\quad 加载一个基础的预训练模型以进行训练,然后添加一个适配器配置来指定如何调整模型参数。 from transformers import AutoModelForCausalLM from peft import LoraConfig model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") config = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64, bias...
from_pretrained(model_name, quantization_config=quant_config, device_map={"":0}) model.gradient_checkpointing_enable() model = prepare_model_for_kbit_training(model) config = LoraConfig( r=8, lora_alpha=32, target_modules=["query_key_value"], lora_dropout=0.05, bias="none", task_...
num_virtual_tokens=10) model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path...
LoRA是一种用于高效训练大型语言模型的技术。它通过引入两个低秩矩阵A和B来减少训练参数的数量。原始的大型语言模型可能需要训练数百万到数十亿个参数,而LoRA只训练这两个矩阵,从而大幅减少了参数数量。LoRA的一个关键优点是,降低了训练资源的消耗,显著加快模型的训练速度。 from peft import LoraConfig, get_peft_mod...
from pyreftimport(ReftTrainerForCausalLM,make_last_position_supervised_data_module)tokenizer=transformers.AutoTokenizer.from_pretrained(model_name_or_path,model_max_length=2048,padding_side="right",use_fast=False)tokenizer.pad_token=tokenizer.unk_token ...
complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)+ model = PeftModel.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(config.base...
PeftModel.from_pretrained() 的参数 is_trainable 默认为 False，所以使用 param.requires_grad 进行判断...
迁移学习算法:包括模型迁移、参数共享和模型集成等。 自适应学习率策略:包括学习率调整、权重更新和训练效果评估等。 2.2 PEFT技术核心算法 微调算法: 损失函数:用于衡量模型预测值与真实值之间的差距,常用的损失函数有交叉熵损失函数、均方误差损失函数等。
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) model = model.to(device) model.eval() inputs = tokenizer("Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again. Label :", return_tensors=...
代码 # 根据参数选择加载到CPU或自动选择设备 if args.device == 'auto': device_arg = {'device_map': 'auto'} else: device_arg = {'device_map': {"": args.device}} print(f"Loading base model: {args.base_model_name_or_path}") base_model = AutoModelForCausalLM.from_pretrained( args...