PEFT大模型高效 随着深度学习技术的不断发展,大型预训练模型(Large Pre-trained Models)在自然语言处理、计算机视觉等领域取得了显著的成果。然而,这些模型的训练和优化需要大量的计算资源和数据,对很多研究机构和企业来说是一个不小的挑战。参数高效微调(Parameter-Efficient Fine-Tuning, PEFT)作为一种解决方案,正逐渐...
[5] Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters lightning.ai/pages/comm [6] Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning 本文使用 Zhihu On VSCode 创作并发布 ...
I located the bug. It works when loading models with the function AutoModelForCausalLM. I replaced the loading function and the bug disappear. Also, I tried to replace the function to GPT2Model or LlamaModel in the code you gave above, the bug will be reproduced ...
comparision_data.jsonranked responses from three models, including GPT-4, GPT-3.5 and OPT-IML b...
Clarify how AutoModelforCausalLM actually loads the adapter (I assume it keeps the adapter weights unmerged with the base model, hence slower inference?). [btw, it's useful that this can be used to load models because you can put in a path that can be either an adapter model OR a ful...
LoRA是Low-Rank Adaptation of Large Language Models的缩写,于2023年初推出,它已成为最常用的PEFT方法,帮助公司和研究人员降低微调成本。使用重新参数化,该技术通过执行低秩近似来缩小可训练参数集的大小。 例如,如果我们有一个100,000x100,000的权重矩阵,那么为了进行全面微调,我们需要更新10,000,000,000个参数。使...
[4]BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models: https://arxiv.org/pdf/2106.10199.pdf [5]GPT Understands, Too: https://arxiv.org/pdf/2103.10385.pdf [6]TOWARDS A UNIFIED VIEW OF PARAMETER-EFFICIENT TRANSFER LEARNING: ...
组成类标签的标记。提示作为一系列标记添加到输入中。Prompt tuning背后的核心思想是提示标记拥有自己的参数...
今天要介绍的,是其中之一,也是最近比较热门的LORA (LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS)。 1. LORA原理介绍 LORA的论文写的比较难读懂,但是其原理其实并不复杂。简单理解一下,就是在模型的Linear层,的旁边,增加一个“旁支”,这个“旁支”的作用,就是代替原有的参数矩阵W进行训练。 ...
https://arxiv.org/abs/2110.07602 LoRA: Low-Rank Adaptation of Large Language Models https://arxiv.org/abs/2106.09685 ReFT: Representation Finetuning for Language Models https://arxiv.org/abs/2404.03592