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...
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'...
在这种情况下,您实际上可以使用完全相同的代码通过 HuggingFace Accelerate 在 CPU/GPU/多 GPU/TPU 上...
Reward trainer multi-gpu eval bug #513 Merged Contributor rlindskog commented Jul 12, 2023 I believe I fixed in #513. Just need to send labels to the correct device. labels = torch.zeros(logits.shape[0]) labels = self._prepare_input(labels) ️ 1 younesbelkada closed this as...
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...
这些trainer为了吸引更多人使用,肯定要加尽可能多的功能,比如基本的日志、tensorboard、断点重训、训练时...
你可以轻松地使用 SFTTrainer 和官方脚本对 Llama2 模型进行微调。例如,要对 llama2-7b 在 Guanaco 数据集上进行微调,请运行以下命令(已在单个 NVIDIA T4-16GB 上进行了测试): ''' python examples/scripts/sft_trainer.py --model_name meta-llama/Llama-2-7b-hf --dataset_name timdettmers/openassistant...
from transformers import TrainingArguments, Trainer training_args = TrainingArguments( output_dir='pegasus-samsum', num_train_epochs=1, warmup_steps=500, per_device_train_batch_size=1, per_device_eval_batch_size=1, weight_decay=0.01, logging_steps=10, push_to_hub=True, ...
I am trying to fine-tune Llama 2 7B with QLoRA on 2 GPUs. From what I've read SFTTrainer should support multiple GPUs just fine, but when I run this I see one GPU with high utilization and one with almost none: Expected behaviour would b...
I am fine-tuning a BERT model for a multiclass classification task. My problem is that I don't know how to add "early stopping" to those Trainer instances. Any ideas? python deep-learning neural-network huggingface-transformers huggingface ...