打造轻量推理模型，让小模型也能高效处理复杂任务。
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
- 设计新训练流程，提升小模型的逻辑推理能力
- 在保持低资源消耗下达到顶尖性能表现
- 适合边缘设备部署，兼顾隐私与计算效率
大型语言模型（LLMs）和多模态大型语言模型（MLLMs）在推理能力上取得了显著进展，但仍面临高计算开销和隐私问题。本文致力于开发具备竞争力推理能力的小型语言模型（SLMs）和多模态小型语言模型（MSLMs）。提出一种新型训练流程，有效增强模型推理能力，并支持在边缘设备上部署，实现先进性能的同时大幅降低开发成本。InfiR旨在通过更小的模型规模推动人工智能系统发展，提升推理效率，降低应用门槛，缓解隐私风险。相关资源已开源：https://github.com/Reallm-Labs/InfiR。
原文摘要 · Abstract (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.
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