Given that the script works fine (i.e., not run into the out of memory issue) on a single machine, I would expect multi-node to be the same. Any insight into what might be going on is appreciated! 👍2Novaal and zh-plus reacted with thumbs up emoji ...
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch") Trainer 包含了模型,训练的参数,训练集,测试集,指标参数 from transformers import TrainingArguments, Trainer training_args = TrainingArguments( 'test-trainer', per_device_train_batch=16, per_device_eavl_batch=16,...
基于上面的三个类,提供更上层的pipeline和Trainer/TFTrainer,从而用更少的代码实现模型的预测和微调。因...
这样子使用是OK的,但是这样子处理之后,tokenized_dataset不再是一个dataset格式。而且是一旦我们的dataset 过大,无法放在 RAM 中,那么这样子的做法会导致 Out of Memory 的异常。 然而Datasets库使用的是 Apache Arrow 文件格式,所以你只需要加载你需要的样本到内存中就行了,不需要全量加载。 为了使我们的数据保持da...
there is a bug in CPOTrainer. when runing CPOTrainer after runing sevreal steps, the usage of gpu memory increases and it raises the out-of-memory exception. we found that the exception is caused by missing the "detach" in line 741 of CP...
+ PEFT。确保在创建模型时使用device_map=“auto”,transformers trainer会处理剩下的事情。
# 然后,脚本使用Trainer在支持摘要的架构上对数据集进行微调。# 下面的示例展示了如何在CNN/DailyMail数据集上微调T5-small。# 由于T5模型的训练方式,它需要一个额外的source_prefix参数。这个提示符让T5知道这是一个汇总任务.pythonexamples/pytorch/summarization/run_summarization.py\--model_name_or_patht5-small...
accelerator.process_index=1 GPU Memory before entering the loading : 0 accelerator.process_index=1 GPU Memory consumed at the end of the loading (end-begin): 0 accelerator.process_index=1 GPU Peak Memory consumed during the loading (max-begin): 0 ...
Model.from_pretrained(checkpoint)# Transformer 输出的向量有三个维度:# Batch size,单次处理的句子数# Sequence size,经分词器处理后的句子的长度# Hidden size,模型输入的向量维度outputs=model(**inputs)print(outputs.last_hidden_state.shape)# 也可以是outputs['last_hidden_state']# Out: torch.Size([2...
登陆后复制torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 106.00 MiB. GPU 可以发现batch_size对于显存的占用的确有明显影响,但是对训练时间影响不大 接下来我们对模型进行半精度的加载方式 登陆后复制1 登陆后复制model = AutoModelForCausalLM.from_pretrained("Qwen2-0.5B-Instruct",low...