LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, r=8, target_modules={'up_proj', 'k_proj', 'o_proj', 'gate_proj', 'q_proj', 'v_proj', 'down_...
第一步,引进必要的库,如:LoRA 配置类LoraConfig。 from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType 第二步,创建 LoRA 微调方法对应的配置。 peft_config = LoraConfig( task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, l...
//github.com/huggingface/peft.gitpip install -q -U git+https://github.com/huggingface/accelerate.git然后就是代码：from transformers import ( AutoModelForCausalLM, BitsAndBytesConfig)from peft import ( get_peft_model, LoraConfig, TaskType, prepare_model_for_kbit_training)# Im...
from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) model = prepare_model_for_int8_training(model) lora_config = LoraConfig( task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=8, lora_dropout=0.05, ) model = get_peft_model(model, lora_config) model.config....
task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) 3、进行推理 from transformers import AutoModelForSeq2SeqLM + from peft import PeftModel, PeftConfig peft_model_id = "smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM" ...
task_type=TaskType.CAUSAL_LM, ) 还可以针对transformer架构中的所有密集层: # From https://github.com/artidoro/qlora/blob/main/qlora.py def find_all_linear_names(args, model): cls = torch.nn.Linear lora_module_names = set() for name, module in model.named_modules(): ...
class LoraWrapperRoberta(nn.Module):def __init__(self, task_type, num_classes=None, dropout_rate=0.1, model_id="roberta-large",lora_rank=8, train_biases=True, train_embedding=False, train_layer_norms=True):"""A wrapper for RoBERTa with Low-R...
task_type=TaskType.CAUSAL_LM, inference_mode=True, r=8, lora_alpha=32, lora_dropout=0.1, target_modules=['query_key_value'], ) model = get_peft_model(model, peft_config).float() count_params(model) if __name__ == '__main__': ...
model = LoraWrapperRoberta(task_type='glue') """super().__init__() # 1. Initialize the base model with parametersself.model_id = model_idself.tokenizer = RobertaTokenizer.from_pretrained(model_id)self.model = RobertaModel.from_pretrained(model_id)self.model_config =self.model.config ...
def __init__(self, task_type, num_classes=None, dropout_rate=0.1, model_id="roberta-large", lora_rank=8, train_biases=True, train_embedding=False, train_layer_norms=True): """ A wrapper for RoBERTa with Low-Rank Adaptation (LoRA) for various NLP tasks. ...