float16, trust_remote_code=True, cache_dir='/home/{username}/huggingface').cuda() 如果希望下载指定 commit 版本对应的模型,也可以指定 revision 参数。我们可以看到,上面这种方式下载的模型权重路径会比较长,我们可以通过调用save_pretrained方法将模型保存到另一个路径。 import torch from transformers import ...
importdatasetsfromdatasetsimportDownloadMode# resume_download是断点续传,max_retries可以在短暂断连后等待一个足够长到恢复连接的间隔config=datasets.DownloadConfig(resume_download=True,max_retries=100)data=datasets.load_dataset('natural_questions',cache_dir=r'',download_config=config,download_mode=DownloadMode...
方法一:代码直接下载 我们在第一次执行BertTokenizer和BertModel中的某个模型的from_pretrained函数的时候,将会自动下载预训练模型的相关文件。Linux中默认下载到~/.cache/huggingface/transformers中。 代码如下: fromtransformersimportBertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained("bert-base-uncased...
# 加载预训练字典和分词方法 tokenizer = BertTokenizer.from_pretrained( pretrained_model_name_or_path = 'bert-base-chinese', cache_dir=None, force_download=False, ) sents = [ '选择珠江花园的原因就是方便。', '笔记本的键盘确实爽。', '房间太小。其他的都一般', '今天才知道这书还有第6卷,真...
from transformers import BartForConditionalGenerationfrom transformers import Seq2SeqTrainingArguments, Seq2SeqTrainermodel = BartForConditionalGeneration.from_pretrained( "facebook/bart-base" )training_args = Seq2SeqTrainingArguments( output_dir="./", evaluation_strategy="steps", per_device...
然而如果你用的huggingface-cli download gpt2 --local-dir /data/gpt2下载,即使你把模型存储到了自己指定的目录,但是你仍然可以简单的用模型的名字来引用他。即: AutoModelForCausalLM.from_pretrained("gpt2") 原理是因为huggingface工具链会在.cache/huggingface/下维护一份模型的符号链接,无论你是否指定了模型的...
model = model_class.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ...
model_id, from_transformers=True)hf_model.to('npu')tokenizer = AutoTokenizer.from_pretrained(model_id)hf_pipe_cls = pipeline("text-classification", model=hf_model, tokenizer=tokenizer)text = "He's a dreadful magician."fp32_outputs = hf_pipe_cls(text)print("FP32 model outputs: ...
(repo,ckpt),device=device))pipe=AnimateDiffPipeline.from_pretrained(base,motion_adapter=adapter,torch_dtype=dtype).to(device)pipe.scheduler=EulerDiscreteScheduler.from_config(pipe.scheduler.config,timestep_spacing="trailing",beta_schedule="linear")output=pipe(prompt="A girl smiling",guidance_scale=...
private static final String CACHE_DIR = "/path/to/model/cache"; // 指定本地缓存目录,或使用默认值(null) private PreTrainedModel model; private PreTrainedTokenizer tokenizer; public void loadModel() throws IOException { BertConfig config = BertConfig.from_pretrained(MODEL_NAME, cache_dir=CACHE_DIR...