I'm training on RTX3090X4EA GPU environment. I need to split the GPU (for GPU memory), but if I put the dataset into DPO Trainer in Dataset format now, I get this error code, what should I do? I'm fix example of DPO training code and using it. ...
We currently have a few issues like #831 and #480 where gradient checkpointing + DDP does not work with the RewardTrainer. Let's use this issue to collect the various training modes we'd like to support and track the status of their fixe...
进行训练,API : Trainer from transformers import Trainer trainer=Trainer(model,
To enable gradient checkpointing in theTrainerwe only need to pass it as a flag to theTrainingArguments. Everything else is handled under the hood: training_args=TrainingArguments(per_device_train_batch_size=1,gradient_accumulation_steps=4,gradient_checkpointing=True,**default_args)trainer=Trainer...
pad_to_multiple_of=8 ) 最后一步是定义训练超参 ( TrainingArguments)。 from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments output_dir="lora-flan-t5-xxl" # Define training args training_args = Seq2SeqTrainingArguments( output_dir=output_dir, auto_find_batch_size=True, learning_rat...
trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT") trainer.save_model(script_args.output_dir)# 或者 , 如果整个模型小于 50 GB (即 LFS 单文件的最大尺寸),你还可以使用 trainer.push_to_hub() 把模型推到 hub 上去。
在自然语言处理领域，以 BERT 为代表的 Transformer 神经网络模型是近年来最重要的模型创新，为诸如阅读...
预训练语言模型(Pre-trained Language Model,PLM)想必大家应该并不陌生,其旨在使用自监督学习(Self-supervised Learning)或多任务学习(Multi-task Learning)的方法在大规模的文本语料上进行预训练(Pre-training),基于预训练好的模型,对下游的具体任务进行微调(Fine-tuning)。目前市面上知名的以英文为主预训练语言模型有...
importos os.environ["CUDA_VISIBLE_DEVICES"] ="1"#or"0,1"formultiple GPUs Run Code Online (Sandbox Code Playgroud) 这样,无论您的计算机上有多少个 GPU,Hugging Face Trainer 都只能查看和使用您指定的 GPU。
+ PEFT。确保在创建模型时使用device_map=“auto”，transformers trainer会处理剩下的事情。