scheduler=get_linear_schedule_with_warmup(optimizer,num_warmup_steps=0,num_training_steps=total_steps)# Training looploss_list=[]forepochinrange(num_epochs):model_with_head.train()forstep,(texts,labels)inenumerate(train_dataloader):labels=labels.to(model.device)optimizer.zero_grad...
1.0e-4 num_train_epochs: 3.0 lr_scheduler_type: cosine warmup_ratio: 0.1 bf16: true ddp_timeout: 180000000 ### eval # val_size: 0.1 # per_device_eval_batch_size: 1 # eval_strategy: steps # eval_steps: 500 数据集示例 [ { "messages": [ { "role": "user", "content": ...
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warmupCosineLRWarmupEpochs integer (int32) Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. weightDecay number (float) Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. ...
(), logging_steps=1, optim="adamw_8bit", weight_decay=0.01, lr_scheduler_type="linear", seed=3407, output_dir="outputs", report_to="mlflow", evaluation_strategy=IntervalStrategy.STEPS, eval_steps=20, save_total_limit=5, load_best_model_at_end=True, metric_for_best_model="eval_loss...
VisitNVIDIA/NeMoon GitHub to get started with LLM customization. You are also invited tojoin the open beta. Related resources GTC session:Watch Your Language: Create Small Language Models That Run On-Device GTC session:Efficient Large Language Model Customization ...
[example.labelforexampleinbatch])returntexts,labels train_dataloader=DataLoader(train_examples,shuffle=True,batch_size=batch_size,collate_fn=collate_fn)# Define the loss function, optimizer, and learning rate scheduler.criterion=nn.CrossEntropyLoss()optimizer=AdamW(model_with_head.paramet...
--scheduler warmup_cosine \ --lr 0.3 \ --weight_decay 1e-4 \ --batch_size 128 \ --brightness 0.4 \ --contrast 0.4 \ --saturation 0.2 \ --hue 0.1 \ --gaussian_prob 1.0 0.1 \ --solarization_prob 0.0 0.2 \ --num_crops_per_aug 1 1 \ --name barlow-400ep-accida \ --proje...
pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n!pip install -q datasets bitsandbytes einops wandb","metadata":{"execution":{"iopub.status.busy":"2023-10-17T20:52:18.132652Z","iopub.execute_input":"2023-10-17T20:52:18.132930Z","iopub.status....
Embeddings are initialized to a small value but are then immediately normalized. This immediately creates an embedding space that is well distributed around the unit hypersphere, and converges in a rapid fashion with desirable qualities, even with no warmup. ...