$ account datawhalechina
This repository is a large-scale educational resource and community showcase for building LLM-powered agents using the custom 'HelloAgents' framework. It is organized as a multi-chapter tutorial (16 chapters) covering agent fundamentals, tool use, memory systems, multi-agent protocols (MCP/A2A/ANP), context engineering, reinforcement learning (SFT→GRPO), and evaluation. Each chapter contains runnable code examples, and the repository also hosts 40+ community-contributed 'Co-creation-projects' that demonstrate real-world agent applications across domains including stock analysis, health record management, academic research, code assistance, game design, and data analysis. The architecture is not a single deployable system but a polyglot collection of independent projects, most following a FastAPI backend + Vue/React frontend pattern, with a shared dependency on the HelloAgents framework core. The primary data flow in each project is: user input → LLM reasoning loop (ReAct/Plan-and-Solve/Reflection) → tool execution (search, database, code execution, APIs) → memory/context update → response generation. External dependencies include OpenAI-compatible LLM APIs, search engines (Tavily, Brave, DuckDuckGo), vector stores (Qdrant, Milvus, Neo4j), and various domain-specific APIs (financial data, academic databases, weather). The codebase serves as both a learning path for agent development and a practical reference for common agent patterns, though quality varies significantly between the polished tutorial chapters and the prototype-level community projects.
This repository is an extensive, open-source educational tutorial on building and training large language models (LLMs) from scratch, maintained by the Datawhale community. It covers the full pipeline: NLP fundamentals, Transformer architecture (with a custom Tiny-K implementation), pretraining, supervised fine-tuning (SFT), parameter-efficient fine-tuning (LoRA), reinforcement learning (GRPO, OPD, Search-R1, ReTool), and practical applications like RAG and Agent systems. The code is organized into chapters with clear progression, and each chapter includes runnable scripts, configuration files, and detailed documentation in Chinese. While primarily educational, the repository contains several well-crafted, reusable components such as a modular Transformer implementation, efficient dataset loaders, a RAG pipeline, an Agent framework, and RL training loops with distributed support. The project is actively maintained with recent additions (Extra-Chapter) and has a large community following. However, reproducibility is moderate due to fragmented dependency files, hardcoded paths, and reliance on external services (e.g., DeepSeek Search, SwanLab). The code is not production-grade but serves as an excellent learning resource and a source of reusable patterns for LLM research and development.
This repository is a comprehensive collection of tutorials and code examples for deploying, fine-tuning, and integrating open-source large language models (LLMs), with a strong focus on the Chinese LLM ecosystem. It covers over 50 models (Qwen, ChatGLM, DeepSeek, InternLM, etc.) and provides step-by-step guides for setting up FastAPI services, Streamlit web demos, LangChain knowledge base assistants, and LoRA/QLoRA fine-tuning using PEFT. The repository also includes example applications such as role-playing chatbots and digital persona creation. While primarily educational, the code patterns are directly applicable to production scenarios, including model serving with vLLM/SGLang, experiment tracking with SwanLab, and deployment on diverse hardware (NVIDIA, AMD ROCm, Ascend NPU, Apple Silicon). The codebase is highly repetitive across models, with significant copy-paste, but the underlying patterns (fine-tuning pipeline, deployment scaffold, LangChain wrapper) are well-established and reusable.
This repository is a companion documentation site for Zhou Zhihua's 'Machine Learning' (Watermelon Book), providing detailed step-by-step formula derivations for all chapters. It is built with VitePress, a static site generator, and deployed to GitHub Pages. The site includes 16 chapters of mathematical derivations, an errata page tracking corrections across print editions, and French translations for the first two chapters. While the content is educational and not a software application in the traditional sense, the repository functions as a VitePress project with custom theme configuration, MathJax rendering, and CI/CD deployment. It is actively maintained by the Datawhale community and serves as a learning resource for machine learning practitioners.
This repository is a comprehensive Chinese-language educational tutorial and codebase for Retrieval-Augmented Generation (RAG) systems, created by Datawhale. It systematically covers the full RAG stack from fundamentals (data loading, chunking, embeddings) to advanced techniques (hybrid search, query construction, Text2SQL, reranking, agentic RAG, and Graph RAG). The code is organized by chapter, with runnable Python scripts demonstrating each concept using popular frameworks like LangChain, LlamaIndex, and LangGraph, alongside vector databases (Milvus, FAISS) and graph databases (Neo4j). While primarily an educational resource, the repository contains several well-crafted, reusable components such as a minimal Text2SQL agent framework, a clean LangGraph-based agentic RAG pattern, and a custom ColBERT reranker. The practical application is a Chinese recipe Q&A system ('尝尝咸淡'), which serves as a consistent domain for demonstrating various RAG techniques. The codebase is hybrid in nature, blending structured educational content with functional demo applications. The repository is actively maintained with a large community following. However, users should be aware of technical drift risks including vendored model code, hardcoded API dependencies, and the presence of AI-generated code sections. The core value lies in its structured curriculum and reusable RAG patterns rather than as a production-ready system.
This repository is a curated, interactive learning roadmap for building AI agents. It is not a software library or framework, but a single-page web application (index.html) that renders a comprehensive todo list, project ladder, and curated resource collection. The application allows users to track progress via checkboxes stored in localStorage, take Markdown notes per stage, search content, toggle dark mode, and navigate via a sidebar. The underlying data is a static JSON structure embedded in the JavaScript. The repo's primary value is its meticulously organized learning path covering agent loops, tool use, RAG, multi-agent coordination, skills/protocols, browser agents, evaluation, and production deployment. It references dozens of high-quality external resources (official docs, papers, open-source projects) and provides clear learning principles. While the repo contains no runnable agent code, the interactive frontend is a well-crafted tool for self-paced learning. The codebase is a hybrid: it is an application (web UI) but its content is a curated research/learning resource.
This repository is an educational collection of minimal, from-scratch implementations of core LLM and generative AI systems, maintained by DataWhale. It is not a single production application; rather, it is a set of self-contained 'Tiny' sub-projects covering a small LLM trained on TinyStories, a RAG pipeline, a ReAct agent, an LLM evaluation harness, a DDPM diffusion model, a Transformer, a GraphRAG system, and an image-based RAG pipeline. Each sub-project has its own README, requirements, and runnable code or notebooks, with heavy Chinese-language comments explaining the underlying theory. The code is intentionally simple and readable rather than production-hardened, and it serves as a teaching resource for understanding how these systems work internally.
This repository is a curated collection of baseline solutions for data science competitions, primarily from Chinese platforms (科大讯飞 AI开发者大赛, Tianchi, DataFountain, AIWIN, etc.) and some international ones (Kaggle). It contains hundreds of Jupyter notebooks and Python scripts covering a wide range of tasks: structured data classification/regression (LightGBM, XGBoost), computer vision (ResNet, EfficientNet, YOLO, CRNN), natural language processing (BERT, TF-IDF, Qwen), time series forecasting, and multimodal challenges. Each baseline is self-contained, providing a quick-start pipeline from data loading to submission generation. The repository serves as a practical learning resource for competition participants, demonstrating common feature engineering techniques, model training loops, and ensemble strategies. However, the code quality is uneven: many notebooks are exploratory with hardcoded paths, outdated library versions, and minimal error handling. The repo lacks centralized dependency management, environment definitions, or modular reusable libraries, making reproducibility low without manual adaptation. Despite these limitations, it offers a valuable catalog of patterns and templates that can be adapted for new competitions.
This repository is a static website for a Chinese-language educational course titled 'Deep Agents 实战' (Deep Agents in Action). It provides 16 chapters of technical content on building AI agents using LangChain and LangGraph, including text, video links, and downloadable PDFs. The site is built with Astro 6, Tailwind CSS 4, and TypeScript, and is deployed to GitHub Pages via GitHub Actions. The codebase is well-structured: content is authored in Markdown under `content/`, metadata is managed in `scripts/chapters.json`, and a preprocessing script injects frontmatter into the Astro content collection. Custom Astro components handle layout, navigation, theme toggling, and a responsive table of contents. The repository also includes CI/CD workflows for asset validation and cross-platform installation testing. While the primary purpose is educational content delivery, the build and content pipelines demonstrate reusable patterns for static site generation with Astro.
This repository is an educational book/course (DataWhale 'diy-llm') that teaches building and training LLMs from scratch. It spans the full pipeline: tokenizer, Transformer architecture, GPU high-performance programming, scaling laws, data engineering, distributed training, alignment (including GRPO/RLVR), evaluation, and multimodal models. The content is primarily Chinese, with Jupyter notebooks and code snippets embedded in markdown chapters. It is not a production library or application; it is a tutorial/textbook with homework assignments.
This is a Chinese-language educational companion to DeepLearning.AI's 'Agentic AI' course by Andrew Ng, maintained by the Datawhale community. The repository is a hybrid learning resource: ~60 translated markdown files across 5 modules (agentic workflow intro, reflection design patterns, tool use, practical building tips, highly autonomous agents) plus executable Jupyter notebooks and supporting Python modules that implement the lab exercises. It is not a production application or service — it is courseware with real, runnable agent code demonstrating current LLM agent patterns.
This repository is an educational tutorial implementation of a stereo visual SLAM (Simultaneous Localization and Mapping) system, based on the classic feature-point method (ORB/GFTT) with optical flow tracking, PnP pose estimation, and local bundle adjustment using g2o. It is structured as a modular C++ library (libmyslam.so) with components for frontend, backend, map management, camera model, dataset reading, and Pangolin-based 3D visualization. The primary entry point is a CLI application that processes KITTI stereo sequences to estimate camera trajectory and sparse 3D map points. While the code demonstrates solid software engineering practices such as threading, singletons, and factory methods, it is not production-ready: it lacks loop closure, map persistence, and comprehensive testing. The repository serves as a hands-on companion to the 'Visual SLAM: From Theory to Practice' textbook, with documentation and chapter-wise code examples. External dependencies include OpenCV, Eigen, Sophus, g2o, Pangolin, and Glog, with version-specific requirements that may cause compatibility issues on modern systems.