peft_model_id = f"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}" model.save_pretrained(peft_model_id) 输出的模型权重文件如下所示: /data/nfs/llm/model/bloomz-560m_LORA_CAUSAL_LM ├── [ 447] adapter_config.json ├── [3.0M] adapter_model.bin └── [ ...
+ model = PeftModel.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) model = model.to(device) model.eval() inputs = tokenizer("Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I wil...
peft_model_id="smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM"config=PeftConfig.from_pretrained(peft_model_id)model=AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)+model=PeftModel.from_pretrained(model,peft_model_id)tokenizer=AutoTokenizer.from_pretrained(c...
+ model = PeftModel.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) model = model.to(device) model.eval() inputs = tokenizer('Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I wil...
加载LoRA参数的时候调用的是model = PeftModel.from_pretrained(model, model_id)函数,这个参数会调用PeftModel.load_adapter函数 (图5 278行)。然后load_adapter函数中有load_peft_weights (图6 554),会加载所有保存的参数,包括modules_to_save。然后557行set_peft_model_state_dict会把这些参数拷贝到模型里面。
torch.manual_seed(0)# modelmodel_id ="facebook/opt-125m"model = AutoModelForCausalLM.from_pretrained(model_id)# inputinput= torch.tensor([[1,2,3,4,5]])# base outputoutput_base = model(input).logitsprint("Base model output:")print(output_base[0, :3, :5])# lora model, with tw...
model_id="/root/work/chatglm3-6b"model=AutoModel.from_pretrained(model_id,trust_remote_code=True)tokenizer=AutoTokenizer.from_pretrained(model_id,trust_remote_code=True) 1. 2. 3. 4. 5. 加载数据集 fromdatasetsimportload_dataset data_id="/root/work/jupyterlab/zd.json"dataset=load_dataset(...
- peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto" - ) - model = PeftModel.from_pretrained(model, peft_model_id) + model = AutoPeftModel.from_pretrained(peft_model_id) ``` ## Next steps 1 change: 1 addition & 0 deletions 1 src/peft/__init__.py ...
reft_model=get_reft_model(model,reft_config) reft_model.print_trainable_parameters() 剩下的代码就和HuggingFace训练模型没有任何的区别了,我们来做一个完整的演示: frompyreftimport( ReftTrainerForCausalLM, make_last_position_supervised_data_module ...
(model,peft_model_id)tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)model=model.to(device)model.eval()inputs=tokenizer("Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again. Label :",return_...