尽管 MOSS/LLaMa/GPT-J 等实现细节有差异,它们都基于 transformer 结构,所以在模型量化环节,方法大同小异。 OpenMMLab 社区成员利用业余时间,针对开源项目 GPTQ-for-LLaMa 做了误差分析,并在此基础上增加一些工程改进。改进后的量化支持 <输入int8,权重 int4,无 zero_point>,有机会进一步加速推理。目前关键代码已...
gptq-for-llama代码解析 gptq-for-llama代码解析旨在深入剖析相关代码原理与运行机制。 对gptq-for-llama代码进行全面梳理以助力技术研究与优化。代码中数据预处理模块精心处理输入数据以适配模型需求。量化算法部分采用独特策略实现模型的低比特量化。模型结构解析能清晰看到不同层的功能及相互关系。权重矩阵在代码里有...
A combination of Oobabooga's fork and the main cuda branch of GPTQ-for-LLaMa in a package format. - GPTQ-for-LLaMa-CUDA/quant_cuda_faster/quant_cuda.cpp at main · jllllll/GPTQ-for-LLaMa-CUDA
GPTQ-for-LLaMA I am currently focusing onAutoGPTQand recommend usingAutoGPTQinstead of GPTQ for Llama. 4 bits quantization ofLLaMAusingGPTQ GPTQ is SOTA one-shot weight quantization method It can be used universally, but it is not thefastestand only supports linux. ...
'GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ' qwopqwop200 GitHub: github.com/qwopqwop200/GPTQ-for-LLaMa #开源##机器学习# û收藏 10 评论 ñ5 评论 o p 同时转发到我的微博 按热度 按时间 正在加载,请稍候... AI博主 3 公司 北京邮电大学 Ü...
Cerebras 推出超快推理能力,Llama 3.1 405B模型性能创纪录 链接:https://news.miracleplus.com/share_link/48186 重点信息 Cerebras在其Inference平台上运行Meta的Llama 3.1 405B模型,创下推理速度新纪录,达每秒969个输出Token,比当前最快的GPU解决方案快12倍,比AWS快75倍。该模型支持128K上下文长度,并将...
for the error: [ModuleNotFoundError: No module named 'llama_inference_offload'] llama_inference_offload is located in dir: GPTQ-for-LLaMa/ what you have to do is to make it in you python path; copy works, or modify the import path. yanchunchun commented May 8, 2023 why i have th...
conda create --name gptq python=3.9 -y conda activate gptq conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa cd GPTQ-for-LLaMa pip install -r requirements.txt ...
Thank you for the repo. I am curious what benchmark results (MMLU and BBH) we shall expect for the gptq-flan-t5 models. I am getting an average accuracy of 25.2% for MMLU using the xl version (4bit, 128 groupsize). It seems a bit far off...
This can be overriden by setting theQUANT_CUDA_OVERRIDEenvironment variable to eitheroldornewbefore importing. There is also an experimental function for switching versions on the fly: fromgptq_for_llamaimportswitch_gptqswitch_gptq('new')importgptq_for_llama.llama_inference_offload ...