bitnet.cpp是1bit LLM（例如 BitNet b1.58）的官方推理框架。该框架配备了一系列优化内核，支持在CPU上进行快速且无损的1.58bit模型推理，未来将扩展支持NPU和GPU。bitnet.cpp的首版主要支持CPU推理。具体性能改进方面，在ARM CPU上，该框架可实现1.37至5.07倍的加速，而且更大的模型将有更显著的性能提升。同时...
Receivers based on 1-bit quantization and oversampling with respect to the transmit signal bandwidth enable a lower power consumption and a reduced circuit complexity compared to conventional amplitude quantization. In this work, the achievable rate for systems using such analog-to-digital conversion ...
记笔记 这篇视频主要简单介绍了超低bit量化的一篇工作:Huang W, Liu Y, Qin H, et al. Billm: Pushing the limit of post-training quantization for llms[J]. arXiv preprint arXiv:2402.04291, 2024. 知识 校园学习 AI 人工智能 学习 Transformer ...
1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead Overview QJL (Quantized Johnson-Lindenstrauss) is a novel approach to compress the Key-Value (KV) cache in large language models (LLMs). It applies a Johnson-Lindenstrauss (JL) transform as a preconditioner to the embedd...
For example, please see this demo of llama 7B running on a pixel 5 at 1 token/sec using 4 bit quantization:https://twitter.com/ggerganov/status/1635605532726681600 So this issue can probably be re-opened considering it is viable to gain this benefit without hardware support?llama.cpphas gro...
We propose a linear minimum mean-squared error (MMSE)-based detector that accounts for the non-linearity effects of the 1-bit quantization as well as for channel estimation error. An analytical framework that derives the achievable rate of the MMSE-based detector in a massive MIMO configuration ...
[1] Jacob B, Kligys S, Chen B, et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2704-2713.
In this work, we show that reconstructing a sparse signal from quantized compressive measurement can be achieved in an unified formalism whatever the (scalar) quantization resolution, i.e., from 1-bit to high resolution assumption. This is achieved by generalizing the iterative hard thresholding (...
1-bit FQT算法包括激活梯度修剪(Activation Gradient Pruning, AGP)和样本通道联合量化(Sample Channel joint Quantization, SCQ)两个主要策略。AGP策略通过剪除信息量较少的梯度组,重新分配资源以提高剩余梯度的数值精度,从而减少梯度方差。SCQ策略则在权重梯度和激活梯度的计算中采用不同的量化方法,确保这些操作能够在低...
【16-Bit to 1-Bit: Visual KV Cache Quantization for Efficient Multimodal LLMs】http://t.cn/A6Btd40R 16 位到 1 位:高效多模 LLM 的视觉 KV 缓存量化。