From the documentationfor from_pretrained, I understand I don't have to download the pretrained vectors every time, I can save them and load from disk with this syntax: - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~tr...
By using AutoTokenizer.from_pretrained, you can easily load the tokenizer associated with a specific pre-trained model without explicitly specifying the tokenizer's name or type. This allows for a more streamlined and convenient workflow when working with different...
11 How to load the saved tokenizer from pretrained model 0 How to use adapter transformers with a Huggingface Pipeline 0 Getting started: Huggingface Model Cards 8 How to convert a PyTorch nn.Module into a HuggingFace PreTrainedModel object? 0 NLP, how to fix that pretrained mo...
dataset = load_dataset('text', data_files='https://huggingface.co/datasets/lhoestq/test/resolve/main/some_text.txt') 1.2.4 Parquet 与基于行的文件(如 CSV)不同,Parquet 文件以柱状格式存储。大型数据集可以存储在 Parquet 文件中,因为它更高效,返回查询的速度更快。#加载 Parquet 文件,如下例所示...
model.load_state_dict(torch.load('checkpoints/depth_anything_vitl14.pth')) model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval() title = "# Depth Anything" description = """Official demo for **Depth Anything: Unleashing the Power of Large-Scale Unlabele...
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...
import tensorflow as tf from transformers import AutoModel, TFBertModel auto_model = AutoModel.from_pretrained("bert-base-uncased") tfbert_model = TFBertModel.from_pretrained("bert-base-uncased") print(auto_model.__class__) print(tfbert_model.__class__) Then the output is: <class 'tra...
text2img_pipe = StableDiffusionPipeline.from_pretrained( "stablediffusionapi/deliberate-v2" , torch_dtype = torch.float16 , safety_checker = None ).to("cuda:0") lora_path = "<path/to/lora.safetensors>" text2img_pipe.load_lora_weights(lora_path) ...
pretrained_model_name_or_path=str(pretrained_model_name_or_path) 改成 pretrained_model_name_or_path = "D:\models--openai--clip-vit-large-patch14" <-刚才手动下载保存的路径 最后重启软件就可以了,一定要重启软件!因为这个路径是在软件启动的时候加载的。
model= BertModel.from_pretrained(dir_path) 成功运行,下图给出对hugging face的模型文件的查找: --- 之所以python代码不能访问并下载huggingface.co的模型和不能web访问huggingface.co网站的原因是一样的,就和不能访问Google的原因一样,自然的想法就是使用下面的工具来解决这个问题: 根据之前的经验,我们只需要在终...