首先下载英文和西班牙文子集,如下。 fromdatasetsimportload_datasetspanish_dataset=load_dataset("amazon_reviews_multi","es")english_dataset=load_dataset("amazon_reviews_multi","en")english_datasetDatasetDict({train:Dataset({features:['review_id','product_id','reviewer_id','stars','review_body'...
Currently, I am trying to fine tune the Korean Llama model(13B) on a private dataset through DeepSpeed and Flash Attention 2, TRL SFTTrainer. I am using 2 * A100 80G GPUs for the fine-tuning, however, I could not conduct the fine-tuning.. I can't find out the problem and any solu...
我使用了以下Python脚本之一(例如:run_clm.py),其中trainer.train()在那里:https://github.com/...
HuggingFace 提供如下的training_args。当我使用 HF trainer 训练模型时,我发现默认使用 cuda:0。我浏览了 HuggingFace 文档,但仍然不知道在使用 HF 训练器时如何指定在哪个 GPU 上运行。training_args = TrainingArguments( output_dir='./results', # output directory num_train_epochs=3, # total # of ...
+ PEFT。确保在创建模型时使用device_map=“auto”,transformers trainer会处理剩下的事情。
config: -compute_environment: LOCAL_MACHINE - distributed_type: MULTI_GPU - mixed_pr...
首先我们来看一下在hugging face中的trainer.py[2] 在传入的class transformers.TrainingArguments配置文件类中,我们可以发现一个叫做gradient_checkpointing的入参。当这个参数为True时,将启用检查点技术来节省显存。 该参数描述如下: gradient_checkpointing(bool,optional, defaults toFalse) — If True, use gradient...
trainer.train() score = evaluate_summaries_pegasus( dataset_samsum["test"], rouge_metric, trainer.model, tokenizer, batch_size=2, column_text="dialogue", column_summary="summary") rouge_dict = dict((rn, score[rn].mid.fmeasure) for rn in rouge_names) ...
The example of multi-gpu training on the SFTrainer docs shows that I should load the GPU into memory, but this doesn't work if the model doesn't fit into memory in the first place. Is there any guidance somewhere on how to use FSDP with SFTtrainer for models that don't fit in one...
For Whisper multi-gpu naive pp using accelerate and peft and trainer, following changes are required: Base model loading: from transformers import WhisperForConditionalGeneration import copy from accelerate import dispatch_model model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, lo...