首先注册HuggingFace平台：https://huggingface.co/join 随后在用户的设置界面新建token，也就是令牌：这里...
temperature=0.5)llm_chain=LLMChain(prompt=prompt,llm=llm)print(llm_chain.run(question))...
Performance on iPhone is somewhat erratic, sometimes it's ~20x slower and the phone heats up. This happens because the model could not be scheduled to run on the Neural Engine and everything happens in the CPU. We have not been able to determine the reasons for this problem. If you obse...
cargo runshould display a tensor of shapeTensor[[2, 4], f32]. Having installedcandlewith Cuda support, simply define thedeviceto be on GPU: -let device = Device::Cpu;+let device = Device::new_cuda(0)?; For more advanced examples, please have a look at the following section. ...
"train_micro_batch_size_per_gpu":"auto", "wall_clock_breakdown":false } 现在,该训练脚本上场了。我们根据Fine Tune FLAN-T5准备了一个run_seq2seq_deepspeed.py训练脚本,它支持我们配置 deepspeed 和其他超参数,包括google/flan-t5-xxl的模型 ID。
per_gpu_train_batch_size:Union[int,NoneType]=None,per_gpu_eval_batch_size:Union[int,NoneType]=None,gradient_accumulation_steps:int=1,eval_accumulation_steps:Union[int,NoneType]=None,learning_rate:float=5e-05,weight_decay:float=0.0,adam_beta1:float=0.9,adam_beta2:float=0.999,adam_epsilon:...
RUNpip3 install--upgrade torch==1.9.0torchvision==0.10.0 在上面的命令中,我们会将torch替换为指定版本。实际构建镜像的过程中,大概需要额外下载 800MB 的数据。即使我们使用了国内的软件源,时间可能也会比较漫长,可以考虑去冰箱里拿一罐冰可乐,缓解等待焦虑。🥤 ...
These modelshave an interesting feature. They run well on the cloud platform, but once you want to run them locally, you have to struggle. You can always see user feedback in the GitHub associated with the project: this model and code , I can't run it locally, it's too troublesome ...
RUN pip install towhee 将上面的内容保存为 Dockerfile 后,执行docker build -t soulteary/docker-pytorch-playground .,等到命令执行完毕,我们的 PyTorch 基础镜像就构建完成了。 如果你不想浪费时间构建,也可以直接使用我已经构建好的基础镜像(支持自动区分 x86 / ARM 架构设备),直接从 DockerHub 下载即可: ...
per_gpu_train_batch_size: Optional[int] = None,per_gpu_eval_batch_size: Optional[int] = None,gradient_accumulation_steps: int = 1,eval_accumulation_steps: Optional[int] = None,learning_rate: float = 5e-05,weight_decay: float = 0.0,adam_beta1: float = 0.9,adam_beta2: float = ...