parameters()) 559,214,592 get_peft_model > /usr/local/lib/python3.8/dist-packages/peft/mapping.py(120)get_peft_model() 119 import ipdb; ipdb.set_trace() --> 120 return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config) 121 ipdb> peft_config PromptTuning...
fromtransformersimportAdamWoptimizer=AdamW(model.parameters(),lr=5e-5) 学习率调度器默认使用的是线性衰减,从最大值5e-5降到0。 fromtransformersimportget_schedulernum_epochs=3num_training_steps=num_epochs*len(train_dataloader)lr_scheduler=get_scheduler("linear",optimizer=optimizer,num_warmup_steps=0,nu...
model.config.num_labels = 10 # print(model.get_output_embeddings) # print(model.classifier) model.classifier = nn.Linear(768,10) print(model.classifier) parameters = list(model.parameters()) for x in parameters[:-1]: x.requires_grad = False model.to(device) # 定义损失函数和优化器 crite...
noise_pred = model(noisy_images, timesteps, return_dict=False)[0] # Calculate the loss loss = F.mse_loss(noise_pred, noise) loss.backward(loss) losses.append(loss.item()) # Update the model parameters with the optimizer optimizer.step() optimizer.zero_grad() if(epoch +1) %5==0: ...
optimizer=AdamW(model.parameters(),lr=5e-5)num_epochs=3num_training_steps=num_epochs*len(train_dataloader)# numofbatches*numofepochs lr_scheduler=get_scheduler('linear',optimizer=optimizer,# scheduler是针对optimizer的lr的 num_warmup_steps=0,num_training_steps=num_training_steps)print(num_training...
(dim=1) return out model = Model() model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids).shape from transformers import AdamW # 训练 optimizer = AdamW(model.parameters(), lr=5e-4) criterion = torch.nn.CrossEntropyLoss() model.train() for i, (input...
classMyCustomRuntime(MLModel):# 加载模型 defload(self)->bool:settings=get_settings_from_env()pp=pipeline(**settings)self._model=pp # 执行推理 defpredict(self,*args,**kwargs):prediction=self._model(*args,**kwargs)returnself.serialize(prediction) ...
模型仓库(Model Repository):Git仓库可以让你管理代码版本、开源代码。而模型仓库可以让你管理模型版本、开源模型等。使用方式与Github类似。 模型(Models):Hugging Face为不同的机器学习任务提供了许多预训练好的机器学习模型供大家使用,这些模型就存储在模型仓库中。 数据集(Dataset):Hugging Face上有许多公开数据集。
{name: param for name, param in _get_named_parameters(model, remove_duplicate=False)} _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ...
│ 553 │ │ │ params: Dict[str, Any] = dict(**dict(zip(sig.parameters, │ │ 554 │ │ │ for param in sig.parameters.values(): │ │ │ │ D:\ProgramData\Anaconda3\Lib\site-packages\unstructured\chunking\title.py:2 │