/abs/2502.19655
106. https://arxiv.org/abs/2503.21620v1
107. https://arxiv.org/abs/2503.16081
108. https://github.com/ShadeCloak/ADORA
109. https://github.com/appletea233/Temporal-R1
110. https://github.com/inclusionAI/AReaL
111. https://github.com/lzhxmu/CPPO
112. https://arxiv.org/abs/2503.23829
113. https://github.com/TencentARC/SEED-Bench-R1
114. https://github.com/McGill-NLP/nano-aha-moment
115. https://github.com/VLM-RL/Ocean-R1
116. https://github.com/OpenGVLab/VideoChat-R1
117. https://github.com/ByteDance-Seed/Seed-Thinking-v1.5
118. https://github.com/SkyworkAI/Skywork-OR1
119. https://github.com/MoonshotAI/Kimi-VL
120. https://arxiv.org/abs/2504.08600
121. https://github.com/ZhangXJ199/TinyLLaVA-Video-R1
122. https://arxiv.org/abs/2504.11914
123. https://github.com/policy-gradient/GRPO-Zero
124. https://github.com/linkangheng/PR1
125. https://github.com/jiangxinke/Agentic-RAG-R1
126. https://github.com/shangshang-wang/Tina
127. https://github.com/aliyun/qwen-dianjin
128. https://github.com/RAGEN-AI/RAGEN
129. https://github.com/XiaomiMiMo/MiMo
130. https://github.com/yuanzhoulvpi2017/nano_rl
131. https://huggingface.co/a-m-team/AM-Thinking-v1
132. https://huggingface.co/Intelligent-Internet/II-Medical-8B
133. https://github.com/CSfufu/Revisual-R1

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## 推理 Open o3

1. Mini-o3: https://arxiv.org/abs/2509.07969
2. Simple-o3: https://arxiv.org/abs/2508.12109
3. Thyme: https://arxiv.org/abs/2508.11630
4. Open o3 Video: https://arxiv.org/abs/2510.20579

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## 小语言模型 Small Language Model

1. https://github.com/jiahe7ay/MINI_LLM
2. https://github.com/jingyaogong/minimind
3. https://github.com/DLLXW/baby-llama2-chinese
4. https://github.com/charent/ChatLM-mini-Chinese
5. https://github.com/wdndev/tiny-llm-zh
6. https://github.com/Tongjilibo/build_MiniLLM_from_scratch
7. https://github.com/jzhang38/TinyLlama
8. https://github.com/AI-Study-Han/Zero-Chatgpt
9. https://github.com/loubnabnl/nanotron-smol-cluster ([使用Cosmopedia训练cosmo-1b](https://huggingface.co/blog/zh/cosmopedia))
10. https://github.com/charent/Phi2-mini-Chinese
11. https://github.com/allenai/OLMo
12. https://github.com/keeeeenw/MicroLlama
13. https://github.com/Chinese-Tiny-LLM/Chinese-Tiny-LLM
14. https://github.com/leeguandong/MiniLLaMA3
15. https://github.com/Pints-AI/1.5-Pints
16. https://github.com/zhanshijinwat/Steel-LLM
17. https://github.com/RUC-GSAI/YuLan-Mini
18. https://github.com/Om-Alve/smolGPT
19. https://github.com/skyzh/tiny-llm
20. https://github.com/qibin0506/Cortex
21. https://github.com/huggingface/picotron
22. https://github.com/Alic-Li/Mini_RWKV_7
23. https://huggingface.co/Nanbeige/Nanbeige4-3B-Thinking-2511 `23T tokens预训练模型`
24. https://github.com/stepfun-ai/SteptronOss
25. https://github.com/huggingface/nanowhale
26. https://github.com/OpenBMB/ForgeTrain


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## 小多模态模型 Small Vision Language Model

1. https://github.com/jingyaogong/minimind-v
2. https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/train_llava
3. https://github.com/AI-Study-Han/Zero-Qwen-VL
4. https://github.com/Coobiw/MPP-LLaVA
5. https://github.com/qnguyen3/nanoLLaVA
6. https://github.com/TinyLLaVA/TinyLLaVA_Factory
7. https://github.com/ZhangXJ199/TinyLLaVA-Video
8. https://github.com/Emericen/tiny-qwen
9. https://github.com/merveenoyan/smol-vision
10. https://github.com/huggingface/nanoVLM
11. https://github.com/GeeeekExplorer/nano-vllm
12. https://github.com/ritabratamaiti/AnyModal
13. https://github.com/yujunhuics/Reyes
14. https://github.com/Victorwz/Open-Qwen2VL
15. https://github.com/jingyaogong/minimind-o

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## 技巧 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)
5. [LLMs for Text Classification: A Guide to Supervised Learning](https://www.striveworks.com/blog/llms-for-text-classification-a-guide-to-supervised-learning)
6. [Unsupervised Text Classification: Categorize Natural Language With LLMs](https://www.striveworks.com/blog/unsupervised-text-classification-how-to-use-llms-to-categorize-natural-language-data)
7. [Text Classification With LLMs: A Roundup of the Best Methods](https://www.striveworks.com/blog/text-classification-with-llms-a-roundup-of-the-best-methods)
8. [LLM Pricing](https://docs.google.com/spreadsheets/d/18GHPEBJzDbICmMStPVkNWA_hQHiWmLcqUdEJA1b4MJM/edit?gid=0#gid=0)
9. [Uncensor any LLM with abliteration](https://huggingface.co/blog/mlabonne/abliteration)
10. [Tiny LLM Universe](https://github.com/datawhalechina/tiny-universe)
11. [Zero-Chatgpt](https://github.com/AI-Study-Han/Zero-Chatgpt)
12. [Zero-Qwen-VL](https://github.com/AI-Study-Han/Zero-Qwen-VL)
13. [finetune-Qwen2-VL](https://github.com/zhangfaen/finetune-Qwen2-VL)
14. [MPP-LLaVA](https://github.com/Coobiw/MPP-LLaVA)
15. [build_MiniLLM_from_scratch](https://github.com/Tongjilibo/build_MiniLLM_from_scratch)
16. [Tiny LLM zh](https://github.com/wdndev/tiny-llm-zh)
17. [MiniMind](https://github.com/jingyaogong/minimind): 3小时完全从0训练一个仅有26M的小参数GPT，最低仅需2G显卡即可推理训练.
18. [LLM-Travel](https://github.com/Glanvery/LLM-Travel): 致力于深入理解、探讨以及实现与大模型相关的各种技术、原理和应用
19. [Knowledge distillation: Teaching LLM's with synthetic data](https://wandb.ai/byyoung3/ML_NEWS3/reports/Knowledge-distillation-Teaching-LLM-s-with-synthetic-data--Vmlldzo5MTMyMzA2)
20. [Part 1: Methods for adapting large language models](https://ai.meta.com/blog/adapting-large-language-models-llms/)
21. [Part 2: To fine-tune or not to fine-tune](https://ai.meta.com/blog/when-to-fine-tune-llms-vs-other-techniques/)
22. [Part 3: How to fine-tune: Focus on effective datasets](https://ai.meta.com/blog/how-to-fine-tune-llms-peft-dataset-curation/)
23. [Reader-LM: Small Language Models for Cleaning and Converting HTML to Markdown](https://jina.ai/news/reader-lm-small-language-models-for-cleaning-and-converting-html-to-markdown/?nocache=1)
24. [LLMs应用构建一年之心得](https://iangyan.github.io/2024/09/08/building-with-llms-part-1/)
25. [LLM训练-pretrain](https://zhuanlan.zhihu.com/p/718354385)
26. [pytorch-llama](https://github.com/hkproj/pytorch-llama): LLaMA 2 implemented from scratch in PyTorch.
27. [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm) 【[support model](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForVision2Seq)】
28. [Fine-tuning visual language models using SFTTrainer](https://huggingface.co/blog/vlms) 【[docs](https://huggingface.co/docs/trl/sft_trainer#extending-sfttrainer-for-vision-language-models)】
29. [A Visual Guide to Mixture of Experts (MoE)](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts)
30. [Role-Playing in Large Language Models like ChatGPT](https://promptengineering.org/role-playing-in-large-language-models-like-chatgpt/)
31. [Distributed Training Guide](https://github.com/LambdaLabsML/distributed-training-guide): Best practices & guides on how to write distributed pytorch training code.
32. [Chat Templates](https://hf-mirror.com/blog/chat-templates)
33. [Top 20+ RAG Interview Questions](https://www.analyticsvidhya.com/blog/2024/04/rag-interview-questions/)
34. [LLM-Dojo 开源大模型学习场所，使用简洁且易阅读的代码构建模型训练框架](https://github.com/mst272/LLM-Dojo)
35. [o1 isn’t a chat model (and that’s the point)](https://www.latent.space/p/o1-skill-issue)
36. [Beam Search快速理解及代码解析](https://www.cnblogs.com/nickchen121/p/15499576.html)
37. [基于 transformers 的 generate() 方法实现多样化文本生成：参数含义和算法原理解读](https://blog.csdn.net/muyao987/article/details/125917234)
38. [The Ultra-Scale Playbook: Training LLMs on GPU Clusters](https://huggingface.co/spaces/nanotron/ultrascale-playbook)

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![](https://camo.githubusercontent.com/2722992d519a722218f896d5f5231d49f337aaff4514e78bd59ac935334e916a/68747470733a2f2f692e696d6775722e636f6d2f77617856496d762e706e67)

贡献者:

<a href="https://github.com/WangRongsheng/awesome-LLM-resources/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=WangRongsheng/awesome-LLM-resources" />
</a>

如果你觉得本项目对你有帮助，欢迎引用：
```bib
@misc{wang2024llm,
      title={awesome-LLM-resourses}, 
      author={Rongsheng Wang},
      year={2024},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/WangRongsheng/awesome-LLM-resourses}},
}
```

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