Official code repository for the O'Reilly book "Hands-On Large Language Models". Features 12 core chapters and bonus content covering Tokens, Transformers, RAG, and Fine-tuning. Includes 300+ illustrations and runnable Jupyter Notebooks optimized for Colab and local environments.
A comprehensive AI engineering hub featuring 93+ production-ready projects with in-depth tutorials and implementations for LLMs, RAGs, AI Agents, and MCP, covering beginner to advanced skill levels.
A curated collection of resources that bridge the gap between Retrieval-Augmented Generation (RAG) and Reasoning in Large Language Models and Agents, featuring papers, tools, and implementations.
A collection of Jupyter notebooks accompanying the "Building LLMs for Production" book by Towards AI, providing practical tutorials on RAG, LLMs, prompt engineering, fine-tuning, and more.
A benchmark platform featuring 100 PhD-level research tasks across 22 distinct fields, systematically evaluating Deep Research Agents (DRAs) on report generation quality and information retrieval capabilities.
A curated collection of papers and blogs on Large Language Model based Long Context Modeling, covering Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation (RAG), and Evaluation methods。
DeepResearch is an open-source deep research agent developed by Alibaba, designed for long-horizon, deep information-seeking tasks. With 30.5 billion total parameters but only 3.3 billion activated per token, it demonstrates state-of-the-art performance across various agentic search benchmarks like Humanity's Last Exam, BrowseComp, and WebWalkerQA.
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