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...
How to choose dataset_text_field in SFTTrainer hugging face for my LLM model Note: Newbie to LLM's Background of my problem I am trying to train a LLM using LLama3 on stackoverflow c langauge dataset. LLm - meta-llama/Meta-Llama-3-8B Dataset - Mxode/StackOverflow-QA-C-Language-....
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。
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 ... python pytorch
+ PEFT。确保在创建模型时使用device_map=“auto”，transformers trainer会处理剩下的事情。
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 ...
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...
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: ...