max_steps (int, 可选, 默认为 -1):如果设置为正数,就是执行的总训练步数,会覆盖num_train_epochs。注意如果使用此参数,就算没有达到这个参数值的步数,训练也会在数据跑完后停止。 lr_scheduler_type (str, 可选, 默认为"linear"):用于指定学习率scheduler的类型,根据训练的进程来自动调整学习率。详细见: "...
max_steps=40,因为step是根据accumulate_grad_batches来计数的,所以max_steps=40意味着最多训练40*5=200个micro batch every_n_train_steps=10同上,每经过10个step,即10*5=50个micro batch后,会执行一次save ckpt的操作 val_check_interval=100,这是和limit_train_batches强相关的参数,意味着每经过100个micro ...
TrainLimit( batch_size_limit=None, max_seq_len_options=None, epoch_limit=None, learning_rate_limit=(3e-05, 0.001), log_steps_limit=None, warmup_ratio_limit=None, weight_decay_limit=None, lora_rank_options=None, lora_alpha_options=None, lora_dropout_limit=None, scheduler_name_options=Non...
13. gradient_accumulation_steps (optional): 梯度累积的步数,用于提高训练效果。 14. max_steps (optional): 最大训练步数。 15. num_train_epochs (optional): 最大训练轮数。 这些参数只是Trainer类的一部分,根据具体的任务和需求,您可能还需要使用其他参数。请参考Huggingface的官方文档以获取更详细的信息和示...
(response)进行编码作为标签3. 将标签添加到模型输入中"""inputs = examples["prompt"]# 获取输入文本targets = examples["response"]# 获取目标文本# 对输入文本进行编码(包含截断和填充)model_inputs = tokenizer(inputs,max_length=256,# 最大长度truncation=True,# 启用截断padding="max_length"# 填充到最...
Total optimization steps = 938 与上面的 notebook_launcher 示例类似,也可以将这个过程封装成一个训练函数: def train_trainer_ddp(): model = BasicNet() training_args = TrainingArguments( "basic-trainer", per_device_train_batch_size=64, per_device_eval_batch_size=64, num_train_epochs=1, evalua...
max_steps = 1 # Approx the size of guanaco at bs 8, ga 2, 2 GPUs. warmup_ratio = 0.1 lr_scheduler_type = "cosine" training_arguments = TrainingArguments( output_dir=output_dir, per_device_train_batch_size=per_device_train_batch_size, ...
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(default:1.0) --max_steps 如果设置为正数,则表示要执行的训练步骤总数。 覆盖`num_train_epochs`。(`int`,可选,默认为 -1) If > 0: set total number of training steps to perform.Override num_train_epochs. (default: -1 --lr_scheduler_type 要使用的学习率调度策略。 (`str`, 可选, 默认为...
# batch size per device during training per_device_eval_batch_size=64, # batch size for evaluation logging_dir='./logs', # directory for storing logs logging_steps=100, do_train=True, do_eval=True, no_cuda=False, load_best_model_at_end=True, # eval_steps=100, evaluation_strategy="...