LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS

828 篇文章

已下架不支持订阅

LongLoRA是一种新的微调方法,能够在不显著增加计算成本的情况下,有效扩展预训练大型语言模型的上下文大小。通过引入转移短注意力和支持长上下文的LoRA,LongLoRA在保持模型性能的同时减少了GPU内存和训练时间。实验表明,该方法成功应用于LLaMA2模型,实现了从几千到数十万的上下文扩展。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

本文是LLM系列文章,针对《LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS》的翻译。

Longlora:长上下文大型语言模型的高效微调

摘要

我们提出了LongLoRA,一种有效的微调方法,以有限的计算成本扩展预训练的大型语言模型(llm)的上下文大小。通常,训练具有长上下文大小的llm在计算上是昂贵的,需要大量的训练时间和GPU资源。例如,在上下文长度为8192的情况下进行训练,自注意力层的计算成本是在上下文长度为2048的情况下的16倍。本文从两个方面加快LLM的语境扩展。一方面,虽然在推理过程中需要密集的全局关注,但通过稀疏的局部关注可以有效地对模型进行微调。所提出的转移短注意力(S2 -Attn)有效地支持上下文扩展,从而节省大量计算,性能与使用普通注意力进行微调相似。特别的是,它可以在训练中仅用两行代码实现,而在推理中是可选的。另一方面,我们重新审视了上下文扩展的参数有效微调机制。值得注意的是,我们发现LoRA在可训练的嵌入和规范化的前提下可以很好地进行上下文扩展。LongLoRA在LLaMA2模型从7B/13B到70B的各种任务上证明了强有力的实证结果。LongLoRA在单个8× A100机器上采用LLaMA2 7B从4k上下文到100k,或LLaMA2 70B到32k。LongLoRA扩展了模型的上下文,同时保留了它们原来的架构,并且与大多数现有技术兼容,

已下架不支持订阅

### Adapter-Transformers Library Information #### Overview of Adapter-Tuning with Pre-Trained Models Adapter-tuning leverages pre-trained language models from model zoos like Hugging Face's Transformers library as a foundation. These libraries offer various utilities and functionalities that facilitate efficient adaptation of large-scale models without requiring extensive retraining or modification of the original architecture[^1]. #### Installation Process for Required Libraries To begin working with adapters, installing necessary packages is essential. Specifically, the `transformers` library must be installed since it supplies access to pre-trained models including BERT along with optimizers required for fine-tuning operations. ```bash !pip install transformers ``` This command installs all dependencies needed to work with adapter modules within Python environments[^2]. #### Parameter-Efficient Fine-Tuning Techniques Research on parameter-efficient methods has led to developments such as LLm-Adapters, an innovative approach introduced by researchers at multiple institutions. This technique allows developers to apply minimal changes—specifically through low-rank updates—to existing weights while maintaining performance gains during transfer learning tasks[^3]. #### Implementation Details Using Low-Rank Adaptation (LoRA) One notable implementation detail involves breaking down weight updates into two smaller matrices \( A \) and \( B \), where: - Matrix \( A \): Dimensions are \( d_{\text{model}} \times r \). - Matrix \( B \): Dimensions are \( r \times d_{\text{model}} \). Here, \( r \) represents rank size much lower than \( d_{\text{model}} \), leading to significant reductions in computational overhead when applying these adjustments compared to full matrix modifications[^4]. --related questions-- 1. What specific advantages does using adapter-based tuning have over traditional fine-tuning approaches? 2. How do different ranks affect the efficiency and effectiveness of LoRA implementations? 3. Can you explain how one might integrate custom adapters into their own projects built upon Transformer architectures? 4. Are there any particular challenges associated with deploying adapter-modified models in production settings?
评论 1
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

UnknownBody

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值