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anton-hugging
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Model Details
Model Provider
anton-hugging
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
TimeOmni-1 is a generalized reasoning model for time series. Pretrained LLMs often lack temporal priors because they are rarely exposed to time series during pretraining. To address this, we use a two-stage training pipeline: (1) supervised fine-tuning (SFT) to inject temporal priors and anchor the model in a temporal knowledge space, and (2) reinforcement learning (RL) with task-grounded rewards (see Reward Evaluation in the figure above) to improve robustness and reasoning quality.
Table 1. Overall Benchmark Comparison
Table 2. Model Size Scaling Comparison
This repository hosts the model weights for TimeOmni-1. For installation, usage instructions, and further documentation, please visit our GitHub repository.
TimeOmni-1 is licensed under the Apache 2.0 license.
bibtex
@inproceedings{guan2026timeomni,title={TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models},author={Tong Guan and Zijie Meng and Dianqi Li and Shiyu Wang and Chao-Han Huck Yang and Qingsong Wen and Zuozhu Liu and Sabato Marco Siniscalchi and Ming Jin and Shirui Pan},booktitle={The Fourteenth International Conference on Learning Representations},year={2026},url={https://openreview.net/forum?id=kOIclg7muL}}