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Source channel @githubtrending · Post #15340 · Dec 17

#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym

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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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#4g #networks #sdr #documentation #cellular #infrastructure #hardware #opensource #librecellular #cellulairelibre