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bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models on CPU (with NPU and GPU support coming next).
- GitHub - kyegomez/BitNet: Implementation of "BitNet: Scaling 1-bit ...
PyTorch Implementation of the linear methods and model from...
- GitHub - kyegomez/BitNet: Implementation of "BitNet: Scaling 1-bit ...
28 lut 2024 · Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}.
18 wrz 2024 · BitNet is an architecture introduced by Microsoft Research that uses extreme quantization, representing each parameter with only three values: -1, 0, and 1. This results in a model that uses just 1.58 bits per parameter, significantly reducing computational and memory requirements.
In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch.
PyTorch Implementation of the linear methods and model from the paper "BitNet: Scaling 1-bit Transformers for Large Language Models". Paper link: BitLinear = tensor -> layernorm -> Binarize -> abs max quantization -> dequant.
This is a reproduction of the BitNet b1.58 paper. The models are trained with RedPajama dataset for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following paper. All models are open-source in the repo.
1 mar 2024 · BitNet 1.58 B marks a significant advancement, offering improvements in speed, memory usage, and energy efficiency at a 3B model scale, with a 2.71 times speed increase and substantial memory...
