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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}.
9 mar 2024 · # clips x between range [a,b], a is min clip value and b is max clip value # for example, Clip(-5,0,10) = 0 and Clip(57,0,10) = 10 def Clip(x,a,b): max(a,min(b,x)) b = 8 #Bitnet uses...
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.
26 mar 2024 · Unlike its predecessor, BitNet b1.58 replaces the conventional nn.Linear layers with BitLinear layers, leveraging 1.58-bit weights and 8-bit activations.
29 mar 2024 · python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048 python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \ --batch_size 1 \ --tasks \ --output_path result.json \ --num_fewshot 0 \ --ctx_size 2048
