Model interpretability and understanding for PyTorch
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Algorithms for explaining machine learning models
A game theoretic approach to explain the output of any machine learning model.
Foundation Architecture for (M)LLMs
A library for mechanistic interpretability of GPT-style language models
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
