happy-llm and llm-universe

Both are tutorials from Datawhale China, with the former providing a foundational understanding of LLM principles and practices, while the latter focuses on the application development of large models for beginners, making them complementary resources in the LLM learning ecosystem.

happy-llm
59
Established
llm-universe
48
Emerging
Maintenance 13/25
Adoption 10/25
Maturity 16/25
Community 20/25
Maintenance 10/25
Adoption 10/25
Maturity 8/25
Community 20/25
Stars: 27,292
Forks: 2,515
Downloads:
Commits (30d): 1
Language: Jupyter Notebook
License:
Stars: 12,159
Forks: 1,262
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
No Package No Dependents
No License No Package No Dependents

About happy-llm

datawhalechina/happy-llm

📚 从零开始的大语言模型原理与实践教程

This project is a comprehensive learning guide for building large language models (LLMs) from scratch. It takes you from understanding core NLP concepts to designing, training, and fine-tuning your own LLM, like LLaMA2. It's intended for students, researchers, and AI enthusiasts who want to grasp the inner workings of LLMs and develop practical skills.

natural-language-processing large-language-models machine-learning-engineering deep-learning AI-research

About llm-universe

datawhalechina/llm-universe

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/

This project offers a practical, step-by-step tutorial for building applications powered by Large Language Models (LLMs). It guides you through creating a personal knowledge base assistant, taking you from understanding LLM basics to deploying a functional application. This is for developers with basic Python skills who want to quickly learn how to integrate LLM APIs into their projects.

LLM-application-development personal-knowledge-base API-integration RAG-systems application-deployment

Scores updated daily from GitHub, PyPI, and npm data. How scores work