llm-action
Aims to share large model technology principles and practical experience (large model engineering, application implementation)
GraphCanon updated 4d · GitHub synced 4d
Decision brief
llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training.
Good fit when
- - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual
- - You require information specifically about deploying and managing large model operations using a framework dedicated to comprehensive coverage of various phases from training to serving.
Avoid when
- - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes.
- - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but不如
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (28d since push)
- As of 4d
- Provenance
- Not a fork · Personal account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/liguodongiot/llm-actionHow it fits your stack(5)
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Evidence and technical details
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Overview
Repository focused on the sharing of knowledge regarding large language models including their engineering, practical deployment, inference, serving, and training.
Capability facts
- Languages
- html
Source: github.language · Aug 16, 2026
Categories
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README
目录
- :snail: LLM训练
- 🐫 LLM训练实战
- 🐼 LLM参数高效微调技术原理
- 🐰 LLM参数高效微调技术实战
- 🐘 LLM分布式训练并行技术
- 🌋 分布式AI框架
- 📡 分布式训练网络通信
- :herb: LLM训练优化技术
- :hourglass: LLM对齐技术
- 🐎 LLM推理
- 🚀 LLM推理框架
- ✈️ LLM推理优化技术
- ♻️ LLM压缩
- 📐 LLM量化
- 🔰 LLM剪枝
- 💹 LLM知识蒸馏
- ♑️ 低秩分解
- :herb: LLM测评
- 🔯 LLM效果评测
- 🔘 LLM推理性能压测
- :palm_tree: LLM数据工程
- :dolphin: LLM微调高效数据筛选技术
- :cyclone: 提示工程
- ♍️ LLM算法架构
- :jigsaw: LLM应用开发
- 🀄️ LLM国产化适配
- 🔯 AI编译器
- 🔘 AI基础设施
- :maple_leaf: AI加速卡
- :octocat: AI集群网络通信
- 💟 LLMOps
- 🍄 LLM生态相关技术
- 💹 LLM性能分析
- :dizzy: LLM面试题
- 🔨 服务器基础环境安装及常用工具
- 💬 LLM学习交流群
- 👥 微信公众号
- ⭐️ Star History
- :link: 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） | 配套代码 |
| 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 2. 使用 LoRA 技术对 LLaMA 65B 大模型进行微调及推理 | 配套代码 |
| 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 2. [BELLE(LLaMA-7B/Bloomz-7B1-mt)大模型使用GPTQ量化后推理性能测试](https://zhua |
For agents
This page has a .md twin and JSON over the API.