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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).
18 wrz 2024 · BitNet is a special transformers architecture that represents each parameter with only three values: (-1, 0, 1), offering a extreme quantization of just 1.58 ( l o g 2 (3) log_2(3) l o g 2 (3)) bits per parameter. However, it requires to train a model from scratch.
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}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and ...
This repository not only provides PyTorch implementations for training and evaluating 1.58-bit neural networks but also includes a unique integration where the experiments conducted automatically update a LaTeX-generated paper.
7 wrz 2024 · Reducing the precision of model weights from 32-bit floats to 8-bit integers, or even 1-bit binary numbers is called quantization. This article aims to demystify 1.58-bit large language models with an easily accessible overview based on a literature review.
Based on Microsoft's 'The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits' paper. This repository introduces a toy work-in-progress implementation of BitNet - a scalable and stable 1-bit Transformer architecture designed specifically for large language models.
29 lut 2024 · Unlike its predecessors, BitNet b1.58 is trained from the ground up, utilizing weights quantized to 1.58-bits and activations reduced to 8-bits. This approach significantly deviates from the standard full-precision formats typically seen in AI models.
