论文:How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition Blind Daisy 没有Xbox 的全平台玩家 15 人赞同了该文章 目录 收起 研究目的 1. 实验 1.1 实验准备 1.2 实验细节 1.2.1 实验 1:单项能力表现 VS 数据量 1.2.2 实验 2:单项能力表现 VS 混合数据量...
Yi: 10K, 有这么一句话: "Our finetuning dataset consists of less than 10K multi-turn instruction response dialog pairs, with each and every one of the entry constructed and polished over multiple iterations and from user feedback. We take this approach because in our preliminary experiments, we...
Continue PreTraining(增量预训练):一般垂直大模型是基于通用大模型进行二次的开发。为了给模型注入领域知识,就需要用领域内的语料进行继续的预训练。 SFT( Supervised Finetuning, 有监督微调): 通过SFT可以激发大模型理解领域内的各种问题并进行回答的能力(在有召回知识的基础上) RLHF(奖励建模、强化学习训练): 通...
PEFT reduces the memory footprint for supervised fine-tuning of Text2SQL at the parameter level, but the length of the context input to the model is also one of the important factors affecting the memory usage during fine-tuning. it is important to note that GPU memory usage during fine-tu...
1. Distantly Supervised Fine-tuning on Relation Extraction 关于selective attention,可以参考刘知远教授2016年的论文(Neural Relation Extraction with Selective Attention over Instances)。这里只给出公式。 那么,如果把一个bag的句子集合用S表示,那么就可以这么说,给定一个S,和训练的参数,最大化这个实体对的关系labe...
Pre-training and Fine-tuning (P&F) Joint Learning (JL) Unsupervised Representation Learning (URL)2.4.1 预训练和微调 Pre-training and Fine-tuning (P&F)框架如下:首先它是一个两阶段框架,预训练:其中的 Encoder fθ(⋅)fθ(⋅) 是通过代理任务预训练得到的,然后预训练得到的参数 θinit θinit 将...
论文:https://openreview.net/pdf?id=cu7IUiOhujH 资料:https://zhuanlan.zhihu.com/p/278127741 ABSTRACT we propose a supervised contrastive learning (SCL) objective for the fine-tuning stage INTRODUCTION cross-entropy loss has several shortcomings. ...
《QLoRA: Efficient Finetuning of Quantized LLMs》(2023) GitHub: github.com/ChrisHayduk/QLoRA-for-MLM《Multichannel Acoustic Echo Cancellation with Beamforming in Dynamic Environments》(2023) GitHub: github.com/IsraelCohenLab/MultichannelAcousticEchoCancellation...
同时,为了进一步验证GRN的效果,论文在表2中展示了使用ConvNeXt-Base模型进行的一系列实验,来研究如何利用全局响应归一化(GRN)技术来增加特征的多样性,从而提高模型的性能。 Table 2. GRN ablations with ConvNeXt-Base. We report fine-tuning accuracy on ImageNet-1K. Our final proposal is marked in gray ....
Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning, when the model is weak, CNN is fine-tuned on a small amount of reliable examples that locate near to cluster centroids in the feature space. As the model...