InfiR : Crafting Effective Small Language Models and Multimodal Small
  Language Models in Reasoning
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.
67b6f629d9da6999328e38f5
Congkai Xie
admin_assigned
2025-02-20T17:12:49.025Z
6719f1ad725123d503b5ef3c
/avatars/08e1be1f4afa1b6e1501a15cdb786a47.svg
Congkai Xie
user
congkai
67b6f629d9da6999328e38f6
Shuo Cai
67b6f629d9da6999328e38f7
Wenjun Wang
67b6f629d9da6999328e38f8
Pengxiang Li
admin_assigned
2025-02-20T17:13:23.938Z
65d6bd8ebb862d1b367580fe
https://cdn-avatars.huggingface.co/v1/production/uploads/65d6bd8ebb862d1b367580fe/Pf5o-Oxi2IZxJPa-ZgEtV.jpeg
Pengxiang Li
user
PengxiangLi
67b6f629d9da6999328e38f9
Zhijie Sang
admin_assigned
2025-02-20T17:13:31.244Z
6719f2569665701fd6a6a43d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/8gS7Tzr6rIZMl-8sS1x_r.png
Zhijie Sang
user
SANGZHIJIE
67b6f629d9da6999328e38fa
Kejing Yang
67b6f629d9da6999328e38fb
Yiming Zhang
admin_assigned
2025-02-20T17:13:47.213Z
626309292597e1cb7c76ab6f
/avatars/594b9ccfc5f3aa61613b4bc3158fed4f.svg
Yiming Zhang
user
yimingzhang
67b6f629d9da6999328e38fc
Zhen Li
67b6f629d9da6999328e38fd
Guanghao Zhu
admin_assigned
2025-02-20T17:13:55.619Z
670f8a7d4e3a710738fd13cc
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2ITxPXZCAzdY-LhbNA6NI.png
Guanghao
user
GuanghaoZhu
67b6f629d9da6999328e38fe
Zeyu Liu
67b6f629d9da6999328e38ff
Yang Yu
67b6f629d9da6999328e3900
Yuhang Liu
claimed_verified
2025-04-22T10:16:24.806Z
62722849517c0ca41f7cd13d
/avatars/bb1f8f2f2665944930cb5a7ce19c47d4.svg
Yuhang Liu
user
SiriusL
67b6f629d9da6999328e3901
Su Lu
67b6f629d9da6999328e3902
Baoyi He
67b6f629d9da6999328e3903
Qi Zhou
67b6f629d9da6999328e3904
Xiaotian Han
admin_assigned
2025-02-20T17:32:33.779Z
650dde4ce14eeb01d42b37a1
https://cdn-avatars.huggingface.co/v1/production/uploads/650dde4ce14eeb01d42b37a1/n5Yv24uofZ2XJjXdYCrKd.png
Xiaotian Han
user
xiaotianhan
67b6f629d9da6999328e3905
Jianbo Yuan
67b6f629d9da6999328e3906
Shengyu Zhang
67b6f629d9da6999328e3907
Fei Wu
67b6f629d9da6999328e3908
Hongxia Yang
InfiR : Crafting Effective Small Language Models and Multimodal Small
  Language Models in Reasoning
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.
Researchers developed Small Language Models and Multimodal Small Language Models to enhance reasoning capabilities while reducing computational demands and privacy concerns.
Large Language Models Multimodal Large Language Models Small Language Models Multimodal Small Language Models training pipeline reasoning capabilities edge devices state-of-the-art performance privacy concerns InfR