然后就是使用AutoModelForCausalLM.from_pretrained加载基本模型,在第31行设置了model.config。use_cache为False,当启用缓存时可以减少变量。禁用缓存则在执行计算的顺序方面引入了一定程度的随机性,这在微调时非常有用。 在第32行设置了model.config.pretraining_tp = 1这里的tp代表张量并行性,根据这里的Llama 2的提...
load_in_4bit=use_4bit, bnb_4bit_use_double_quant=use_double_nested_quant, bnb_4bit_quant_type=bnb_4bit_quant_type, bnb_4bit_compute_dtype=compute_dtype ) # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = Fals...
quantization_config=bnb_config, use_cache=False, device_map="auto")model.config.pretraining_tp = 1# 通过对比doc中的字符串，验证模型是在使用flash attentionif use_flash_attention:from utils.llama_patch import forward assert model.model.layers[].self_attn.forward.__doc__ == forward.__doc_...
bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) # 使用量化配置加载预训练模型 model = AutoModelForCausalLM.from_pretrained( model_name, device_map=device,torch_dtype=compute_dtype, quantization_config=bnb_config, ) model.config.use_cache = False model.config.pretrainin...
quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token ...
model=AutoModelForCausalLM.from_pretrained(model_id,quantization_config=bnb_config,use_cache=False,device_map=device_map)model.config.pretraining_tp=1# Load the tokenizer tokenizer=AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)tokenizer.pad_token=tokenizer.eos_token ...
model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=bnb_config, use_cache =False,device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True) ...
model.config.use_cache=Falsetrainer.train(resume_from_checkpoint=resume_from_checkpoint)model.save_pretrained(output_dir) 注意: 在步骤 3 中,设置 TrainingArguments 中的参数以提高调优性能。请注意,当使用 BF16 和 FP32 混合数据类型时,设置 BF16=True 会带来更平衡的调优性能和模型精度。
quantization\_config=bnb\_config, use\_cache = False, device\_map=device\_map\) model.config.pretraining\_tp = 1 \# Load the tokenizer tokenizer = AutoTokenizer.from\_pretrained\(model\_id, trust\_remote\_code=True\) tokenizer.pad\_token = tokenizer.eos\_token ...
use_auth_token=True ) base_model.config.use_cache = False # More info: https://github.com/huggingface/transformers/pull/24906 base_model.config.pretraining_tp = 1 peft_config = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64,