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...
Even when we set the batch size to 1 and use gradient accumulation we can still run out of memory when working with large models. In order to compute the gradients during the backward pass all activations from the forward pass are normally saved. This can create a big memory overhead. Alt...
from transformers import TrainingArguments, Trainer 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_de...
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。 数据读取 Datasets library 提供给了数据集快速下载的...
I tried using device_map={"":0}, but I am still encountering an Out of Memory (OOM) error. Here are my LoRA params: Sign up for freeto join this conversation on GitHub. Already have an account?Sign in to comment 8 participants
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 ...
3. 使用trl和SFTTrainer指令微调 Llama 2 我们将使用最近在由 Tim Dettmers 等人的发表的论文“QLoRA: Quantization-aware Low-Rank Adapter Tuning for Language Generation”中介绍的方法。QLoRA 是一种新的技术,用于在微调期间减少大型语言模型的内存占用,且并不会降低性能。QLoRA 的 TL;DR; 是这样工作的: ...
[np.nan, np.nan, np.nan]])and when you apply.argmax(-1)to this, you get torch.tensor(0). The big mystery for me is why the logits would become "nan", because the model does not do that when I use the same input data only outside of the trainer. => Does anyone ...
Step 4: Train the EncoderDecoderModel using theTrainerclass from thetransformerslibrary You can use theTrainerclass from thetransformerslibrary to train the EncoderDecoderModel using the Huggingface dataset. You need to specify the model, the training arguments, the data collator, t...
# 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...