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is a PyTorch library for authoring, post-training and experimenting with LLMs. It supports single and multi-GPU setups and enables distributed training with . The library provides YAML-based for tasks like fine-tuning, inference, evaluation and quantization-aware training. Each recipe offers a focused set of features, avoiding complex flag-based configurations. It prioritizes simplicity, favoring code clarity over excessive abstractions. It also includes a for downloading models, managing recipes and running experiments efficiently.