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...
lora_dropout = 0.05, # lora_dropout inference_mode = False, # 是否使用推理模式 bias = "none", # 偏置 task_type = "CAUSAL_LM", # 任务类型 ) # 步骤4:bnb配置 bnb_config = BitsAndBytesConfig( # bnb配置 load_in_4bit=True, # 是否使用4bit bnb_4bit_use_double_quant=True, # 是否使...
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.print_trainable_parameters() 3、加载数据 dataset =...
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=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.
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) ...
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...
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) ...