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,...
CUDA out of memory when training Llama-2-7b-hf model locally I want to finetune meta-llama/Llama-2-7b-hf locally on my laptop. I am running out of CUDA memory when instantiating the Trainer class. I have 16Gb system RAM and a GTX 1060 with 6 Gb of GPU memory. I ... ...
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...
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。 因此它不是一个基础的神经网络库来一步一步构造Transformer,而是把常见的Transformer模型封装成一个building block,我们可以方便的在PyTorch或者TensorFlow里使用它。
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...
run_clm_no_trainer.py 运行accelerate config命令后得到的 FSDP 配置示例如下: compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: FSDP fsdp_config: min_num_params: 2000 offload_params:false sharding_strategy: 1 machine_rank: 0 ...
training_args = TrainingArguments( output_dir="bloom_finetuned", max_steps=2048 * 3, num_train_epochs=3, per_device_train_batch_size=1, per_device_eval_batch_size=1, learning_rate=2e-5, weight_decay=0.01, fp16=True, no_cuda=False, evaluation_strategy="epoch", save_strategy="epoch"...