lora_config)unet=get_peft_model(unet,lora_config)# 如果设置为继续训练,则加载上一次的模型权重,当然,你可以修改 model_path 来指定其他的路径ifresume:# 加载上次训练的模型权重,注意这里只加载权重,而不是覆盖整个模型,覆盖:model = torch.load(...)text_encoder=torch.load(os.path...
from transformers import AutoModelForCausalLMfrom peft import get_peft_modelmodel = AutoModelForCausalLM.from_pretrained(model_id)lora_model = get_peft_model(model, peft_config)lora_model.print_trainable_parameters()训练完成后，可以单独保存适配器，也可以将它们合并到模型中。# Save only adapaters...
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) peft_lora_fine...
lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type=TaskType.CAUSAL_LM ) model = prepare_model_for_kbit_training(model) model_lora = get_peft_model(model, lora_config) model_lora.print_trainable_parameters(...
如上图,在modellink/training.py代码逻辑中,若lora被enable,会在GPTModel上调用get_peft_model包装成一个PeftModel,然而修改的只是拷贝,返回的原始的model列表中还是GPTModel wangbo 创建了缺陷 6个月前 glhyy 将任务状态从TODO 修改为WIP 6个月前 展开全部操作日志 line290 4个月前 蹲一下后续 分支bk_...
model_path, device_map='auto', quantization_config=nf4_config, ) model.resize_token_embeddings(len(tokenizer)) model = get_peft_model(model, lora_config) model.print_trainable_parameters() trainer = SFTTrainer( model, train_dataset=to_train_dataset, ...
Use the [get_peft_model] function to create a [PeftModel] from the base facebook/opt-350m model and thelora_configyou created earlier. frompeftimportget_peft_modellora_model=get_peft_model(model,lora_config)lora_model.print_trainable_parameters()"trainable params: 1,572,864 || all params...
BitsAndBytesConfig, TrainingArguments, pipeline, logging, ) from peft import LoraConfig, PeftModel from trl import SFTTrainer 我们继续分析导入 torch是我们很熟悉的深度学习库,这里我们不需要torch的那些低级功能,但是它是transformers和trl的依赖,在这里我们需要使用torch来获取dtypes(数据类型),比如torch.Float16...
, use_rslora = False, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) alpaca_prompt = """Below is a question with an answer that provides a clear explanation. ### Question: {} ### Response: {} """
from peft import AutoPeftModelForCausalLMmodel = AutoPeftModelForCausalLM.from_pretrained( args.output_dir, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()#...