BitNet is a PyTorch library implementing the BitNet architecture for 1-bit transformers, as described in the paper "BitNet: Scaling 1-bit Transformers for Large Language Models". The library provides drop-in replacements for standard linear layers with BitLinear layers that use quantized weights and activations to enable efficient computation. It includes components such as BitMGQA (Multi-Grouped Query Attention), BitFeedForward, and full transformer models (BitNetTransformer), as well as adapters for other architectures like Mamba and Mixture of Experts. The library also offers utilities to convert existing Hugging Face or PyTorch models to use BitLinear layers and includes an optimized CUDA kernel for low-precision GEMM operations to accelerate inference. The architecture is modular, with the core BitLinear layer encapsulated in `bitnet/bitlinear.py`, which can be used independently. Higher-level components like attention and feed-forward networks are built using BitLinear and are designed to be reusable in other projects. The library supports training and inference workflows, as demonstrated by the provided example scripts and training routine for the enwik8 dataset. With active maintenance and a focus on performance through CUDA optimization, BitNet serves as a specialized tool for researchers and practitioners interested in extremely low-precision deep learning models.
This repository is a curated 'awesome list' of research resources on multimodal learning in medical imaging. It contains no application code, tests, or data pipelines; its sole substantive file is a large README.md that catalogs papers, datasets, benchmarks, and code repositories. Topics span medical vision-language models, report generation, visual question answering, retrieval-augmented generation (RAG), and multimodal LLMs. The list is organized by task and includes links to arXiv PDFs, GitHub code, and dataset sources, with badges indicating publication venues.