似乎在不指定task_type 的情况下一切都正常。 可能的任务类型包括 CAUSAL_LM、FEATURE_EXTRACTION、QUESTION_ANS、SEQ_2_SEQ_LM、SEQ_CLS 和 TOKEN_CLS。 7、其他参数 其余参数包括 fan_in_fan_out、modules_to_save、layers_to_transform 和layers_pattern 不太常用。 原文链接:More about LoraConfig from PEFT...
PEFT 支持广泛使用的大型语言模型低秩适应 (LoRA)。 为了从预训练的Transformer模型创建 LoRA 模型,我们导入并设置 LoraConfig。 例如, from peft import LoraConfig config = LoraConfig( r=8, lora_alpha=16, target_modules=["q", "v"], lora_dropout=0.01, bias="none" task_type="SEQ_2_SEQ_LM", ...
return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type]( model, peft_config, adapter_name=adapter_name, autocast_adapter_dtype=autocast_adapter_dtype, low_cpu_mem_usage=low_cpu_mem_usage, ) 进入MODEL_TYPE_TO_PEFT_MODEL_MAPPING中调用,这里因为是TaskType类型为"CAUSAL_LM",根据MODEL_TYP...
from peft import LoraConfig, TaskTypelora_config = LoraConfig( r=16, lora_alpha=16, target_modules=["query_key_value"] lora_dropout=0.1, bias="none", 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...
lora_config = LoraConfig( r=16, lora_alpha=16, target_modules=["query_key_value"] lora_dropout=0.1, bias="none", 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 ...
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.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__': ...
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, inference_mode=False, r=8, lora_alpha=8, lora_dropout=0.05, ) model = get_peft_model(model, lora_config) model.config.use_cache = False 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
Example: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(mode...