TranslatedDive into LLMs: a series of hands-on programming tutorials
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In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Translated📚 Building large models from scratch
Translated"开源大模型食用指南" (Open-Source Large Model Usage Guide): a tutorial tailor-made for Chinese users on quickly fine-tuning (full-parameter/LoRA) and deploying domestic and international open-source large language models (LLM) and multimodal large models (MLLM) in a Linux environment
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
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 model family and using them on various provider services
TranslatedThis project is a tutorial on developing large model applications, aimed at beginner developers. Read it online at: https://datawhalechina.github.io/llm-universe/
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
TranslatedLarge language model knowledge sharing that everyone can understand, a must-read before LLM spring/autumn recruitment interviews, so you can talk confidently with interviewers
- llm
- interview-questions
- interview-practice
- agent
Collection of notebooks about quantitative finance, with interactive python code.
- quantitative-finance
- jupyter-notebooks
- kalman-filter
- option-pricing
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
TranslatedNeeding only a Python foundation, build large language models from scratch; build GLM4\Llama3\RWKV6 step by step from scratch, to gain a deep understanding of how large models work
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
- agentic-ai
- agentic-rag
- agents
- rag
TranslatedSharing AI Infra knowledge & code exercises: PyTorch, vLLM/SGLang, introductions to the slime/vime frameworks⚡️, performance acceleration🚀, large model fundamentals🧠, AI software and hardware🔧, and more
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
- gpu-acceleration
- large-language-models
- llm
- llm-inference
Everything you need to know to build your own RAG application
- agentic-rag
- chatbot
- llm
- machine-learning
HuggingLLM, Hugging Future.
LLM Finetuning with peft
- falcon
- fine-tuning
- huggingface
- llama
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano
TranslatedLLM teaching material for high school students: based on Karpathy's microgpt, for Google Colab