是的,您可以使用Hugging Face库加载ModelScope中的通用检查点(checkpoint)。 ModelScope的通用检查点是以PyTorch格式保存的模型权重和配置信息。Hugging Face提供了一个名为transformers的库,它支持加载和使用各种预训练模型,包括通用检查点。 以下是使用Hugging Face加载ModelScope通用检查点的示例代码: pythonCopyfrom trans...
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') 对于因果语言建模(CLM),我们将获取数据集中的所有文本,并在标记化后将它们连接起来。然后,我们将它们分成一定序列长度的样本。这样,模型将接收连续文本块。 from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_checkp...
def __init__(self,checkpoint,num_labels): super(CustomModel,self).__init__() self.num_labels = num_labels #Load Model with given checkpoint and extract its body self.model = model = AutoModel.from_pretrained(checkpoint,config=AutoConfig.from_pretrained(checkpoint, output_attentions=True,outpu...
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') 对于因果语言建模(CLM),我们将获取数据集中的所有文本,并在标记化后将它们连接起来。然后,我们将它们分成一定序列长度的样本。这样,模型将接收连续文本块。 from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_checkp...
checkpoint="bert-base-uncased"tokenizer=AutoTokenizer.from_pretrained(checkpoint)model=AutoModelForSequenceClassification.from_pretrained(checkpoint) 下面是模型输出的warning: 代码语言:javascript 复制 >>>Some weightsofthe model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClass...
from transformers import AutoTokenizer model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)deftokenize_function(examples):returntokenizer(examples["text"]) tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])defgroup_texts(examples):# Concatenate all texts.concatenat...
num_layers)loaded_model.load_state_dict(torch.load('simple_gpt2.pth'))# 进行推理input_tensor=...
在调用timm.create_model时传入pretrained_cfg_overlay参数 其中checkpoint可以是*.safetensors,*.bin,*.pth,*.pt,*.ckpt等格式的存储模型权重的文件。 在传入pretrained_cfg_overlay=dict(file=r'path\to\checkpoint')参数后,默认的pretrained_cfg预训练 config 中会添加file=r'path\to\checkpoint键值对,导入模型...
# Load peft config for pre-trained checkpoint etc. peft_model_id = "results" config = PeftConfig.from_pretrained(peft_model_id) # load base LLM model and tokenizer model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map={"":0}) ...