device_map ="cuda:0"iftorch.cuda.is_available()else"auto" model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,device_map=device_map,torch_dtype=torch.float16,load_in_8bit=True,trust_remote_code=True,use_flash_attention_2=True) # 加载微调模型权重参数 # 例如: fine...
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_...
( model_name, load_in_8bit=True, device_map="auto", use_auth_token=True ) model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b", adapter_name="eng_alpaca") model.load_adapter("22h/cabrita-lora-v0-1", adapter_name="portuguese_alpaca") model.set_adapter("eng_alpaca") ...
.base_model_name_or_path,\n load_in_8bit=False,\n return_dict=True,\n device_map="auto",\n torch_dtype=torch.float16,\n low_cpu_mem_usage=True,\n)\nmodel = PeftModel.from_pretrained(\n model,\n output_dir,\n torch_dtype=torch.float16,\n device_map="auto",\n)\nmodel....
model=transformers.AutoModelForCausalLM.from_pretrained( model_name_or_path,torch_dtype=torch.bfloat16,device_map="cuda" ) # Wrap the model with rank-1 constant reFT reft_config=ReftConfig( representations={ "layer":19, "component":"block_output", ...
model_name_or_path, torch_dtype=torch.bfloat16, device_map="cuda" ) # Wrap the model with rank-1 constant reFT reft_config = ReftConfig( representations={ "layer": 19, "component": "block_output", "intervention": LoreftIntervention( ...
@@ -146,6 +146,10 @@ def test_peft_model_device_map(self, test_name, model_id, config_cls, config_kwa def test_delete_adapter(self, test_name, model_id, config_cls, config_kwargs): self._test_delete_adapter(model_id, config_cls, config_kwargs) @parameterized.expand(PeftTestConfig...
bnb_4bit_use_double_quant=True,#添加nf4配置,去掉为fp4)model=AutoModelForCausalLM.from_pretrained(model_dir,device_map=device,trust_remote_code=True,torch_dtype=torch.float16,quantization_config=quantization_config)print(model) 输出模型结构,可以看到Attention和MLP层中的Linear线性层全部变成了linear4bit...
map(process_func, batched=True) # 创建模型和训练参数 model = AutoModelForCausalLM.from_pretrained("bigscience/bloom") training_args = TrainingArguments( output_dir="./chatbot", per_device_train_batch_size=1, num_train_epochs=3, save_steps=1000, save_total_limit=2 ) # 配置训练器并训练...
def test_inference_safetensors(self, test_name, model_id, config_cls, config_kwargs): self._test_inference_safetensors(model_id, config_cls, config_kwargs) @parameterized.expand(TEST_CASES) def test_peft_model_device_map(self, test_name, model_id, config_cls, config_kwargs): self._...