import torch # 定义模型路径 model_path = 'E:\\\Python\\\IMDB_movies_transform\\\model_cache\\\gpt2' # 不同项目绝对路径不同,可自行改为相对路径 # 加载tokenizer tokenizer = GPT2Tokenizer.from_pretrained(model_path) # 加载模型 model = GPT2LMHeadModel.from_pretrained(model_path) # 定义输入...
下载你需要的模型 from huggingface_hub import snapshot_download model_path = "./download/" #模型下载路径,可以改为你自己的,你可以在colab上创建一个download文件夹,将模型下载于此 snapshot_download(repo_id="bert-base-uncased", local_dir=model_path) 填入前面获取的fresh_token from aligo import Aligo...
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", torch_dtype=torch.float16).to("cuda") unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet", torch_dtype=torch.float16).to("cuda") tokenizer = CLIPTokenizer....
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda() 通过这种方式,模型会下载到 huggingface 的 cache 目录(通常为 ~/.cache/huggingface,可以通过环境设置 TRANSFORMERS_CACHE 设置默认的 cache 目录),例如下载到 ~/.cache...
# 选择模型,例如'bert-base-uncased'model_name="bert-base-uncased"# 加载模型和分词器 model=AutoModel.from_pretrained(model_name)tokenizer=AutoTokenizer.from_pretrained(model_name)# 使用模型和分词器 text="Hello, world!"encoded_input=tokenizer(text,return_tensors='pt')output=model(**encoded_input...
Describe the bug I tried the very first example Text-to-Image generation with Stable Diffusion. I have set every single variable I can find on Internet to the current path, but it keeps downloading model to ~/.cache. I have very limited ...
The PyTorch model downloads just fine, but the I cannot download the tensorflow model. I used this code to download: from transformers import BertTokenizer, TFBertModel model_name='cahya/bert-base-indonesian-522M' model = TFBertModel.from_pretrained(model_name) Here's what I got when ...
GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("gpt2") if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token probe_network = GPT2LMHeadModel.from_pretrained("gpt2") device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else ...
def download(url, path=None, overwrite=False, sha1_hash=None): """Download an given URL """Download a given URL Parameters --- url : str URL to download url : dict, url for downloading the model, with keys: repo_id, subfolder, filename path : str, optional Destination path to st...
然而,我们下载的还是这样的一个文件,想要通过from_pretrained方法加载,就还需要把模型文件名改成pytorch_model.bin。 但不管如何,我们可以通过这种方案,下载我们想要的模型了,而且可以备份起来,哪里都能用。 只是,这种方式还是欠缺优雅,我们能不能也像from_pretrained方法那样,只要给模型的名字,就能自动下载成我们想要的...