from .blocks import FeatureFusionBlock, _make_scratch import torch.nn.functional as F from huggingface_hub import PyTorchModelHubMixin, hf_hub_download from depth_anything.blocks import FeatureFusionBlock, _make_scratch def _make_fusion_block(features, use_bn, size = None): @@ -164,7 +166,...
This only load a .pt file. But in huggingface, most libraries don't provide checkpoints, butpytorch_model.binfiles. And whisper library can't load them easily. Could you please offer a example that load a model from huggingface likehttps://huggingface.co/openai/whisper-mediumorhttps://huggi...
from datasets import load_dataset dataset = load_dataset("parquet", data_files={'train': 'train.parquet', 'test': 'test.parquet'}) 要通过 HTTP 加载远程镶木地板文件,您可以传递 URL: base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/"...
一般来说报这种错应该是是因为本地缓存没有模型,而又无法连接huggingface去下载导致 寻找出现问题的代码,因为是传参,不好直接输入路径 self.qa_model = AutoModelForQuestionAnswering.from_pretrained(self.hparams.transformer_model) 因为服务器没有配置代理,因此通过镜像网站使用huggingface-cli下载了模型到本地的缓存中...
手动下载huggingface.co的模型并修改测试代码: import torch from transformers import BertModel, BertTokenizer, BertConfig dir_path="/home/devil/.cache/huggingface/hub/models--bert-base-chinese/snapshots/8d2a91f91cc38c96bb8b4556ba70c392f8d5ee55/"# 首先要import进来 ...
OSError: We couldn‘t connect to ‘https://huggingface.co‘ to load this file, couldn‘t find it(亲测有效),意思是无法访问这个网址,主要是代码会从huggingface上下载模型,但是国内又存在墙的问题,因此,我们有两种解决方式。
I want to load a huggingface pretrained transformer model directly to GPU (not enough CPU space) e.g. loading BERT from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") would be loaded to CPU until executing model.to('cuda')...
Unable to load weights from pytorch checkpoint file. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. 1. 2. 模型地址:https://huggingface.co/models?search=albert_chinese 方法一: ...
初步判断是因为连不上huggingface,无法下载模型导致的。sd本体也有不少人遇到这个问题,解决办法是手动下载之后改代码里的指向路径。 解决办法 手动下载 首先访问这个仓库,切换到File And Versions选项卡,点文件大小右边的↓依次下载所有文件,然后保存到同一个目录。例如:D:\clip-vit-large-patch14,如果是Linux平台记得...
>>> from random import randint >>> from transformers import pipeline >>> fillmask = pipeline("fill-mask", model="roberta-base") >>> mask_token = fillmask.tokenizer.mask_token >>> smaller_dataset = dataset.filter(lambda e, i: i<100, with_indices=True) 下面的函数会随机选择一个单词进...