datawhalechina / awesome-compressionLinks
模型压缩的小白入门教程,PDF下载地址 https://github.com/datawhalechina/awesome-compression/releases
☆334Updated 4 months ago
Alternatives and similar repositories for awesome-compression
Users that are interested in awesome-compression are comparing it to the libraries listed below
Sorting:
- 深度学习系统笔记,包含深度学习数学基础知识、神经网络基础部件详解、深度学习炼丹策略、模型压缩算法详解。☆498Updated 4 months ago
- 大模型/LLM推理和部署理论与实践☆352Updated 3 months ago
- MindSpore online courses: Step into LLM☆477Updated 2 months ago
- 通过带领大家解读Transformer模型来加深对模型的理解☆218Updated 4 months ago
- LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.☆829Updated last month
- 解锁HuggingFace生态的百般用法☆93Updated 10 months ago
- 校招、秋招、春招、实习好项目,带你从零动手实现支持LLama2/3和Qwen2.5的大模型推理框架。☆439Updated 3 months ago
- 看图学大模型☆320Updated last year
- TinyRAG☆353Updated 3 months ago
- 一个很小很小的RAG系统☆306Updated 5 months ago
- LLM全栈优质资源汇总☆640Updated 3 months ago
- a chinese tutorial of git☆157Updated last year
- LLM Tokenizer with BPE algorithm☆43Updated last year
- wow-fullstack,令人惊叹的全栈开发教程☆215Updated 2 weeks ago
- LLM大模型(重点)以及搜广推等 AI 算法中手写的面试题,(非 LeetCode),比如 Self-Attention, AUC等,一般比 LeetCode 更考察一个人的综合能力,又更贴近业务和基础知识一点☆402Updated 9 months ago
- LLM/MLOps/LLMOps☆118Updated 5 months ago
- 《李宏毅生成式人工智能教程》,PDF下载地址:https://github.com/datawhalechina/leegenai-tutorial/releases☆185Updated 2 months ago
- ☆299Updated last year
- LLM101n: Let's build a Storyteller 中文版☆135Updated last year
- A simple and trans-platform rag framework and tutorial☆217Updated last month
- 关于Transformer模型的最简洁pytorch实现,包含详细注释☆217Updated last year
- A simple and trans-platform agent framework and tutorial☆178Updated last week
- ☆309Updated 5 months ago
- ☆78Updated last year
- 从0开始,将chatgpt的技术路线跑一遍。☆264Updated last year
- 从零实现一个小参数量中文大语言模型。☆856Updated last year
- 《动手学深度学习》习题解答,在线阅读地址如下:☆520Updated last year
- 大模型技术栈一览☆117Updated last year
- Inference code for LLaMA models☆127Updated 2 years ago
- yolo master 本课程主要对yolo系列模型进行介绍,包括各版本模型的结构,进行的改进等,旨在帮助学习者们可以了解和掌握主要yolo模型的发展脉络,以期在各自的应用领域可以进一步创新并在自己的任务上达到较好的效果。☆231Updated 3 months ago