$ huggingface-cli download --repo-type [repo_type] [repo_id] [filename1 filename2 ...] Upload an entire folder or a file to Hugging Face $ huggingface-cli upload --repo-type [repo_type] [repo_id] [path/to/local_file_or_directory] [path/to/repo_file_or_directory] Scan cache to...
#Upload `path/to/folder` to `path/to/folder/` on the Hub>>> huggingface-cli upload Wauplin/my-cool-model path/to/folder Implicit folder #Upload pwd to `/` on the Hub>>> huggingface-cli upload Wauplin/my-cool-model. Upload folder with filters #Upload only safetensors files>>> hug...
上传模型到阿里网盘 # 无法指定文件夹上传,只能传到根目录下,估计是cli的bugremote_folder = ali.get_folder_by_path(out_path,create_folder=True) ali.upload_folder(out_path) ModelScope虚拟机 我的所有操作,都记录在notebook。包括模型的下载和上传到ModelScope模型库 notebook 登录ModelScope社区,启动虚拟机。
upload_folder,Repository,create_commit,push_to_hubto push files to the Hub). Different methods are meant to be optimized for different use cases. Having a CLI would not be as exhaustive as what's existing in Python. Besides, it is always...
huggingface-cli 隶属于 huggingface_hub 库,不仅可以下载模型、数据,还可以可以登录huggingface、上传模型、数据等huggingface-cli 属于官方工具,其长期支持肯定是最好的。优先推荐!安装依赖 1 pip install -U huggingface_hub 注意:huggingface_hub 依赖于 Python>=3.8,此外需要安装 0.17.0 及以上的版本,推荐0.19.0+...
huggingface-cli login 无论使用上述哪种方式生成token,都需要输入登录Hub时的账号和密码。输入完成后,token就已经生成了,这个token会存储在缓存目录中,接下来就可以创建仓库了。 创建仓库也有两种方式,取决于我们进行模型训练的方法:使用Trainer接口还是自己搭建训练代码 ...
你可以使用命令行来通过此令牌登录 (huggingface-cli login) 或者运行以下单元来登录: from huggingface_hubimportnotebook_login notebook_login() Login successful Your token has been saved to /root/.huggingface/token 然后你需要安装 Git LFS 来上传模型检查点: ...
transformers-cli login#loginusing the same credentials as on huggingface.co Upload your model: transformers-cli upload ./path/to/pretrained_model/#^^ Upload folder containing weights/tokenizer/config#saved via `.save_pretrained()`transformers-cli upload ./config.json [--filename folder/foobar.json...
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of the dolomites" ...
Without closing the app, navigate to the folder containing the Android SDK Platform-Tools and open a terminal inside it. Run the ADP command.\adb.exe devices. If everything is working correctly, you should see output similar to this: