huggingface-cli是抱抱脸官方推荐的下载方式之一,但是我原来一直没有用过。这里简单介绍一下该命令的用法(还是挺方便的)。 服务器运行huggingface-cli download --helps可以看到参数详解: usage: huggingface-cli [<args>] download [-h] [--repo-type {model,dataset,space}] [--revision...
命令行hf cli这一段并不稳定,有时候经常会线程卡死,经本人多次测试,http://hf-mirror.com下载最快的方式是:git clone,当然,先设置LFS大文件过滤,set GIT_LFS_SKIP_SMUDGE=1,然后,模型大文件通过IDM或迅雷多线程满速下载。稳定而快捷。 #!/bin/bash trap 'printf "\nDownload interrupted. If you re-run ...
然而如果你用的huggingface-cli download gpt2 --local-dir /data/gpt2下载,即使你把模型存储到了自己指定的目录,但是你仍然可以简单的用模型的名字来引用他。即: AutoModelForCausalLM.from_pretrained("gpt2") 原理是因为huggingface工具链会在.cache/huggingface/下维护一份模型的符号链接,无论你是否指定了模型的...
使用 huggingface 官方提供的huggingface-cli命令行工具。 1. 安装相关依赖 pip install -U huggingface...
from huggingface_hub import snapshot_download snapshot_download(repo_id="tatsu-lab/alpaca_eval", repo_type='dataset') 1. 2. 或者也可以通过huggingface-cli 命令行进行下载模型 huggingface-cli download --repo-type dataset tatsu-lab/alpaca_eval ...
EDIT:The plan is to use HTTP-only methods. Downloading via this CLI will never be meant to create a local git clone. 7. Download private model >>> huggingface-cli download Wauplin/private-model --token=hf_*** Token can be passed in CLI command. Let's encouragehuggingface-cli loginor...
huggingface-cli login 通过snapshot_download下载模型 fromhuggingface_hubimportsnapshot_download snapshot_download(repo_id="bigscience/bloom-560m",local_dir="/data/user/test",local_dir_use_symlinks=False,proxies={"https":"http://localhost:7890"}) ...
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 136, in download_weights local_pt_files = utils.download_weights(pt_filenames, model_id, revision) File "/opt/conda/lib/python3.9/site-packages/text_generation_server/utils/hub.py", line 156, in download_...
Error: DownloadError File "/opt/conda/bin/text-generation-server", line 8, in sys.exit(app()) File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 182, in download_weights utils.convert_files(local_pt_files, local_st_files, discard_names)...
方法2：import transformers as ppb model = ppb.BertForSequenceClassification.from_pretrained('bert-...