Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
| Date | Stars |
|---|---|
| 2026-07-31 | 24830 |
| 2026-08-01 | 24830 |
| 2026-08-02 | 24845 |
| 2026-08-06 | 24845 |
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<p align="center"> <img src="https://github.com/liguodongiot/llm-action/blob/main/pic/llm-action-v4.jpg" > </p> <p> <a href="https://github.com/liguodongiot/llm-action/stargazers"> <img src="https://img.shields.io/github/stars/liguodongiot/llm-action?style=social" > </a> <a href="https://github.com/liguodongiot/llm-action/blob/main/pic/wx.jpg"> <img src="https://img.shields.io/badge/吃果冻不吐果冻皮-1AAD19.svg?style=plastic&logo=wechat&logoColor=white" > </a> <a href="https://www.zhihu.com/people/liguodong-iot"> <img src="https://img.shields.io/badge/吃果冻不吐果冻皮-0079FF.svg?style=plastic&logo=zhihu&logoColor=white"> </a> <a href="https://juejin.cn/user/3642056016410728"> <img src="https://img.shields.io/badge/掘金-吃果冻不吐果冻皮-000099.svg?style=plastic&logo=juejin"> </a> <a href="https://liguodong.blog.csdn.net/"> <img src="https://img.shields.io/badge/CSDN-吃果冻不吐果冻皮-6B238E.svg"> </a> <a href="https://www.lab4ai.cn/register?agentID=user-PqCML6LJZO"> <img src="https://img.shields.io/badge/Lab4AI-大模型实验室-1E90FF.svg"> </a> </p> ## 目录 - :snail: [LLM训练](#llm训练) - 🐫 [LLM训练实战](#llm训练实战) - 🐼 [LLM参数高效微调技术原理](#llm微调技术原理) - 🐰 [LLM参数高效微调技术实战](#llm微调实战) - 🐘 [LLM分布式训练并行技术](#llm分布式训练并行技术) - 🌋 [分布式AI框架](#分布式ai框架) - 📡 [分布式训练网络通信](#分布式训练网络通信) - :herb: [LLM训练优化技术](#llm训练优化技术) - :hourglass: [LLM对齐技术](#llm对齐技术) - 🐎 [LLM推理](#llm推理) - 🚀 [LLM推理框架](#llm推理框架) - ✈️ [LLM推理优化技术](#llm推理优化技术) - ♻️ [LLM压缩](#llm压缩) - 📐 [LLM量化](#llm量化) - 🔰 [LLM剪枝](#llm剪枝) - 💹 [LLM知识蒸馏](#llm知识蒸馏) - ♑️ [低秩分解](#低秩分解) - :herb: [LLM测评](#llm测评) - 🔯 [LLM效果评测](#llm效果评测) - 🔘 [LLM推理性能压测](#llm推理性能压测) - :palm_tree: [LLM数据工程](#llm数据工程) - :dolphin: [LLM微调高效数据筛选技术](#llm微调高效数据筛选技术) - :cyclone: [提示工程](#提示工程) - ♍️ [LLM算法架构](#llm算法架构) - :jigsaw: [LLM应用开发](#llm应用开发) - 🀄️ [LLM国产化适配](#llm国产化适配) - 🔯 [AI编译器](#ai编译器) - 🔘 [AI基础设施](#ai基础设施) - :maple_leaf: [AI加速卡](#ai加速卡) - :octocat: [AI集群网络通信](#ai集群网络通信) - 💟 [LLMOps](#llmops) - 🍄 [LLM生态相关技术](#llm生态相关技术) - 💹 [LLM性能分析](#llm性能分析) - :dizzy: [LLM面试题](#llm面试题) - 🔨 [服务器基础环境安装及常用工具](#服务器基础环境安装及常用工具) - 💬 [LLM学习交流群](#llm学习交流群) - 👥 [微信公众号](#微信公众号) - ⭐️ [Star History](#star-history) - :link: [AI工程化课程推荐](#ai工程化课程推荐) ## LLM训练 ### LLM训练实战 下面汇总了我在大模型实践中训练相关的所有教程。从6B到65B，从全量微调到高效微调（LoRA，QLoRA，P-Tuning v2），再到RLHF（基于人工反馈的强化学习）。 | LLM | 预训练/SFT/RLHF... | 参数 | 教程 | 代码 | | --------------------------- | ----------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | | Alpaca | full fine-turning | 7B | [从0到1复现斯坦福羊驼（Stanford Alpaca 7B）](https://zhuanlan.zhihu.com/p/618321077) | [配套代码](https://github.com/liguodongiot/llm-action/tree/main/llm-train/alpaca) | | Alpaca(LLaMA) | LoRA | 7B~65B | 1.[足够惊艳，使用Alpaca-Lora基于LLaMA(7B)二十分钟完成微调，效果比肩斯坦福羊驼](https://zhuanlan.zhihu.com/p/619426866)<br>2. [使用 LoRA 技术对 LLaMA 65B 大模型进行微调及推理](https://zhuanlan.zhihu.com/p/632492604) | [配套代码](https://github.com/liguodongiot/llm-action/tree/main/llm-train/alpaca-lora) | | BELLE(LLaMA/Bloom) | full fine-turning | 7B | 1.[基于LLaMA-7B/Bloomz-7B1-mt复现开源中文对话大模型BELLE及GPTQ量化](https://zhuanlan.zhihu.com/p/618876472) <br> 2. [BELLE(LLaMA-7B/Bloomz-7B1-mt)大模型使用GPTQ量化后推理性能测试](https://zhua
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:670dd3783c6612ec, topic:llm-inference, topic:llm-serving
matched fp:670dd3783c6612ec, topic:llmops
matched fp:670dd3783c6612ec, topic:llm