pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118 使用pip安装速度较慢,我选用conda安装,如下 方式二: conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia 安装完成,测试是否安装成功,如下:进入python环...
同时,请留意自己的mamba或者conda有没有预设python版本,正如这里我的mamba会预设安装python 3.10的版本,此时如果我去搜索安装较低版本的pytorch和torchvision,也会出现无法install成功的可能 (your_env_name) C:\Users\your_PC_name>mamba install pytorch torchvision=0.12.0=py310_cu113 torchaudio cudatoolkit cudnn...
conda create-n your_env_name python=3.10.13conda activate your_env_name conda install cudatoolkit==11.8-c nvidia pip install torch==2.1.1torchvision==0.16.1torchaudio==2.1.1--index-url https://download.pytorch.org/whl/cu118 conda install-c"nvidia/label/cuda-11.8.0"cuda-nvcc conda install...
3、 受博文 “flash-attention踩坑:使用conda管理CUDA”启发,合理调整按照顺利,先安装CUDA,并且安装cuda-nvcc,正确的安装步骤如下: conda create -n your_env_name python=3.10.13conda activate your_env_nameconda install cudatoolkit==11.8 -c nvidiapip install torch==2.1.1 torchvision==0.16.1 torchaudio...
!pip install causal-conv1d==1.0.0!pip install mamba-ssm==1.0.1 然后直接使用transformers库读取预训练的Mamba-3B 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importtorchimportos from transformersimportAutoTokenizer from mamba_ssm.models.mixer_seq_simpleimportMambaLMHeadModel ...
blas-devel-3.9.0-21_win64_mkl The following packages will be UPDATED: openssl 3.2.1-hcfcfb64_1 -->3.3.0-hcfcfb64_0 pytorch 2.2.1-py3.12_cuda12.1_cudnn8_0 -->2.3.0-py3.12_cuda12.1_cudnn8_0 torchaudio 2.2.1-py312_cu121 -->2.3.0-py312_cu121 torchvision 0.17.1-py312_cu121...
Although, I've also encountered issues similar to those being described in the issue later on (graph compilation errors). I'd also suggest using torch==2.2.0 with triton 2.2.0 (no idea why but it ran faster than 2.3.0 in my case). ...
!pip installmamba-ssm==1.0.1 然后直接使用transformers库读取预训练的Mamba-3B importtorchimportos from transformersimportAutoTokenizer from mamba_ssm.models.mixer_seq_simpleimportMambaLMHeadModel tokenizer =AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ...
按照网上的配置,最好是torch 2.1版本的,注:所有的安装时间偏慢, conda activate 虚拟环境名称 condainstallpytorch==2.1.1torchvision==0.16.1torchaudio==2.1.1pytorch-cuda=11.8-c pytorch -c nvidia condainstallcudatoolkit==11.8-c nvidia condainstall-c"nvidia/label/cuda-11.8.0"cuda-nvcc ...
首先我们安装依赖,这是官网介绍的: !pip install causal-conv1d==1.0.0 ! 然后直接使用transformers库读取预训练的Mamba-3B import torch import os from transformers import AutoTokenizer frommamba_ssm.models.mixer_seq_simple import MambaLMHeadModel tokenizer =AutoTokenizer.from...