requires_grad: trainable_params += num_params print(f"可训练参数量: {trainable_params}") print(f"总参数量: {all_params}") print(f"可训练参数占比: {100 * trainable_params / all_params:.2f}%") print_trainable_parameters(model) 输出: 可训练参数量: 294912 总参数量: 124734720 可训练...
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-...
通过调用 get_peft_model 包装基础 🤗 Transformer 模型 model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)+ model = get_peft_model(model, peft_config)+ model.print_trainable_parameters()# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.1915105310011...
model = get_peft_model(model, lora_config) model.print_trainable_parameters() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. ● AdaLoRA AdaLoRA是LoRA的一个变体,它进一步改进了LoRA的参数效率。AdaLoRA通过考虑LoRA矩阵的奇异值作为其重要性的指标,来动态地为不同的适配器分配不同的秩。这...
( 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 ...
inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) model = get_peft_model(model, peft_config) model.print_trainable_parameters() # output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19...
print_trainable_parameters() "trainable params: 2359296 || all params: 1231940608 || trainable%: ...
model.print_trainable_parameters() 主要是这一句:model = get_peft_model(model, peft_config),所以在这里设置断点。 首先跳转到:peft->mapping.py->get_peft_model函数。我逐行阅读并做出中文注释。 defget_peft_model(model: PreTrainedModel, peft_config: PeftConfig, adapter_name:str="default") -> Peft...
reft_config=ReftConfig(representations={"layer":19,"component":"block_output","intervention":LoreftIntervention(embed_dim=model.config.hidden_size,low_rank_dimension=1),})reft_model=get_reft_model(model,reft_config)reft_model.print_trainable_parameters() ...
from_pretrained(model_name_or_path) model = get_peft_model(model, peft_config) model.print_trainable_parameters() # output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282Use CasesGet comparable performance to full finetuning by adapting LLMs to downstream ...