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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).
By applying 1.58-bit quantization to convolutional neural networks and building upon transformative research in the field, this project extends beyond simple implementation to create a living document that evolves with ongoing experimentation.
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}.
26 mar 2024 · BitNet b1.58 addresses this by halving activation bits, enabling a doubled context length with the same resources, with potential further compression to 4 bits or lower for 1.58-bit LLMs, a...
9 mar 2024 · The basic formula for this is shown as, x_q = round (x/S + Z) x: The original continuous variable that we want to quantize. x_q : The quantized value of x. S: The scaling factor. This parameter...
27 kwi 2024 · pip install bitnet. Usage: BitLinear. Example of the BitLinear layer which is the main innovation of the paper! import torch from bitnet import BitLinear # Input x = torch. randn (10, 1000, 512) # BitLinear layer layer = BitLinear (512, 400) # Output y = layer (x) print (y) BitLinearNew
