8 Best Large-Language-Model-Notebooks-Course Alternatives in 2026 (Open Source)
Large-Language-Model-Notebooks-Course — Practical course about Large Language Models. . Comprehensive free hands-on LLM course with 30+ Jupyter notebooks covering the full stack from prompting to fine-tuning to enterprise architecture — backed by an Apress published book for deeper coverage
These 8 open-source tools do the same job. They are ordered by how closely they match Large-Language-Model-Notebooks-Course, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Large-Language-Model-Notebooks-Course(original) | 1.8k | +7 | 2026-09-29 |
| Intro to the course | 3.4k | +4 | 2024-12-09 |
| Anthropic courses | 22.9k | +468 | 2025-11-13 |
| Generative AI on Google Cloud | 17.8k | +206 | 2026-09-30 |
| Hugging Face Diffusion Models Course | 4.4k | +10 | 2026-09-17 |
| Hands-On-LangChain-for-LLM-Applications-Development | 239 | +3 | 2025-09-28 |
| bRAG-langchain | 4.2k | +17 | 2026-08-03 |
| GenAI_Agents | 24.4k | +580 | 2026-09-28 |
| AI Getting Started | 4.1k | +0 | 2024-06-10 |
1. Intro to the course
🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦
What sets it apart: vs generic LLM tutorials: 3-pipeline production architecture (training + streaming + inference) with real financial data — teaches QLoRA fine-tuning, real-time embeddings, and RAG deployment end-to-end
Best for: ML engineers wanting to learn production LLM deployment end-to-end; Practitioners building real-time RAG systems with streaming data; Teams learning QLoRA fine-tuning with LLMOps best practices
2. Anthropic courses
Anthropic's educational courses
What sets it apart: vs generic prompt engineering guides: official Anthropic courses with hands-on Claude API exercises, covering fundamentals through production evaluation
Best for: Developers learning Anthropic Claude API from scratch; Teams establishing prompt engineering best practices
3. Generative AI on Google Cloud
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
What sets it apart: Google's official sample repository for Generative AI on Google Cloud — the most comprehensive collection of Gemini, Imagen, and Vertex AI notebooks, unlike third-party tutorials it's maintained by Google and always reflects latest APIs
Best for: Learning Google Cloud's generative AI capabilities with hands-on examples; Teams already on Google Cloud wanting to integrate Gemini/Vertex AI
4. Hugging Face Diffusion Models Course
Materials for the Hugging Face Diffusion Models Course
What sets it apart: Official Hugging Face free course teaching diffusion models from theory to practice with hands-on notebooks and community support
Best for: learning-diffusion-models; hands-on-generative-ai-training; understanding-stable-diffusion
5. Hands-On-LangChain-for-LLM-Applications-Development
Practical LangChain tutorials for LLM applications development
What sets it apart: Curated collection of practical LangChain tutorials for LLM application development, organized from beginner to advanced topics
Best for: learning-langchain-practically; building-first-llm-apps; understanding-rag-and-chatbots
6. bRAG-langchain
Everything you need to know to build your own RAG application
What sets it apart: Comprehensive hands-on RAG tutorial series covering basic to advanced techniques including multi-query, routing, re-ranking, and ColBERT integration
Best for: learning-rag-from-scratch; hands-on-advanced-rag-techniques; building-custom-rag-chatbots
7. GenAI_Agents
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI s
What sets it apart: vs single-framework tutorials: comprehensive cross-framework collection covering 45+ agent architectures with step-by-step notebooks
Best for: Learning GenAI agent architectures from scratch; Exploring diverse agent patterns (multi-agent, memory, tools)
8. AI Getting Started
A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs
What sets it apart: vs building from scratch: a16z-curated opinionated stack (Next.js + LangChain + vector DB + auth + security) eliminates decision paralysis for AI app development
Best for: Learning full-stack AI app development with modern tools; Rapid prototyping of RAG applications