llamafactory-cli train examples/train_full/qwen_pt.yaml Expected behavior 问题:使用llamafactory预训练模型时,max_token=8192的情况下,为什么perdevice_batchsize =2 ,会超出4卡A100 40G的显存,而perdevice_batchsize = 1 的时候只会占用每卡20G。 O
在image_classification_timm_peft_lora模型微调任务时,训练这一步报错:KeyError: 'per_gpu_train_batch_size',但是在args中两句代码是这样的:per_device_train_batch_size=batch_size,per_device_eval_batch_size=batch_size并没有问题。 Environment / 环境信息 (Mandatory / 必填) -- MindSpore version : 2.3....
--model_name_or_path /data/baichuan-13b-chat --do_train --template baichuan --dataset self_cognition --finetuning_type full --output_dir output/baichuan-13b --per_device_train_batch_size 1 --gradient_accumulation_steps 1 --preprocessing_num_workers 16 --lr_scheduler_type cosine --logging...
# 初始化梯度缩放器 scaler = GradScaler() # 设置训练参数 training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, fp16=True, # 启用自动混合精度 ) # 创建 Trainer 实例 trainer = Trainer( model=model, args=training_args, optimizer=...
I want to train the model in .mat dataset but i am getting the memory error my dataset size is [256,340,2] when i try gpuDevice(1) ans = CUDADevicewith properties: Name:'NVIDIA GeForce GTX 1080 Ti' Index: 1 ComputeCapability:'6.1' ...
train() Dtr, Dte = nn_seq_wind(model.name, 50) optimizer = torch.optim.Adam(model.parameters(), lr=args.alpha) loss_function = nn.MSELoss().to(args.device) loss = 0 for epoch in range(1): for seq, label in Dtr: seq, label = seq.to(args.device), label.to(args.device) y...
parser.add_argument('--start-epoch', default=0, type=int, help='manual epoch number')parser.add_argument('--batch-size', default=128, type=int, help='mini-batch size')parser.add_argument('--optimizer', default='sgd', help='optimizer function used')parser.add_argument('--lr', ...
在pytorch内部,conf.device_ids依旧是从0开始的; 训练的时候报错: 是由于batchnorm层需要大于一个样本去计算其中的参数,网上搜索了解决方法是将dataloader的一个丢弃参数设置为true: 但是我设置后依旧会报错,然后就在train里面人为修改了一下: 如果剩下的照片为1,那么就丢掉,就可以了:...
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical res
3.解决方法:在torch.utils.data.DataLoader类中或自己创建的继承于DataLoader的类中设置参数drop_last=True,把不够一个batch_size的数据丢弃。成功解决。22.'NoneType' object has no attribute 'parameters' model.parameters()这个问题是python变量的问题,即model是一个NoneType的类型,这是一个空类型,说明你的model...