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Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Learn how to design, develop, deploy and iterate on production-grade ML applications.
12 Weeks, 24 Lessons, AI for All!
《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
A simple screen parsing tool towards pure vision based GUI agent
A guidance language for controlling large language models.
Instruct-tune LLaMA on consumer hardware
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases
AISystem 主要是指AI系统，包括AI芯片、AI编译器、AI推理和训练框架等AI全栈底层技术
llama3 implementation one matrix multiplication at a time
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
《动手学大模型Dive into LLMs》系列编程实践教程
QLoRA: Efficient Finetuning of Quantized LLMs
An annotated implementation of the Transformer paper.
NVIDIA Isaac GR00T N1.6 - A Foundation Model for Generalist Robots.
AIInfra（AI 基础设施）指AI系统从底层芯片等硬件，到上层软件栈支持AI大模型训练和推理。
每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
🎓 无需编写任何代码即可轻松创建漂亮的学术网站 Easily create a beautiful academic résumé or educational website using Hugo and GitHub. No code.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…
Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads