truncation=True,max_length=context_length,return_overflowing_tokens=True,return_length=True,)print(f"Input IDs length: {len(outputs['input_ids'])}")print(f"Input chunk lengths: {(outputs['length
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...
# Update the model parameters with the optimizer optimizer.step() optimizer.zero_grad() if(epoch +1) %5==0: loss_last_epoch = sum(losses [-len(train_dataloader) :]) /len(train_dataloader) print(f"Epoch:{epoch+1}, loss: {loss_last_epoch}") Epoch:5, loss:0.16273280512541533 Epoch:1...
# 训练下游任务模型 from transformers import AdamW # 训练 optimizer = AdamW(model.parameters(),lr=5e-4) criterion = torch.nn.CrossEntropyLoss() model.train() for i,(input_ids,attention_mask,token_type_ids,labels) in enumerate(loader): out = model(input_ids=input_ids, attention_mask=attent...
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...
model.zero_grad() loss, logits= model(batch[0].to(device), token_type_ids=None, attention_mask=(batch[0]>0).to(device), labels=batch[1].to(device)) total_loss+=loss.item() loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(),1.0) ...
optimizer=AdamW(model.parameters())loss=model(**batch).loss #这里的 loss 是直接根据 batch 中提供的 labels 来计算的,回忆:前面章节查看 model 的输出的时候,有loss这一项 loss.backward()optimizer.step() 从Huggingface Hub中加载数据集 这里,我们使用MRPC数据集,它的全称是Microsoft Research Paraphrase Corp...
optimizer = AdamW(model.parameters(),lr = 2e-5,correct_bias=False) total_steps = len(train_data_loader)*EPOCHS scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=0, num_training_steps=total_steps) loss_fn = nn.CrossEntropyLoss().to(device) def train...
What I need A way to run this code to any LLM by programmatically setting model parameters, settings, etc instead on RunPod webui
LM.from_pretrained(base_model, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", low_cpu_mem_usage=True) total_parameters = sum(p.numel() for p in model.parameters()) print(f"Total number of parameters: {total_parameters}"...