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Source channel @githubtrending · Post #14926 · Jul 8

#jupyter_notebook#artificial_intelligence#book#large_language_models#llm#llms#oreilly#oreilly_books You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs. https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

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@libreware · Post #957 · 06/22/2021, 01:28 PM

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— LibreCellular 21.04 documentation –https://librecellular.org/ The LibreCellular project aims to make it easier to create #4G cellular #networks with open source software and low cost software-defined radio (#SDR) hardware. Seeking to achieve this via validated hardware and software configurations that are subjected to rigorous testing, together with additional tooling and #documentation for repeatable deployment. LibreCellular will build on the work of numerous existing open source software and hardware projects, related to both the #cellular platform itself and associated test #infrastructure. Where necessary additional components will be developed, with any software source code and #hardware designs published under #opensource licences. The focus is very much on integration, testing, packaging and documentation, reusing and building upon existing solutions.. #LibreCellular#CellulaireLibre

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