OSError: ./bert-base-chinese doesnotappear to have a file named config.json. Checkout'https://huggingface.co/./bert-base-chinese/None'foravailable files. 那么,如果checkpoint文件有tokenizer.json和config.json: 1 说明: 使用from_pretrained()函数加载模型需要tokenizer.json和config.json文件。但是我们还...
问from_config与from_pretrained在HuggingFace中的差异EN老肥近期参加Kaggle平台的比赛，因为比赛类型为Code ...
在Hugging Face中,config.json文件是用于配置预训练模型参数的文件。这个文件通常包含了模型的架构、超参数和其他模型配置信息。它是一个JSON格式的文件,可以在加载模型时用来初始化模型的配置。 在加载模型时,from_pretrained()方法通常会自动加载相应的config.json文件。例如,BertForSequenceClassification.from_pretrained(...
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = { "roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json", "roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json", "roberta-large-mnli": "https://s3.amazonaws.com/mo...
from_pretrained(model_name_or_path) 179 model = get_peft_model(model, peft_config) 得到的模型为: ipdb> model BloomForCausalLM( (transformer): BloomModel( (word_embeddings): Embedding(250880, 1024) (word_embeddings_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) (...
from trl import SFTTrainer dataset = load_dataset("imdb", split="train") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") peft_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", ...
from datasets import load_dataset from trl import SFTTrainer dataset = load_dataset("imdb", split="train") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") peft_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, ...
import os os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' #加上这行之后又恢复以前的速度了! from transformers import AutoModel, AutoConfig, AutoTokenizer bert_model = AutoModel.from_pretrained('bert-base-uncased') config = AutoConfig.from_pretrained('bert-base-uncased') ...
model = AutoModelForSequenceClassification.from_pretrained("gpt2") peft_config = LoraConfig(task_type=TaskType.SEQ_CLS,inference_mode=False,r=8,lora_alpha=32,lora_dropout=0.1, ) trainer = RewardTrainer(model=model,args=training_args,tokenizer=tokenizer,train_dataset=dataset,peft_config=peft_confi...
from_pretrained( ckpt_id, subfolder="transformer", quantization_config=nf4_config, torch_dtype=torch.bfloat16 ) Then, we use model_nf4 to instantiate the FluxPipeline: from diffusers import FluxPipeline pipeline = StableDiffusion3Pipeline.from_pretrained( ckpt_id, transformer=model_nf4, torch_...