文章目录
- 前言
- 一、Hugging face是什么?
- 二、准备工作
- 三、下载整个仓库或单个大模型文件
- 1. 下载整个仓库
- 2. 下载单个大模型文件
- 总结
- 附录
前言
Hugging face 资源很不错,可是国内下载速度很慢,动则GB的大模型,下载很容易超时,经常下载不成功。很是影响玩AI的信心。(有人说用迅雷啊,试试就知道有无奈。)
经过多次测试,终于搞定了下载,即使超时也可以继续下载。真正实现下载无忧!究竟如何实现?且看本文分解。
一、Hugging face是什么?
Hugging face 起初是一家总部位于纽约的聊天机器人初创服务商,本来打算创业做聊天机器人,在Github上开源了一个Transformers库,虽然聊天机器人业务没搞起来,但是他们的这个库在机器学习社区迅速大火起来。目前已经共享了超100,000个预训练模型,10,000个数据集。俨然陈国了AI开发者的GitHub,提供了模型、数据集(文本|图像|音频|视频)、类库(比如transformers|peft|accelerate)、教程等。
官网网址:https://huggingface.co/
二、准备工作
安装pip install huggingface_hub安装huggingface_hub包
C:\Users\Administrator>pip install huggingface_hub
Requirement already satisfied: huggingface_hub in d:\programdata\anaconda3\lib\site-packages (0.13.2)
Requirement already satisfied: pyyaml>=5.1 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (6.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (4.4.0)
Requirement already satisfied: packaging>=20.9 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (22.0)
Requirement already satisfied: requests in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (2.28.2)
Requirement already satisfied: tqdm>=4.42.1 in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (4.64.1)
Requirement already satisfied: filelock in d:\programdata\anaconda3\lib\site-packages (from huggingface_hub) (3.12.0)
Requirement already satisfied: colorama in d:\programdata\anaconda3\lib\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.6)
Requirement already satisfied: charset-normalizer<4,>=2 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (2022.12.7)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in d:\programdata\anaconda3\lib\site-packages (from requests->huggingface_hub) (1.26.14)
C:\Users\Administrator>
三、下载整个仓库或单个大模型文件
找到自己需要下载的仓库,红色框即是repo_id ,选择蓝色框即可查看仓库内文件。
1. 下载整个仓库
使用snapshot_download下载整个仓库快照,如下几个参数注意下:
- allow_patterns选择需要下载的文件类型,通过ignore_patterns设置需要忽略的文件类型。
- resume_download=True,表示允许断点续传,整个很有必要。
- etag_timeout=100,超时阈值,默认10秒,这里自己根据情况修改。
更多参数详情可访问:https://huggingface.co/docs/huggingface_hub/v0.16.3/guides/download
from huggingface_hub import snapshot_download
repo_id = "LinkSoul/Chinese-Llama-2-7b"
local_dir = 'd:/ai/models'
cache_dir = local_dir + "/cache"
while True:
try:
snapshot_download(cache_dir=cache_dir,
local_dir=local_dir,
repo_id=repo_id,
local_dir_use_symlinks=False,
resume_download=True,
etag_timeout=100,
allow_patterns=["*.model", "*.json", "*.bin",
"*.py", "*.md", "*.txt"],
ignore_patterns=["*.safetensors", "*.msgpack",
"*.h5", "*.ot", ],
)
except Exception as e :
print(e)
print('下载完成')
2. 下载单个大模型文件
个别情况下,我们只需要下载大模型文件,不需要下载整个项目仓库,使用hf_hub_download进行下载即可。参数设置同上解释。
from huggingface_hub import hf_hub_download
repo_id = "BlinkDL/rwkv-4-pile-7b"
local_dir = 'd:/ai/models'
cache_dir = local_dir + "/cache"
filename="RWKV-4-Pile-7B-Chn-testNovel-done-ctx2048-20230404.pth"
while True:
try:
hf_hub_download(cache_dir=cache_dir,
local_dir=local_dir,
repo_id=repo_id,
filename=filename,
local_dir_use_symlinks=False,
resume_download=True,
etag_timeout=100
)
except Exception as e :
print(e)
print('下载完成')
总结
几经测试,终于搞定了模型下载,以后再也不用为下载大文件发愁了。
踩坑无数,撰文只为各位博友少走弯路。
觉得有帮助,发动小手点个赞,谢谢!
附录
huggingface中文社区
https://huggingface.co/blog/zh