import os os.environ["CUDA_VISIBLE_DEVICES"] = "1" # or "0,1" for multiple GPUs Run Code Online (Sandbox Code Playgroud) 这样,无论您的计算机上有多少个 GPU,Hugging Face Trainer 都只能查看和使用您指定的 GPU。归档时间: 1年,3 月前 查看次数: 3442 次 最近记录: 1年,3 月前 相关...
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/...
我假设你正在使用QLORA + PEFT。确保在创建模型时使用device_map=“auto”,transformers trainer会处理剩...
related question:How to adapt LLaMA v2 model to less than 7B parameters? the full code of trainer HF:https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/trainer.py#L846 Bounty: Does one need to load the model to GPU before calling train when using...
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...
How to use Huggingface Trainer with multiple GPUs? Say I have the following model (from this script): from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig config = AutoConfig.from_pretrained( "gpt2", vocab_size=len(... ...
trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT") trainer.save_model(script_args.output_dir)# 或者 , 如果整个模型小于 50 GB (即 LFS 单文件的最大尺寸),你还可以使用 trainer.push_to_hub() 把模型推到 hub 上去。
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 be that both get used during training and it...
I explicitly loaded the models onto two separate GPUs, as they are too large to fit within a single A10 GPU. However, upon attempting to create aDPOTrainerinstance, I encountered the following error message: You can't train a model that has been loaded in 8-bit precision on a different ...
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: ...