As large language models (LLMs) are becoming even bigger, it is increasingly important to provide easy-to-use and efficient deployment paths because the cost of…
因此,减小LLMs的模型大小是一个紧迫的需求。同时,如果我们还可以减少计算成本,它将覆盖提示和生成阶段,以进一步减轻LLMs的服务挑战。 考虑到这些LLMs的训练/微调成本是被禁止的,减轻这些内存/计算挑战最有效的方法之一是进行后训练量化(PTQ),其中不需要/只需最少的训练来将权重和/或激活的位精度降低到INT4或INT8...
Efficient Streaming Language Models with Attention Sinks 韩松实验室发表在ICLR2024上的工作。现在LLM在做的一件事就是追求长度的外推性,即让LLM可以处理足够长的输入序列。作者就是想解决能否在不牺牲效率和性能的情况下,部署一个能处理无限输入的LLM? 作者发现,普通的注意力在处理长序列时计算复杂度和效果都很差...
as well as post-training quantization and quantization-aware training. Each method has its own set of trade-offs between model size, speed, and accuracy, making quantization a versatile and essential tool in deploying efficient AI models on a wide range of hardware platforms. ...
👋 Hi! Thank you for contributing to the vLLM project. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only runfastcheckCI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top ...
Runing Quantized Models with MLC-LLM MLC-LLMoffers a universal deployment solution suitable for various language models across a wide range of hardware backends, encompassing iPhones, Android phones, and GPUs from NVIDIA, AMD, and Intel. We compile the OmniQuant's quantization models through MLC-LL...
19 Mar 2024·Yuexiao Ma,Huixia Li,Xiawu Zheng,Feng Ling,Xuefeng Xiao,Rui Wang,Shilei Wen,Fei Chao,Rongrong Ji· The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and acc...
The remarkable size of large language models (LLMs) has brought about a groundbreaking transformation in human-language applications. Nonetheless, AI developers and researchers often encounter obstacles stemming from the massive size and latency associated with these models. These challenges can hamper col...
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models论文笔记 原文链接:https://arxiv.org/pdf/2305.17888.pdf Meta今年的论文。 PTQ方法在8-bit以下通常效果会显著下降,也很少有PTQ方法同时考虑weight,activation和KV cache。因此求诸QAT。
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 ...