Keepace AI specializes large language model for Chinese exercise guidance
Keepace.ai: A Technical Report on a Large Language Model for Sports and Health
Computation and Language
Summary
Providing safe and personalized exercise advice using large AI models is challenging because there is little good training data and no clear way to judge accuracy in sports and health topics. The authors created Keepace.ai by continuing to train a large model specifically for Chinese language exercise plans, sports knowledge questions, and personal data analysis. Internal tests by experts showed Keepace.ai gives safer, more accurate, and more complete workout advice than general models. This report explains how Keepace.ai was built, tested, and evaluated for safety and scientific reliability.
What this means in practice
- •For health app developers: Generate personalized and scientifically sound workout plans for Chinese-speaking users integrating personal sports data.$Commercial implications: Enables development of advanced fitness apps offering tailored exercise guidance based on a specialized AI model for Chinese speakers.
- •For fitness equipment manufacturers: Enhance smart device interfaces with AI-powered explanations and safety warnings for exercise routines in Chinese.
Authors
Keepace. ai Team, Bowen Wu, Heng Wang, Jufeng Mi, Kang Liu, Kun Li, Ling Man, Long He, Suli Fan, Xiaosen Jiang, Yi Wang, Ying He, Yu Tang, Yunhan Li, Zifang Liu
Abstract
Large language models hold promise for delivering safe, scientifically grounded, and personalized exercise guidance at scale, yet the sports-and-health domain still lacks curated training corpora and rigorous evaluation benchmarks. Keepace.ai is a domain-specialized LLM built on Qwen3.5-27B through post-training, currently optimized for Chinese-language exercise guidance across three tasks: workout plan generation, sports-and-health knowledge QA, and personal sports data analysis. In internal rubric-based evaluations designed with domain experts, Keepace.ai outperforms its base model and larger general-purpose baselines, with the most pronounced improvements in exercise safety, boundary compliance, structured output completeness, and scientific accuracy. This report summarizes the current design, data pipeline, safety-first evaluation framework, and evaluation results of Keepace.ai.