awesome-LLM-resources

全世界最好的大语言模型资源汇总持续更新

[!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 !

数据 Data
微调 Fine-Tuning
推理 Inference
评估 Evaluation
体验 Usage
知识库 RAG
智能体 Agents
代码 Coding
视频 Video
图片 Image
搜索 Search
语音 Speech
统一模型 Unified Model
书籍 Book
课程 Course
教程 Tutorial
论文 Paper
社区 Community
模型上下文协议 MCP
推理 Open o1
推理 Open o3
小语言模型 Small Language Model
小多模态模型 Small Vision Language Model
技巧 Tips

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

AotoLabel: Label, clean and enrich text datasets with LLMs.
LabelLLM: The Open-Source Data Annotation Platform.
data-juicer: A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs!
OmniParser: a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.
MinerU (🔥): MinerU is a one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.
PDF-Extract-Kit: A Comprehensive Toolkit for High-Quality PDF Content Extraction.
Parsera: Lightweight library for scraping web-sites with LLMs.
Sparrow: Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images.
Docling: Get your documents ready for gen AI.
GOT-OCR2.0: OCR Model.
LLM Decontaminator: Rethinking Benchmark and Contamination for Language Models with Rephrased Samples.
DataTrove: DataTrove is a library to process, filter and deduplicate text data at a very large scale.
llm-swarm: Generate large synthetic datasets like Cosmopedia.
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.
Common-Crawl-Pipeline-Creator: The Common Crawl Pipeline Creator.
Tabled: Detect and extract tables to markdown and csv.
Zerox: Zero shot pdf OCR with gpt-4o-mini.
DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception.
TensorZero: make LLMs improve through experience.
Promptwright: Generate large synthetic data using a local LLM.
pdf-extract-api: Document (PDF) extraction and parse API using state of the art modern OCRs + Ollama supported models.
pdf2htmlEX: Convert PDF to HTML without losing text or format.
Extractous: Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
MegaParse: File Parser optimised for LLM Ingestion with no loss.
MarkItDown: Python tool for converting files and office documents to Markdown.
datasketch: datasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy.
semhash: lightweight and flexible tool for deduplicating datasets using semantic similarity.
ReaderLM-v2: a 1.5B parameter language model that converts raw HTML into beautifully formatted markdown or JSON.
Bespoke Curator: Data Curation for Post-Training & Structured Data Extraction.
LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). Extracts signals from prompts & responses, ensuring safety & security.
Curator: Synthetic Data curation for post-training and structured data extraction.
olmOCR: A toolkit for training language models to work with PDF documents in the wild.
Easy Dataset (🔥): A powerful tool for creating fine-tuning datasets for LLM.
BabelDOC: PDF scientific paper translation and bilingual comparison library.
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting.
EasyDistill: Easy Knowledge Distillation for Large Language Models.
ContextGem: a free, open-source LLM framework that makes it radically easier to extract structured data and insights from documents.
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.
DataFlow: Easy Data Preparation with latest LLMs-based Operators and Pipelines.
DatasetLoom (multimodal): 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.
Logics-Parsing
DeepSeek-OCR
PaddleOCR-VL
Chandra: a highly accurate OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
HunyuanOCR: a leading end-to-end OCR expert VLM powered by Hunyuan’s native multimodal architecture.

↥ back to top

微调 Fine-Tuning

LLaMA-Factory (🔥): Unify Efficient Fine-Tuning of 100+ LLMs.
360-LLaMA-Factory: Unify Efficient Fine-Tuning of 100+ LLMs. (add Sequence Parallelism for supporting long context training)
unsloth: 2-5X faster 80% less memory LLM finetuning.
TRL: Transformer Reinforcement Learning.
Firefly: Firefly: 大模型训练工具，支持训练数十种大模型
Xtuner: An efficient, flexible and full-featured toolkit for fine-tuning large models.
torchtune: A Native-PyTorch Library for LLM Fine-tuning.
Swift: Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs.
AutoTrain: A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models.
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO).
Ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models.
mistral-finetune: A light-weight codebase that enables memory-efficient and performant finetuning of Mistral’s models.
aikit: Fine-tune, build, and deploy open-source LLMs easily!
H2O-LLMStudio: H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs.
LitGPT: Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.
LLMBox: A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.
PaddleNLP: Easy-to-use and powerful NLP and LLM library.
workbench-llamafactory: This is an NVIDIA AI Workbench example project that demonstrates an end-to-end model development workflow using Llamafactory.
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral).
TinyLLaVA Factory: A Framework of Small-scale Large Multimodal Models.
LLM-Foundry: LLM training code for Databricks foundation models.
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.
Simplifine: Simplifine lets you invoke LLM finetuning with just one line of code using any Hugging Face dataset or model.
Transformer Lab: Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
Liger-Kernel: Efficient Triton Kernels for LLM Training.
ChatLearn: A flexible and efficient training framework for large-scale alignment.
nanotron: Minimalistic large language model 3D-parallelism training.
Proxy Tuning: Tuning Language Models by Proxy.
Effective LLM Alignment: Effective LLM Alignment Toolkit.
Autotrain-advanced
Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.
Vision-LLM Alignemnt: 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.
finetune-Qwen2-VL: Quick Start for Fine-tuning or continue pre-train Qwen2-VL Model.
Online-RLHF: A recipe for online RLHF and online iterative DPO.
InternEvo: an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
veRL: Volcano Engine Reinforcement Learning for LLM.
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.
Oumi: Everything you need to build state-of-the-art foundation models, end-to-end.
Kiln: The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.
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.
MLX-VLM: MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
RL-Factory: Train your Agent model via our easy and efficient framework.
RM-Gallery: A One-Stop Reward Model Platform.
ART: rain multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training.
VeRL (🔥): Volcano Engine Reinforcement Learning for LLMs.
LMMs-Engine: A simple, any-to-any modality framework for pretraining and finetuning. Lean, flexible, and built for research.
dLLM: a library that unifies the training and evaluation of diffusion language models, bringing transparency and reproducibility to the entire development pipeline. diffusion
Miles: an enterprise-facing reinforcement learning framework for large-scale MoE post-training and production workloads.
Skills: a collection of pipelines to improve “skills” of large language models (LLMs).

↥ back to top

推理 Inference

ollama: Get up and running with Llama 3, Mistral, Gemma, and other large language models.
Open WebUI: User-friendly WebUI for LLMs (Formerly Ollama WebUI).
Text Generation WebUI: A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Xinference: A powerful and versatile library designed to serve language, speech recognition, and multimodal models.
LangChain: Build context-aware reasoning applications.
LlamaIndex: A data framework for your LLM applications.
lobe-chat: an open-source, modern-design LLMs/AI chat framework. Supports Multi AI Providers, Multi-Modals (Vision/TTS) and plugin system.
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.
vllm (🔥): A high-throughput and memory-efficient inference and serving engine for LLMs.
LlamaChat: Chat with your favourite LLaMA models in a native macOS app.
NVIDIA 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.
LM Studio: Discover, download, and run local LLMs.
chat-with-mlx: Chat with your data natively on Apple Silicon using MLX Framework.
LLM Pricing: 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.
Open Interpreter: A natural language interface for computers.
Chat-ollama: An open source chatbot based on LLMs. It supports a wide range of language models, and knowledge base management.
chat-ui: Open source codebase powering the HuggingChat app.
MemGPT: Create LLM agents with long-term memory and custom tools.
koboldcpp: A simple one-file way to run various GGML and GGUF models with KoboldAI’s UI.
LLMFarm: llama and other large language models on iOS and MacOS offline using GGML library.
enchanted: Enchanted is iOS and macOS app for chatting with private self hosted language models such as Llama2, Mistral or Vicuna using Ollama.
Flowise: Drag & drop UI to build your customized LLM flow.
Jan: Jan is an open source alternative to ChatGPT that runs 100% offline on your computer. Multiple engine support (llama.cpp, TensorRT-LLM).
LMDeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
RouteLLM: A framework for serving and evaluating LLM routers - save LLM costs without compromising quality!
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.
Mem0: The memory layer for Personalized AI.
SGLang (🔥): SGLang is yet another fast serving framework for large language models and vision language models.
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.
LLMHub: LLMHub is a lightweight management platform designed to streamline the operation and interaction with various language models (LLMs).
YuanChat
LiteLLM: Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
GuideLLM: GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs).
LLM-Engines: A unified inference engine for large language models (LLMs) including open-source models (VLLM, SGLang, Together) and commercial models (OpenAI, Mistral, Claude).
OARC: 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.
g1: Using Llama-3.1 70b on Groq to create o1-like reasoning chains.
MemoryScope: MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
OpenLLM: Run any open-source LLMs, such as Llama 3.1, Gemma, as OpenAI compatible API endpoint in the cloud.
Infinity: The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text.
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.
LLaMA Box: LLM inference server implementation based on llama.cpp.
ZhiLight: A highly optimized inference acceleration engine for Llama and its variants.
DashInfer: DashInfer is a native LLM inference engine aiming to deliver industry-leading performance atop various hardware architectures.
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.
ktransformers: A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations.
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.
Chitu: High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.
TokenSwift: From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation.
Cherry Studio: a desktop client that supports for multiple LLM providers, available on Windows, Mac and Linux.
Shimmy: Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary.
LlamaBarn: Run local LLMs on your Mac with a simple menu bar app.
Parallax: a distributed model serving framework that lets you build your own AI cluster anywhere.
xLLM: A high-performance inference engine for LLMs, optimized for diverse AI accelerators.

↥ back to top

评估 Evaluation

lm-evaluation-harness: A framework for few-shot evaluation of language models.
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.
llm-comparator: LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed.
EvalScope (🔥)
Weave: A lightweight toolkit for tracking and evaluating LLM applications.
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures.
Evaluation guidebook: If you’ve ever wondered how to make sure an LLM performs well on your specific task, this guide is for you!
Ollama Benchmark: LLM Benchmark for Throughput via Ollama (Local LLMs).
VLMEvalKit: Open-source evaluation toolkit of large vision-language models (LVLMs), support ~100 VLMs, 40+ benchmarks.
AGI-Eval
EvalScope: A streamlined and customizable framework for efficient large model evaluation and performance benchmarking.
DeepEval: a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems.
Lighteval: Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends.
QwQ/eval: QwQ is the reasoning model series developed by Qwen team, Alibaba Cloud.
Evalchemy: A unified and easy-to-use toolkit for evaluating post-trained language models.
MathArena: Evaluation of LLMs on latest math competitions.
YourBench: A Dynamic Benchmark Generation Framework.
MedEvalKit: A Unified Medical Evaluation Framework.

LLM API 服务平台：

↥ back to top

体验 Usage

LMSYS Chatbot Arena: Benchmarking LLMs in the Wild

↥ back to top

知识库 RAG

AnythingLLM: The all-in-one AI app for any LLM with full RAG and AI Agent capabilites.
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
RAGFlow: An open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
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.
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.
Langchain-Chatchat: 基于 Langchain 与 ChatGLM 等不同大语言模型的本地知识库问答
QAnything: Question and Answer based on Anything.
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.
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.
Verba: Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.
FlashRAG: A Python Toolkit for Efficient RAG Research.
GraphRAG: A modular graph-based Retrieval-Augmented Generation (RAG) system.
LightRAG: LightRAG helps developers with both building and optimizing Retriever-Agent-Generator pipelines.
GraphRAG-Ollama-UI: GraphRAG using Ollama with Gradio UI and Extra Features.
nano-GraphRAG: A simple, easy-to-hack GraphRAG implementation.
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.
ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.
kotaemon: An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
RAGapp: The easiest way to use Agentic RAG in any enterprise.
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text.
LightRAG: Simple and Fast Retrieval-Augmented Generation.
TEN: the Next-Gen AI-Agent Framework, the world’s first truly real-time multimodal AI agent framework.
AutoRAG: RAG AutoML tool for automatically finding an optimal RAG pipeline for your data.
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.
Fast-GraphRAG: RAG that intelligently adapts to your use case, data, and queries.
Tiny-GraphRAG
DB-GPT GraphRAG: 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 for details.
Chonkie: The no-nonsense RAG chunking library that’s lightweight, lightning-fast, and ready to CHONK your texts.
RAGLite: RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite.
KAG: KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs.
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.
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.
XRAG: a benchmarking framework designed to evaluate the foundational components of advanced Retrieval-Augmented Generation (RAG) systems.
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation.
RAG-Anything: All-in-One RAG System.

↥ back to top

智能体 Agents

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
CrewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Coze
AgentGPT: Assemble, configure, and deploy autonomous AI Agents in your browser.
XAgent: An Autonomous LLM Agent for Complex Task Solving.
MobileAgent: The Powerful Mobile Device Operation Assistant Family.
Lagent: A lightweight framework for building LLM-based agents.
Qwen-Agent: Agent framework and applications built upon Qwen2, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.
LinkAI: 一站式 AI 智能体搭建平台
Baidu APPBuilder
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.
LazyLLM: 低代码构建多Agent大模型应用的开发工具
AgentScope: Start building LLM-empowered multi-agent applications in an easier way.
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.
Agently: AI Agent Application Development Framework.
OmAgent: A multimodal agent framework for solving complex tasks.
Tribe: No code tool to rapidly build and coordinate multi-agent teams.
CAMEL: First LLM multi-agent framework and an open-source community dedicated to finding the scaling law of agents.
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.
IoA: An open-source framework for collaborative AI agents, enabling diverse, distributed agents to team up and tackle complex tasks through internet-like connectivity.
llama-agentic-system : Agentic components of the Llama Stack APIs.
Agent Zero: Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it.
Agents: An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents.
AgentScope: Start building LLM-empowered multi-agent applications in an easier way.
FastAgency: The fastest way to bring multi-agent workflows to production.
Swarm: Framework for building, orchestrating and deploying multi-agent systems. Managed by OpenAI Solutions team. Experimental framework.
Agent-S: an open agentic framework that uses computers like a human.
PydanticAI: Agent Framework / shim to use Pydantic with LLMs.
Agentarium: open-source framework for creating and managing simulations populated with AI-powered agents.
smolagents: a barebones library for agents. Agents write python code to call tools and orchestrate other agents.
Cooragent: Cooragent is an AI agent collaboration community.
Agno: Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
Suna: Open Source Generalist AI Agent.
rowboat: Let AI build multi-agent workflows for you in minutes.
EvoAgentX: Building a Self-Evolving Ecosystem of AI Agents.
ii-agent: a new open-source framework to build and deploy intelligent agents.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation.
OpenManus: No fortress, purely open ground. OpenManus is Coming.
JoyAgent-JDGenie: 业界首个开源高完成度轻量化通用多智能体产品.
coze-studio: An AI agent development platform with all-in-one visual tools, simplifying agent creation, debugging, and deployment like never before.
OxyGent: An advanced Python framework that empowers developers to quickly build production-ready intelligent systems.
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。
OpenAgents: AI Agent Networks for Open Collaboration.
SandBox: All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.
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).
Astron Agent: Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.

↥ back to top

代码 Coding

Cloi CLI: Local debugging agent that runs in your terminal.
Devin
v0
Blot.new
cursor
Windsurf
cline
Trae
MGX
Roo Code
Kilo Code
AugmentCode
Claude Code
Gemini CLI
Serena
Claudia
OpenCode
Kiro
CodeBuddy
Kiro
CodeX
Kimi-CLI

↥ back to top

视频 Video

模型

[!NOTE] 🤝Awesome-Video-Diffusion

编辑

Wan2.1-VACE-14B: https://huggingface.co/Wan-AI/Wan2.1-VACE-14B
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

推理

https://github.com/ModelTC/LightX2V

实用工具

PySceneDetect: Python and OpenCV-based scene cut/transition detection program & library.
DOVER: Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives.

↥ back to top

图片 Image

模型

awesome-nano-banana
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

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

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

评估

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

↥ back to top

搜索 Search

OpenSearch GPT: SearchGPT / Perplexity clone, but personalised for you.
MindSearch: An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT).
nanoPerplexityAI: The simplest open-source implementation of perplexity.ai.
curiosity: Try to build a Perplexity-like user experience.
MiniPerplx: A minimalistic AI-powered search engine that helps you find information on the internet.

↥ back to top

语音 Speech

TTS

SpeechGPT-2.0-preview: https://github.com/OpenMOSS/SpeechGPT-2.0-preview
Moss-TTSD：https://github.com/OpenMOSS/MOSS-TTSD
Index-TTS：https://github.com/index-tts/index-tts
MegaTTS3：https://github.com/bytedance/MegaTTS3
F5-TTS：https://github.com/SWivid/F5-TTS
GPT-SoVITS：https://github.com/RVC-Boss/GPT-SoVITS
CosyVoice：https://github.com/FunAudioLLM/CosyVoice
Spark-TTS：https://github.com/SparkAudio/Spark-TTS
OpenVoice：https://github.com/myshell-ai/OpenVoice
Dia：https://github.com/nari-labs/dia
ChatTTS：https://github.com/2noise/ChatTTS
Fish Speech：https://github.com/fishaudio/fish-speech
Edge-TTS：https://github.com/rany2/edge-tts
Bark：https://github.com/suno-ai/bark
kokoro: https://github.com/hexgrad/kokoro
Higgs Audio V2: https://github.com/boson-ai/higgs-audio 【Training】
KittenTTS: https://github.com/KittenML/KittenTTS
ZipVoice: https://github.com/k2-fsa/ZipVoice
VyvoTTS: https://github.com/Vyvo-Labs/VyvoTTS
VibeVoice: https://github.com/microsoft/VibeVoice
Index-TTS-2: https://huggingface.co/IndexTeam/IndexTTS-2
FireRedTTS2: https://github.com/FireRedTeam/FireRedTTS2
VoxCPM: https://github.com/OpenBMB/VoxCPM/
Neutts-Air: https://github.com/neuphonic/neutts-air
Maya1: https://huggingface.co/maya-research/maya1
VibeVoice: https://huggingface.co/collections/microsoft/vibevoice

STT

Kyutai: https://github.com/kyutai-labs/delayed-streams-modeling
Whisper: https://github.com/openai/whisper
Audio Flamingo 3: https://huggingface.co/nvidia/audio-flamingo-3
Voxtral: https://huggingface.co/mistralai/Voxtral-Mini-3B-2507
Step-Audio2: https://github.com/stepfun-ai/Step-Audio2
SoulX-Podcast: https://huggingface.co/collections/Soul-AILab/soulx-podcast
Omnilingual ASR: https://github.com/facebookresearch/omnilingual-asr

↥ back to top

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

↥ back to top

书籍 Book

↥ back to top

课程 Course

LLM Resources Hub

斯坦福 CS224N: Natural Language Processing with Deep Learning
吴恩达: Generative AI for Everyone
吴恩达: LLM series of courses
ACL 2023 Tutorial: Retrieval-based Language Models and Applications
llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
微软: Generative AI for Beginners
微软: State of GPT
HuggingFace NLP Course
清华 NLP 刘知远团队大模型公开课
斯坦福 CS25: Transformers United V4
斯坦福 CS324: Large Language Models
普林斯顿 COS 597G (Fall 2022): Understanding Large Language Models
约翰霍普金斯 CS 601.471/671 NLP: Self-supervised Models
李宏毅 GenAI课程
openai-cookbook: Examples and guides for using the OpenAI API.
Hands on llms: Learn about LLM, LLMOps, and vector DBS for free by designing, training, and deploying a real-time financial advisor LLM system.
滑铁卢大学 CS 886: Recent Advances on Foundation Models
Mistral: Getting Started with Mistral
斯坦福 CS25: Transformers United V4
Coursera: Chatgpt 应用提示工程
LangGPT: Empowering everyone to become a prompt expert!
mistralai-cookbook
Introduction to Generative AI 2024 Spring
build nanoGPT: Video+code lecture on building nanoGPT from scratch.
LLM101n: Let’s build a Storyteller.
Knowledge Graphs for RAG
LLMs From Scratch (Datawhale Version)
OpenRAG
通往AGI之路
Andrej Karpathy - Neural Networks: Zero to Hero
Interactive visualization of Transformer
andysingal/llm-course
LM-class
Google Advanced: Generative AI for Developers Learning Path
Anthropics：Prompt Engineering Interactive Tutorial
LLMsBook
Large Language Model Agents
Cohere LLM University
LLMs and Transformers
Smol Vision: Recipes for shrinking, optimizing, customizing cutting edge vision models.
Multimodal RAG: Chat with Videos
LLMs Interview Note
RAG++ : From POC to production: Advanced RAG course.
Weights & Biases AI Academy: Finetuning, building with LLMs, Structured outputs and more LLM courses.
Prompt Engineering & AI tutorials & Resources
Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
LLM Evaluation: A Complete Course
HuggingFace Learn
Andrej Karpathy: Deep Dive into LLMs like ChatGPT
LLM技术科普
CS25: Transformers United V5
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.
[100+ LLM & RL Algorithm Maps 原创 LLM / RL 100+原理图](https://github.com/changyeyu/LLM-RL-Visualized)
Reinforcement Learning of Large Language Models
NanoChat: The best ChatGPT that $100 can buy.
斯坦福CS146S: The Modern Software Developer

↥ back to top

教程 Tutorial

↥ back to top

论文 Paper

[!NOTE] 🤝Huggingface Daily Papers、Cool Papers、ML Papers Explained

↥ back to top

社区 Community

↥ back to top

模型上下文协议 MCP

MCP工具聚合：

↥ back to top

推理 Open o1

[!NOTE]

开放的技术是我们永恒的追求

https://github.com/atfortes/Awesome-LLM-Reasoning
https://github.com/hijkzzz/Awesome-LLM-Strawberry
https://github.com/wjn1996/Awesome-LLM-Reasoning-Openai-o1-Survey
https://github.com/srush/awesome-o1
https://github.com/open-thought/system-2-research
https://github.com/ninehills/blog/issues/121
https://github.com/OpenSource-O1/Open-O1
https://github.com/GAIR-NLP/O1-Journey
https://github.com/marlaman/show-me
https://github.com/bklieger-groq/g1
https://github.com/Jaimboh/Llamaberry-Chain-of-Thought-Reasoning-in-AI
https://github.com/pseudotensor/open-strawberry
https://huggingface.co/collections/peakji/steiner-preview-6712c6987110ce932a44e9a6
https://github.com/SimpleBerry/LLaMA-O1
https://huggingface.co/collections/Skywork/skywork-o1-open-67453df58e12f6c3934738d0
https://huggingface.co/collections/Qwen/qwq-674762b79b75eac01735070a
https://github.com/SkyworkAI/skywork-o1-prm-inference
https://github.com/RifleZhang/LLaVA-Reasoner-DPO
https://github.com/ADaM-BJTU
https://github.com/ADaM-BJTU/OpenRFT
https://github.com/RUCAIBox/Slow_Thinking_with_LLMs
https://github.com/richards199999/Thinking-Claude
https://huggingface.co/AGI-0/Art-v0-3B
https://huggingface.co/deepseek-ai/DeepSeek-R1
https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero
https://github.com/huggingface/open-r1
https://github.com/hkust-nlp/simpleRL-reason
https://github.com/Jiayi-Pan/TinyZero
https://github.com/baichuan-inc/Baichuan-M1-14B
https://github.com/EvolvingLMMs-Lab/open-r1-multimodal
https://github.com/open-thoughts/open-thoughts
Mini-R1: https://www.philschmid.de/mini-deepseek-r1
LLaMA-Berry: https://arxiv.org/abs/2410.02884
MCTS-DPO: https://arxiv.org/abs/2405.00451
OpenR: https://github.com/openreasoner/openr
https://arxiv.org/abs/2410.02725
LLaVA-o1: https://arxiv.org/abs/2411.10440
Marco-o1: https://arxiv.org/abs/2411.14405
OpenAI o1 report: https://openai.com/index/deliberative-alignment
DRT-o1: https://github.com/krystalan/DRT-o1
Virgo：https://arxiv.org/abs/2501.01904
HuatuoGPT-o1：https://arxiv.org/abs/2412.18925
o1 roadmap：https://arxiv.org/abs/2412.14135
Mulberry：https://arxiv.org/abs/2412.18319
https://arxiv.org/abs/2412.09413
https://arxiv.org/abs/2501.02497
Search-o1:https://arxiv.org/abs/2501.05366v1
https://arxiv.org/abs/2501.18585
https://github.com/simplescaling/s1
https://github.com/Deep-Agent/R1-V
https://github.com/StarRing2022/R1-Nature
https://github.com/Unakar/Logic-RL
https://github.com/datawhalechina/unlock-deepseek
https://github.com/GAIR-NLP/LIMO
https://github.com/Zeyi-Lin/easy-r1
https://github.com/jackfsuia/nanoRLHF/tree/main/examples/r1-v0
https://github.com/FanqingM/R1-Multimodal-Journey
https://github.com/dhcode-cpp/X-R1
https://github.com/agentica-project/deepscaler
https://github.com/ZihanWang314/RAGEN
https://github.com/sail-sg/oat-zero
https://github.com/TideDra/lmm-r1
https://github.com/FlagAI-Open/OpenSeek
https://github.com/SwanHubX/ascend_r1_turtorial
https://github.com/om-ai-lab/VLM-R1
https://github.com/wizardlancet/diagnosis_zero
https://github.com/lsdefine/simple_GRPO
https://github.com/brendanhogan/DeepSeekRL-Extended
https://github.com/Wang-Xiaodong1899/Open-R1-Video
https://github.com/lsdefine/simple_GRPO
https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero
https://github.com/lucasjinreal/Namo-R1
https://github.com/hiyouga/EasyR1
https://github.com/Fancy-MLLM/R1-Onevision
https://github.com/tulerfeng/Video-R1
https://huggingface.co/qihoo360/TinyR1-32B-Preview
https://github.com/facebookresearch/swe-rl
https://github.com/turningpoint-ai/VisualThinker-R1-Zero
https://github.com/yuyq96/R1-Vision
https://github.com/sungatetop/deepseek-r1-vision
https://huggingface.co/qihoo360/Light-R1-32B
https://github.com/Liuziyu77/Visual-RFT
https://github.com/Mohammadjafari80/GSM8K-RLVR
https://github.com/ModalMinds/MM-EUREKA
https://github.com/joey00072/nanoGRPO
https://github.com/PeterGriffinJin/Search-R1
https://openi.pcl.ac.cn/PCL-Reasoner/GRPO-Training-Suite
https://github.com/dvlab-research/Seg-Zero
https://github.com/HumanMLLM/R1-Omni
https://github.com/OpenManus/OpenManus-RL
https://arxiv.org/pdf/2503.07536
https://github.com/Osilly/Vision-R1
https://github.com/LengSicong/MMR1
https://github.com/phonism/CP-Zero
https://github.com/SkyworkAI/Skywork-R1V
https://arxiv.org/abs/2503.13939v1
https://github.com/0russwest0/Agent-R1
https://github.com/MetabrainAGI/Awaker2.5-R1
https://github.com/LG-AI-EXAONE/EXAONE-Deep
https://github.com/qiufengqijun/open-r1-reprod
https://github.com/SUFE-AIFLM-Lab/Fin-R1
https://github.com/sail-sg/understand-r1-zero
https://github.com/baibizhe/Efficient-R1-VLLM
https://github.com/hkust-nlp/simpleRL-reason
https://arxiv.org/abs/2502.19655
https://arxiv.org/abs/2503.21620v1
https://arxiv.org/abs/2503.16081
https://github.com/ShadeCloak/ADORA
https://github.com/appletea233/Temporal-R1
https://github.com/inclusionAI/AReaL
https://github.com/lzhxmu/CPPO
https://arxiv.org/abs/2503.23829
https://github.com/TencentARC/SEED-Bench-R1
https://github.com/McGill-NLP/nano-aha-moment
https://github.com/VLM-RL/Ocean-R1
https://github.com/OpenGVLab/VideoChat-R1
https://github.com/ByteDance-Seed/Seed-Thinking-v1.5
https://github.com/SkyworkAI/Skywork-OR1
https://github.com/MoonshotAI/Kimi-VL
https://arxiv.org/abs/2504.08600
https://github.com/ZhangXJ199/TinyLLaVA-Video-R1
https://arxiv.org/abs/2504.11914
https://github.com/policy-gradient/GRPO-Zero
https://github.com/linkangheng/PR1
https://github.com/jiangxinke/Agentic-RAG-R1
https://github.com/shangshang-wang/Tina
https://github.com/aliyun/qwen-dianjin
https://github.com/RAGEN-AI/RAGEN
https://github.com/XiaomiMiMo/MiMo
https://github.com/yuanzhoulvpi2017/nano_rl
https://huggingface.co/a-m-team/AM-Thinking-v1
https://huggingface.co/Intelligent-Internet/II-Medical-8B
https://github.com/CSfufu/Revisual-R1

↥ back to top

推理 Open o3

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

↥ back to top

小语言模型 Small Language Model

https://github.com/jiahe7ay/MINI_LLM
https://github.com/jingyaogong/minimind
https://github.com/DLLXW/baby-llama2-chinese
https://github.com/charent/ChatLM-mini-Chinese
https://github.com/wdndev/tiny-llm-zh
https://github.com/Tongjilibo/build_MiniLLM_from_scratch
https://github.com/jzhang38/TinyLlama
https://github.com/AI-Study-Han/Zero-Chatgpt
https://github.com/loubnabnl/nanotron-smol-cluster (使用Cosmopedia训练cosmo-1b)
https://github.com/charent/Phi2-mini-Chinese
https://github.com/allenai/OLMo
https://github.com/keeeeenw/MicroLlama
https://github.com/Chinese-Tiny-LLM/Chinese-Tiny-LLM
https://github.com/leeguandong/MiniLLaMA3
https://github.com/Pints-AI/1.5-Pints
https://github.com/zhanshijinwat/Steel-LLM
https://github.com/RUC-GSAI/YuLan-Mini
https://github.com/Om-Alve/smolGPT
https://github.com/skyzh/tiny-llm
https://github.com/qibin0506/Cortex
https://github.com/huggingface/picotron
https://github.com/Alic-Li/Mini_RWKV_7

↥ back to top

小多模态模型 Small Vision Language Model

https://github.com/jingyaogong/minimind-v
https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/train_llava
https://github.com/AI-Study-Han/Zero-Qwen-VL
https://github.com/Coobiw/MPP-LLaVA
https://github.com/qnguyen3/nanoLLaVA
https://github.com/TinyLLaVA/TinyLLaVA_Factory
https://github.com/ZhangXJ199/TinyLLaVA-Video
https://github.com/Emericen/tiny-qwen
https://github.com/merveenoyan/smol-vision
https://github.com/huggingface/nanoVLM
https://github.com/GeeeekExplorer/nano-vllm
https://github.com/ritabratamaiti/AnyModal
https://github.com/yujunhuics/Reyes

↥ back to top

技巧 Tips

What We Learned from a Year of Building with LLMs (Part I)
What We Learned from a Year of Building with LLMs (Part II)
What We Learned from a Year of Building with LLMs (Part III): Strategy
轻松入门大语言模型（LLM）
LLMs for Text Classification: A Guide to Supervised Learning
Unsupervised Text Classification: Categorize Natural Language With LLMs
Text Classification With LLMs: A Roundup of the Best Methods
LLM Pricing
Uncensor any LLM with abliteration
Tiny LLM Universe
Zero-Chatgpt
Zero-Qwen-VL
finetune-Qwen2-VL
MPP-LLaVA
build_MiniLLM_from_scratch
Tiny LLM zh
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.
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
Knowledge distillation: Teaching LLM’s with synthetic data
Part 1: Methods for adapting large language models
Part 2: To fine-tune or not to fine-tune
Part 3: How to fine-tune: Focus on effective datasets
Reader-LM: Small Language Models for Cleaning and Converting HTML to Markdown
LLMs应用构建一年之心得
LLM训练-pretrain
pytorch-llama: LLaMA 2 implemented from scratch in PyTorch.
Preference Optimization for Vision Language Models with TRL 【support model】
Fine-tuning visual language models using SFTTrainer 【docs】
A Visual Guide to Mixture of Experts (MoE)
Role-Playing in Large Language Models like ChatGPT
Distributed Training Guide: Best practices & guides on how to write distributed pytorch training code.
Chat Templates
Top 20+ RAG Interview Questions
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
o1 isn’t a chat model (and that’s the point)
Beam Search快速理解及代码解析
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
The Ultra-Scale Playbook: Training LLMs on GPU Clusters

↥ back to top

贡献者:

如果你觉得本项目对你有帮助，欢迎引用：

@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}},
}

Alt

Contents

数据 Data

微调 Fine-Tuning

推理 Inference

评估 Evaluation

体验 Usage

知识库 RAG

智能体 Agents

代码 Coding

视频 Video

模型

编辑

训练

推理

实用工具

图片 Image

模型

编辑

训练

评估

搜索 Search

语音 Speech

TTS

STT

统一模型 Unified Model

书籍 Book

课程 Course

教程 Tutorial

论文 Paper

社区 Community

模型上下文协议 MCP

推理 Open o1

推理 Open o3

小语言模型 Small Language Model

小多模态模型 Small Vision Language Model

技巧 Tips