AirLLM 2.8T LLM inference with single 4GB GPU
《动手学大模型Dive into LLMs》系列编程实践教程
21 Lessons, Get Started Building with Generative AI
Covers pre-training data, Tokenizer, Transformer, MoE,distributed training, Scaling Laws, inference & alignment .6 progressive code assignments for full-stack LLM learning | 涵盖预训练数据、分词器、Transformer、MoE、分布式训练、缩放定律、推理与对齐,6 项渐进代码作业,掌握 LLM 全栈知识
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Examples and guides for using the OpenAI API
📚 从零开始构建大模型
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调(全参数/Lora)、部署国内外开源大模型(LLM)/多模态大模型(MLLM)教程
Anthropic's Interactive Prompt Engineering Tutorial
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Developer Asset Hub for NVIDIA Nemotron — A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples to build with Nemotron models
LLM algorithm practice lab with theory, solutions, and test cases.《大模型算法与系统教程》面向大模型入门到进阶的算法实战教程,覆盖原理讲解、答案解析、测试用例与 CUDA/Triton 实战。
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
A collection of guides and examples for the Gemma open models from Google.
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
Jupyter notebooks and a video playlist that teach retrieval augmented generation (RAG) step by step, from indexing to generation.
A collection of guides and code examples for building AI applications with open models on Nebius Token Factory.