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Source channel @githubtrending · Post #14909 · Jul 3

#other#agent#llm#rag Happy-LLM is a free, open-source learning project that helps you deeply understand large language models (LLMs) from basics to advanced training and applications. It teaches you key concepts like NLP, Transformer architecture, pretraining, and how to build and train your own LLaMA2 model step-by-step. You also learn practical skills like fine-tuning and using cutting-edge techniques such as Retrieval-Augmented Generation (RAG) and intelligent agents. This project is ideal if you know some Python and deep learning, and it offers both theory and hands-on code to help you master LLM development and apply it in real-world AI tasks. This can boost your skills and confidence in AI model building and research. https://github.com/datawhalechina/happy-llm

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@githubtrending · Post #15573 · 03/19/2026, 11:30 AM

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#java#a11y#accessibility#ai#bounding_box#document_parsing#eaa#html#json#markdown#ocr#ocr_recognition#pdf#pdf_accessibility#pdf_converter#pdf_extraction#pdf_parser#pdf_ua#rag#tables#tagged_pdf OpenDataLoader PDF is a free, open-source tool (Apache 2.0) that tops benchmarks with 0.90 accuracy for extracting structured data like Markdown, JSON (with bounding boxes), and HTML from any PDF—digital, scanned, or complex with tables, formulas, charts, and OCR in 80+ languages. It runs locally on CPU (0.05s/page fast mode), filters AI prompt injections for safety, integrates with LangChain/RAG, and automates accessibility tagging to Tagged PDF. You save time and costs on parsing for AI pipelines or compliance (vs. $50–200/manual doc), getting precise, private results for better LLM apps and legal standards. https://github.com/opendataloader-project/opendataloader-pdf

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