"OmniQuant: Omnidirectionally Calibrated Quantization For Large Language Models"论文阅读 9iM 论文信息 会议/期刊来源:ICLR 时间:2024 作者:Ping Luo(HKU) 引言 LLM量化过程中,有很多手动调节的量化参数。过去的工作中,对于这些参数要么采取网格搜索的形式进行调优,要么根据经验手动进行调节。对于多变的输入值,...
Models like large language models or vision models have captured attention due to their remarkable performance and usefulness. If these models are running on a cloud or a big device, this does not create a problem. However, their size and computational demands pose a major challenge when ...
其实还有:SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models 但是这篇说他不适合更小的位数的例如4位,所以就没有做实验; 然后就是看实验结果: 我简单总结就是: 1 w4a16 上,这三个方法不相伯仲, 模型越大,量化影响越小,(也就是说模型越大,鲁棒性越好) 与其他论文的...
Models like large language models or vision models have captured attention due to their remarkable performance and usefulness. If these models are running on a cloud or a big device, this does not create a problem. However, their size and computational demands pose a major challenge when ...
Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits). It reduces memor...
Official PyTorch implement of paper EfficientQAT: Efficient Quantization-Aware Training for Large Language ModelsNews[2024/10] 🔥 We release a new weight-activation quantization algorithm, PrefixQuant, which is the first work to let the performance of static activation quantization surpasses dynamic one...
#large-language-models-(llms) Increased LLM Vulnerabilities from Fine-tuning and Quantization: Experiment Set-up & Results Quantization Oct 17, 2024 #large-language-models-(llms) 100+ Increased LLM Vulnerabilities from Fine-tuning and Quantization: Problem Formulation and Experiments ...
What if you could get similar results from your large language model (LLM) with 75% less GPU memory? In myprevious article,, we discussed the benefits of smaller LLMs and some of the techniques for shrinking them. In this article, we’ll put this to test by comparing the results of th...
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs. However, existing PTQ methods only focus on handling the outliers within one layer or one block, which ignores the dependency of blocks and leads to severe performance ...
The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and accelerating neural networks. Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of ...