共 3 条相关资源。
GitHub 40k+ star 的大模型学习路线图：按 Fundamentals → LLM Scientist → LLM Engineer 三阶段组织，每步附论文、Colab 与资源清单，从原理到量化部署全链路覆盖。A 40k+ star LLM learning roadmap organized in three tiers: Fundamentals, LLM Scientist and LLM Engineer, with papers and Colabs for every step.
Datawhale 出品的中文 LLM 应用开发教程：从 API 调用到 Prompt 工程、RAG 知识库、Vector 检索、LangChain 应用与部署，全程围绕一个「知识库助手」项目实战。Datawhale's Chinese LLM application course: from API calls to prompt engineering, RAG, vector search and LangChain, built around one knowledge-base assistant project.
Datawhale 的开源大模型部署手册：环境配置、Llama/Qwen/ChatGLM 等主流开源模型的部署、推理、LoRA 微调全流程，中文环境踩坑指南性质极强。A Chinese hands-on guide to deploying open-source LLMs: environment setup, inference and LoRA fine-tuning for Llama, Qwen, ChatGLM and more.