( r=lora_r, lora_alpha=lora_alpha, target_modules=modules, lora_dropout=lora_dropout, # modules_to_save=modules_to_save, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) model.print_trainable_parameters() # Be more transparent about the % of trainable ...
model.print_trainable_parameters() 通过print_trainable_parameters方法可以查看到P-Tuning v2可训练参数的数量(仅为1,474,560)以及占比(仅为0.2629%)。 trainable params: 1,474,560 || all params: 560,689,152 || trainable%: 0.26299064191632515 PEFT 中 Prefix Tuning 相关的代码是基于清华开源的P-tuning-...
peft_model = get_peft_model(model, peft_config) print('PEFT Model') peft_model.print_trainable_parameters() peft_lora_finetuning_trainer = get_trainer(peft_model) peft_lora_finetuning_trainer.train() peft_lora_finetuning_trainer.evaluate() 可以看到 模型参数总计:125,537,288,而LORA模型的训练...
model = get_peft_model(model, lora_config) model.print_trainable_parameters() trainer = SFTTrainer( model, train_dataset=to_train_dataset, eval_dataset = test_dataset, dataset_text_field=text_field, max_seq_length=2048, args=training_args, ) #Upcast layer norms to float 32 for stability ...
print(f'{total_trainable_params:,} training parameters.') return total_params, total_trainable_params cnn_paras_count(net) 直接输出参数量,然后自己计算 需要注意的是,一般模型中参数是以float32保存的,也就是一个参数由4个bytes表示,那么就可以将参数量转化为存储大小。
print(dataset) 6.5 抽取模型参数 def print_number_of_trainable_model_parameters(model): trainable_model_params = 0 all_model_params = 0 for _, param in model.named_parameters(): all_model_params += param.numel() if param.requires_grad: ...
With LoRA, the output from print_trainable_parameters()indicates we were able to reduce the number of model parameters from 7 billion to 3.9 million. This means that only 5.6% of the original model parameters need to be updated. This significant reduct...
from transformers import AutoModelForCausalLMfrom peft import get_peft_modelmodel = AutoModelForCausalLM.from_pretrained(model_id)lora_model = get_peft_model(model, peft_config)lora_model.print_trainable_parameters()训练完成后，可以单独保存适配器，也可以将它们合并到模型中。# Save only adapaters...
本文先对Qwen2模型结构进行一览,做到心中有数,之后讲解如何编写print_trainable_parameters(model)方法,如何冻结所有参数,如何... 17210 【AI大模型】Transformers大模型库(六):torch.cuda.OutOfMemoryError: CUDA out of memory解决 模型cudamemorymodeltorch ...
model.summary()是Keras中的一个函数,用于打印出模型的概要信息,包括模型的层结构、参数数量和每一层的输出形状。它通常用于检查模型的结构是否与预期一致。 当model.summary...