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Top 23 Jupyter Notebook LLM Projects
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A hands-on, end-to-end course on building, evaluating, and deploying LLM applications. Ideal when you want a clear path from spark of an idea to deployment. Link: https://github.com/mlabonne/llm-course
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llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
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RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Project mention: Building with Generative AI: Lessons from 5 Projects Part 1: RAG | dev.to | 2025-08-08There is also another repo that showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems.
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Hands-On-Large-Language-Models
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
It’s the full code from the book, with notebooks covering LLM basics, training, and fine-tuning. If you like a guided, notebook-first path from foundations to customization, this feels like a friendly trail map. Link: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
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GenAI_Agents
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Clear tutorials and implementations of generative AI agent techniques, from basic builds to advanced strategies. You’ll see how different agent strategies are wired up, which makes design choices feel obvious. Link: https://github.com/NirDiamant/GenAI_Agents
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Project mention: AirLLM Shrinks 70B LLMs to 4GB VRAM; DPO & Supermemory Boost Open Models | dev.to | 2026-06-03
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agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Project mention: Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval | news.ycombinator.com | 2026-01-17GraphRAG's approach certainly makes sense for regulation-heavy documents, where interconnected references can be critical for retrieval quality. The challenge you mentioned about separate systems for graph storage and vector indexing resonates with what we've seen in the field. In practice, this separation often adds unnecessary complexity for ephemeral analysis tasks. One thing we've explored is using a dual-memory architecture, which might be relevant here even in an in-memory context. It allows you to effectively manage recent context and longer-term semantic connections without the overhead of persistent storage. If you're interested in seeing how this approach could be integrated into production systems, I wrote up some patterns that might be useful: https://github.com/NirDiamant/agents-towards-production.
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llama-cookbook
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
While people often focus on the big models, smaller and more specialised models are increasingly important. Codex for code generation, Stable Diffusion for images, and small LLaMA-class models for focused tasks are all good examples.
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generative-ai
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
ℹ️ The complete source code is available in this notebook (including all setup details and future updates) under the Apache 2.0 license. You can also directly open the notebook in Colab. This article reproduces all the results generated by a click on “Run all”.
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evidently
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Project mention: Why Your Neural Network Fails Silently and How to Actually Debug It | dev.to | 2026-04-25Set up drift detection. Compare weekly distributions of model inputs against your training data baseline. Tools like Evidently make this straightforward.
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Prompt_Engineering
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
View on GitHub
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GenerativeAIExamples
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
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nixtla
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
Project mention: Google's 200M-parameter time-series foundation model with 16k context | news.ycombinator.com | 2026-03-30 -
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higgsfield
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
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jailbreak_llms
[CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts).
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agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
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awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
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Alpaca-CoT
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook LLM discussion
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I Built a Money-Making Machine with Free AI Tools — Here is the Full Blueprint
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A note from our sponsor - SaaSHub
www.saashub.com | 12 Jul 2026
Index
What are some of the best open-source LLM projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | LLMs-from-scratch | 98,779 |
| 2 | llm-course | 80,849 |
| 3 | llm-app | 59,063 |
| 4 | RAG_Techniques | 28,481 |
| 5 | Hands-On-Large-Language-Models | 27,368 |
| 6 | GenAI_Agents | 23,060 |
| 7 | airllm | 22,406 |
| 8 | agents-towards-production | 20,939 |
| 9 | llama-cookbook | 18,399 |
| 10 | generative-ai | 17,218 |
| 11 | evidently | 7,685 |
| 12 | Prompt_Engineering | 7,657 |
| 13 | bRAG-langchain | 4,136 |
| 14 | GenerativeAIExamples | 4,110 |
| 15 | nixtla | 3,943 |
| 16 | ReAct | 3,796 |
| 17 | higgsfield | 3,761 |
| 18 | jailbreak_llms | 3,719 |
| 19 | agentic-rag-for-dummies | 3,637 |
| 20 | awesome-generative-ai | 3,498 |
| 21 | lida | 3,251 |
| 22 | examples | 3,027 |
| 23 | Alpaca-CoT | 2,797 |
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