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LLM全栈优质资源汇总
| Date | Stars |
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| 2026-07-31 | 724 |
| 2026-08-01 | 724 |
| 2026-08-06 | 724 |
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# llm-resource（LLM 百宝箱） LLM全栈优质资源汇总 > 非常欢迎大家也参与进来，收集更多优质大模型相关资源。 ## 目录 - 🐼 [LLM算法](#llm算法) - 🐘 [LLM训练](#llm训练) - 🐘 [LLM微调](#llm微调) - 🐼 [LLM对齐](#llm对齐) - 🔥 [LLM推理](#llm推理) - :palm_tree: [LLM数据工程（Data Engineering）](#llm数据工程) - 📡 [LLM压缩](#llm压缩) - 🐰 [LLM测评](#llm测评) - 🐘 [AI基础知识](#ai基础知识) - 📡 [AI基础设施](#ai基础设施) - :palm_tree: [AI芯片](#ai芯片) - 🐰 [CUDA](#cuda) - 🐘 [AI编译器](#ai编译器) - 🐰 [AI框架](#ai框架) - 📡 [LLM应用开发](#llm应用开发) - 🐘 [LLMOps](#llmops) - 📡 [LLM实践](llm实践) - 📡[微信公众号文章集锦](#微信公众号文章集锦) ## LLM算法 ### Transformer 原理： - [Transformer模型详解（图解最完整版](https://zhuanlan.zhihu.com/p/338817680) - [OpenAI ChatGPT（一）：十分钟读懂 Transformer](https://zhuanlan.zhihu.com/p/600773858) - [Transformer的结构是什么样的？各个子模块各有什么作用？](https://blog.csdn.net/m0_54929869/article/details/118881804) - [以Transformer结构为基础的大模型参数量、计算量、中间激活以及KV cache剖析](https://mp.weixin.qq.com/s/3JYz6yrLeBr5ujip3LZe6w) - [Transformer 一起动手编码学原理](https://mp.weixin.qq.com/s/NgUNuWhvp2SqG-XWYv2PGQ) - [为什么transformer(Bert)的多头注意力要对每一个head进行降维？](http://www.sniper97.cn/index.php/note/deep-learning/note-deep-learning/4002/) - [Decoder-Only Transformers: The Workhorse of Generative LLMs](https://cameronrwolfe.substack.com/p/decoder-only-transformers-the-workhorse) 源码： - [OpenAI ChatGPT（一）：Tensorflow实现Transformer](https://zhuanlan.zhihu.com/p/603243890) - [OpenAI ChatGPT（一）：十分钟读懂 Transformer](https://zhuanlan.zhihu.com/p/600773858) - [GPT （一）transformer原理和代码详解](https://zhuanlan.zhihu.com/p/632880248) - [Transformer源码详解（Pytorch版本）](https://zhuanlan.zhihu.com/p/398039366) - [搞懂Transformer结构，看这篇PyTorch实现就够了](https://zhuanlan.zhihu.com/p/339207092) ### GPT1 ### GPT2 - GPT2 源码：https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py - GPT2 源码解析：https://zhuanlan.zhihu.com/p/630970209 - nanoGPT：https://github.com/karpathy/nanoGPT/blob/master/model.py - 7.3 GPT2模型深度解析：http://121.199.45.168:13013/7_3.html - GPT（三）GPT2原理和代码详解: https://zhuanlan.zhihu.com/p/637782385 - GPT2参数量剖析: https://zhuanlan.zhihu.com/p/640501114 ### ChatGPT - [State of GPT：大神Andrej揭秘OpenAI大模型原理和训练过程](https://mp.weixin.qq.com/s/zmEGzm1cdXupNoqZ65h7yg) - [OpenAI联合创始人亲自上场科普GPT，让技术小白也能理解最强AI](https://mp.weixin.qq.com/s/MD4WwwJLXm8rEm-sniX8Gw) ### GLM - [预训练语言模型：GLM](https://zhuanlan.zhihu.com/p/641499380) ### LLaMA ### MOE 大模型 - [Mixtral-8x7B MoE大模型微调实践，超越Llama2-65B](https://mp.weixin.qq.com/s?__biz=MzU3Mzg5ODgxMg==&mid=2247486657&idx=1&sn=c5a5e55b01243f477d063c9194d24f42&chksm=fd3be592ca4c6c84bf5eefff23dcc38eeb83624e9f53bbd9a72afba71e235dddf814549322ba&token=499509118&lang=zh_CN#rd) - [大模型分布式训练并行技术（八）-MOE并行](https://mp.weixin.qq.com/s?__biz=MzU3Mzg5ODgxMg==&mid=2247486145&idx=1&sn=299c28153b286465be26e18153c6db5d&chksm=fd3be392ca4c6a84be283dad80f584443302ea29fc95744f83727e7d9d68952d3a0f8b1b66d5&token=499509118&lang=zh_CN#rd) - [MoE架构模型爆发或将带飞国产AI芯片](https://mp.weixin.qq.com/s?__biz=MzU3Mzg5ODgxMg==&mid=2247488422&idx=1&sn=eeb18ec0f5b9e972df31d65e7db13f8f&chksm=fd3bfaf5ca4c73e38a696fe7b6f33a30af962fdddfabd92d74b1d06190442759aabe7b560f22&token=499509118&lang=zh_CN#rd) - [大模型的模型融合方法概述](https://mp.weixin.qq.com/s?__biz=MzU3Mzg5ODgxMg==&mid=2247487652&idx=1&sn=1bbf692b6e1dc6bae719c8e0a10293a0&chksm=fd3bf9f7ca4c70e16473a98d5408f6daea5e8c116a88cb3f41dfb00ffb7f6016874ee092224c&token=499509118&lang=zh_CN#rd) - [混合专家模型 (MoE) 详解](https://zhuanlan.zhihu.com/p/674698482) - [群魔乱舞：MoE大模型详解](https://zhuanlan.zhihu.com/p/677638939) - [大模型LLM之混合专家模型MoE（上-基础篇）](https://zhuanlan.zhihu.com/p/672712751) - [大模型LLM之混合专家模型MoE（下-实现篇）](https://zhuanlan.zhihu.com/p/673048264) ### 下一代大模型 - https://github.com/NExT-GPT/NExT-GPT - https://next-gpt.github.io/ - [Introduction to NExT-GPT: Any-to-Any Multimodal Large Language Model](https://www.kdnuggets.com/introduction-to-nextgpt-anytoany-multimodal-large-language-model) ### 多模态大模型 A Survey on Multimodal Large Language Models：https://arxiv.org/pdf/2306.13549 Efficient-Multimodal-LLMs-Survey：https://github.com/lijiannuist/Efficient-
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matched fp:63bf0b51885b73e9, topic:llmops
matched fp:63bf0b51885b73e9, topic:llm