The dataset could not be loaded because the splits use different data file formats, which is not supported. Read more about the splits configuration. Click for more details.
Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('webdataset', {}), NamedSplit('validation'): (None, {}), NamedSplit('test'): (None, {})}
Error code:   FileFormatMismatchBetweenSplitsError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

此huggingface库主要存储本人电脑的一些重要文件

如果无法下载文件,把下载链接的huggingface.co改成hf-mirror.com 即可

如果你也想要在此处永久备份文件,可以参考我的上传代码:

# 功能函数,清理打包上传
from pathlib import Path
from huggingface_hub import HfApi, login

repo_id = 'ACCC1380/private-model'
yun_folders = ['/kaggle/input']


def hugface_upload(yun_folders, repo_id):
    if 5 == 5:
        hugToken = '********************' #改成你的huggingface_token
        if hugToken != '':
            login(token=hugToken)
            api = HfApi()
            print("HfApi 类已实例化")
            print("开始上传文件...")
            for yun_folder in yun_folders:
                folder_path = Path(yun_folder)
                if folder_path.exists() and folder_path.is_dir():
                    for file_in_folder in folder_path.glob('**/*'):
                        if file_in_folder.is_file():
                            try:
                                response = api.upload_file(
                                    path_or_fileobj=file_in_folder,
                                    path_in_repo=str(file_in_folder.relative_to(folder_path.parent)),
                                    repo_id=repo_id,
                                    repo_type="dataset"
                                )
                                print("文件上传完成")
                                print(f"响应: {response}")
                            except Exception as e:
                                print(f"文件 {file_in_folder} 上传失败: {e}")
                                continue
                else:
                    print(f'Error: Folder {yun_folder} does not exist')
        else:
            print(f'Error: File {huggingface_token_file} does not exist')

hugface_upload(yun_folders, repo_id)

本地电脑需要梯子环境,上传可能很慢。可以使用kaggle等中转服务器上传,下载速率400MB/s,上传速率60MB/s。

在kaggle上面转存模型:

  • 第一步:下载文件
!apt install -y aria2
!aria2c -x 16 -s 16 -c -k 1M "把下载链接填到这双引号里" -o "保存的文件名称.safetensors"
  • 第二步:使用上述代码的API上传
# 功能函数,清理打包上传
from pathlib import Path
from huggingface_hub import HfApi, login

repo_id = 'ACCC1380/private-model'
yun_folders = ['/kaggle/working'] #kaggle的output路径


def hugface_upload(yun_folders, repo_id):
    if 5 == 5:
        hugToken = '********************' #改成你的huggingface_token
        if hugToken != '':
            login(token=hugToken)
            api = HfApi()
            print("HfApi 类已实例化")
            print("开始上传文件...")
            for yun_folder in yun_folders:
                folder_path = Path(yun_folder)
                if folder_path.exists() and folder_path.is_dir():
                    for file_in_folder in folder_path.glob('**/*'):
                        if file_in_folder.is_file():
                            try:
                                response = api.upload_file(
                                    path_or_fileobj=file_in_folder,
                                    path_in_repo=str(file_in_folder.relative_to(folder_path.parent)),
                                    repo_id=repo_id,
                                    repo_type="dataset"
                                )
                                print("文件上传完成")
                                print(f"响应: {response}")
                            except Exception as e:
                                print(f"文件 {file_in_folder} 上传失败: {e}")
                                continue
                else:
                    print(f'Error: Folder {yun_folder} does not exist')
        else:
            print(f'Error: File {huggingface_token_file} does not exist')

hugface_upload(yun_folders, repo_id)
  • 第三步:等待上传完成:

image/png

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