InfiR：打造高效的小型语言模型与多模态小型语言模型在推理中的应用

InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning

February 17, 2025

作者: Congkai Xie, Shuo Cai, Wenjun Wang, Pengxiang Li, Zhijie Sang, Kejing Yang, Yiming Zhang, Zhen Li, Guanghao Zhu, Zeyu Liu, Yang Yu, Yuhang Liu, Su Lu, Baoyi He, Qi Zhou, Xiaotian Han, Jianbo Yuan, Shengyu Zhang, Fei Wu, Hongxia Yang

cs.AI

摘要

大型语言模型（LLMs）与多模态大型语言模型（MLLMs）在推理能力上取得了显著进展，但仍面临高计算需求与隐私保护等挑战。本文致力于开发高效的小型语言模型（SLMs）及多模态小型语言模型（MSLMs），在保持竞争力推理能力的同时，提出了一种创新的训练流程，该流程不仅增强了模型的推理能力，还便于在边缘设备上部署，实现了性能的顶尖水平，同时大幅降低了开发成本。\InfR~旨在通过缩小模型规模，提升AI系统的推理能力，降低应用门槛，并有效应对隐私问题。相关资源已发布于https://github.com/Reallm-Labs/InfiR。

English

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as high computational demands and privacy concerns. This paper focuses on developing efficient Small Language Models (SLMs) and Multimodal Small Language Models (MSLMs) that retain competitive reasoning abilities. We introduce a novel training pipeline that enhances reasoning capabilities and facilitates deployment on edge devices, achieving state-of-the-art performance while minimizing development costs. \InfR~ aims to advance AI systems by improving reasoning, reducing adoption barriers, and addressing privacy concerns through smaller model sizes. Resources are available at https://github. com/Reallm-Labs/InfiR.