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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.
| Date | Stars |
|---|---|
| 2026-07-31 | 354 |
| 2026-08-03 | 353 |
| 2026-08-06 | 353 |
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# llm-origin 本文将 LLM 知识分为以下七个部分，并整理了相关资源： * **LLM Overview**：汇总有关 LLM 的综述和系统课程。 * **LLM Fundamentals**：学习 LLM 之前需要掌握的基础知识，包括数学、Python、深度学习框架、机器学习、深度学习和NLP等。 * **Create LLM**：专注于创建和优化 LLM 所需的技术。 * **Use LLM**：专注于开发基于 LLM 的应用并进行部署。 * **LLM Applications**：基于 LLM 构建的应用。 * **面试准备**：整理与面试相关的八股文和面经。 * **其他**：如语音、视频等。 ## LLM Overview 汇总LLM相关的综述、系统课程。 ### Survey ### Course * [llm-course](https://github.com/mlabonne/llm-course)(32.1k) by mlabonne: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. [mlabonne.github.io/blog](https://mlabonne.github.io/blog/). * [LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch)(17.2k): Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step. * [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM)(15.2k): a curated list of Large Language Model. * [llm-cookbook](https://github.com/datawhalechina/llm-cookbook)(9.9k): 面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版. * [LLMSurvey](https://github.com/RUCAIBox/LLMSurvey)(9.2k): The official GitHub page for the survey paper "A Survey of Large Language Models". * [LLMsPracticalGuide](https://github.com/Mooler0410/LLMsPracticalGuide)(8.8k): A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers) * [llm-action](https://github.com/liguodongiot/llm-action)(6.9k): 本项目旨在分享大模型相关技术原理以及实战经验。 ## LLM Fundamentals ### 数学 ### Python ### 深度学习框架 ### 机器学习 ### 深度学习 ### NLP ## Create LLM ### Transformer Attention Is All You Need(2017), [paper](https://arxiv.org/pdf/1706.03762), [arxiv](https://arxiv.org/abs/1706.03762). **Understand:** * [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) by Jay Alammar: A visual and intuitive explanation of the Transformer model. * [Visual intro to Transformers](https://www.youtube.com/watch?v=wjZofJX0v4M&t=187s) by 3Blue1Brown: Simple easy to understand visual intro to Transformers. * [可视化注意力，变形金刚的心脏 | 第 6 章，深度学习](https://www.youtube.com/watch?v=eMlx5fFNoYc) by 3Blue1Brown. * [Attention? Attention!](https://lilianweng.github.io/posts/2018-06-24-attention/) by Lilian Weng: Introduce the need for attention in a more formal way. * [annotated-transformer](https://github.com/harvardnlp/annotated-transformer)(5.2k) by harvardnlp: An annotated implementation of the Transformer paper. [The Annotated Transformer](https://nlp.seas.harvard.edu/annotated-transformer/) blog. * [How-to-use-Transformers](https://github.com/jsksxs360/How-to-use-Transformers)(732): Transformers 库快速入门教程。[transformers快速入门](https://transformers.run/) blog。 **Use:** * [huggingface/transformers 127k](https://github.com/huggingface/transformers), [huggingface.co/transformers](https://huggingface.co/docs/transformers/index), Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. ### LLM architecture 对于Transformer架构，需要对其输入（tokens）和输出（logits）有一个良好的理解。注意力机制是另一个需要掌握的关键组件，因为其改进版本会在后续介绍。 * **总览**：理解编码器-解码器Transformer架构，特别是decoder-only GPT架构，它在每个现代大语言模型中使用。 * **分词(Tokenization)**：了解如何将原始文本数据转换为模型能够理解的格式，这涉及将文本拆分为tokens（通常是单词或子词）。 * **注意力机制**：掌握注意力机制背后的理论，包括自注意力和缩放点积注意力，这些机制使模型在生成输出时能够关注输入的不同部分。 * **文本生成**：了解模型生成输出序列的不同方式。常见的策略包括贪婪解码、束搜索(beam search)、top-k采样和核采样(nucleus sampling)。 **References:** * [The Illustrated GPT-2](https://jalammar.github.io/illustrated-gpt2/) by Jay Alammar: focused on the GPT architecture, which is very similar to Llama's. * [LLM Visualization](https://bbycroft.net/llm) by Brendan Bycroft: Incredible 3D visualization of what happens inside of an LLM. * [nanoGPT](https://www.youtube.com/watch?v=kCc8FmEb1nY) by Andrej Karpathy: A 2h-long YouTube video to reimplement GPT from scratch (for programmers). * [Decoding Strategies in LLMs](https://mlabonne.github.io/blog/posts/2023-06-07-Decoding_strategies.html): Provide code and a visual introduction to the different decoding strategies to generate text. ### 数据集(Dataset) * [firecrawl](https://github.com/mendableai/firecrawl)(5.3k) by mendableai: Turn entire websites into LLM-r
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e5def987c1de8ece, llm:Repository description (Chinese): 'llm相关内容，包括：基础知识、八股文、面经、经典论文' — translates to 'LLM related content, including: fundamentals, common templates, interview questions, classic papers.'