lora_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", target_modules=['up_proj', 'gate_proj', 'q_proj', 'o_proj', 'down_proj', 'v_proj', 'k_proj'], task_type=TaskType.CAUSAL_LM, inference_mode=False # 训练模式 ) target_modules target_modules是 ...
peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, 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.p...
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_...
inference_mode=False, r=8, lora_alpha=8, lora_dropout=0.05, ) model = get_peft_model(model, lora_config) model.config.use_cache =False 模型显存占用分成两个部分,一部分是静态显存基本由模型参数量级决定,另一部分是动态显存在向前传播的过程中每个样本的每个神经元都会计算激活值并存储,用于向后传播...
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. 14. 15. 16. 模型显存占用分成两个部分,一部分是静态显存基本由模型参数量级决定,另一部分是...
inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1, target_modules=['query_key_value'] ) model = "加载的模型" model = get_peft_model(model, peft_config) model.print_trainable_parameters() 论文中提到了LoRA的一些优势: 1)一个预先训练好的模型可以被共享,并用于为不同的任务建立许多...
peft_config = LoraConfig(task_type="SEQ_CLS", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1) peft_model = get_peft_model(model, peft_config) print('PEFT Model') peft_model.print_trainable_parameters() peft_lora_finetuning_trainer = get_trainer(peft_model) ...
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__': make_peft_model() 1.
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 模型显存占用分成两个部分,一部分是静态显存基本由模型参数量级决定,另一部分是动态显存在向前传播的过程中每个样本的每个神经元都会计算激活值并存储...
P_TUNING peft_config = PromptEncoderConfig(task_type="SEQ_CLS", num_virtual_tokens=20, encoder_hidden_size=128) elif p_type == "lora": peft_type = PeftType.LORA peft_config = LoraConfig(task_type="SEQ_CLS", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1) # print(...