bash pip install absl-py sphinx-glpi-theme prettytable 如果你的网络环境无法直接访问英伟达的服务器,你可能需要配置代理或使用其他方式来解决网络问题。 希望这些步骤能帮助你成功安装pytorch_quantization。如果你在安装过程中遇到任何问题,请随时提问。
安装示例: pipinstalltorch torchvision torchaudio 1. 克隆PyTorch Quantization库的GitHub仓库并安装。 gitclonecdpytorchgitsubmodule update--init--recursivepipinstall-e. 1. 2. 3. 4. 这里的-e参数指的是editable模式,使得在开发过程中我们可以即时看到对代码的修改。 3. 使用PyTorch Quantization 安装完成后,我...
For native builds (not using the CentOS7 build container), first install devtoolset-8 to obtain the updated g++ toolchain as follows: yum -y install centos-release-scl yum-config-manager --enable rhel-server-rhscl-7-rpms yum -y install devtoolset-8 export PATH="/opt/rh/devtoolset-8/root...
pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com 3.Prepare coco dataset .├── annotations │ ├── captions_train2017.json │ ├── captions_val2017.json │ ├── instances_train2017.json │ ├── instances_val2017.json │ ├── person_keypoints_trai...
pip install quanto Quantization workflow Quanto does not make a clear distinction between dynamic and static quantization: models are always dynamically quantized, but their weights can later be "frozen" to integer values. A typical quantization workflow would consist of the following steps: ...
pip install transformers torch 2. 加载预训练的BERT模型 我们将加载一个预训练的BERT模型,并对其进行量化。 importtorchfromtransformersimportBertModel,BertConfig# 加载预训练的BERT模型config=BertConfig.from_pretrained('bert-base-uncased')model=BertModel.from_pretrained(model_name,config=config)# 打印模型结构...
pip install quanto ``` [🤗 quanto](https://github.com/huggingface/quanto) does not make a clear distinction between dynamic and static quantization. Models are dynamically quantized first, but their weights can be "frozen" later to static values. A typical quantization workflow consists of the...
Install lm-eval. Run an evaluation. Example: lm_eval --model hf --model_args pretrained=${HF_USER}/${MODEL_ID} --tasks hellaswag --device cuda:0 --batch_size 8 Check out the lm-eval usage docs for more details. KV Cache Quantization We've added kv cache quantization and other fea...
Install $ pip install vector-quantize-pytorch Usage import torch from vector_quantize_pytorch import VectorQuantize vq = VectorQuantize( dim = 256, codebook_size = 512, # codebook size decay = 0.8, # the exponential moving average decay, lower means the dictionary will change faster commitment_...
conda create -n flatquant python=3.10 -y conda activate flatquant pip install -r requirements.txt && pip install -e . && pip install triton==3.0.0Note: To run models like LLaMA-3.1 or Qwen-2.5, we use transformers==4.45.0 instead....