Human Mobility Inference Model
Overview
This project presents a fine-tuned model based on Deepseel-R1-Distill-Qwen-1.5B, designed for human mobility inference. The model is continuously evolving to enhance its accuracy and robustness in mobility pattern recognition and prediction.
Model Description
- Base Model: Deepseel-R1-Distill-Qwen-1.5B
- Objective: Fine-tuned for human mobility inference
- Status: The model is under active development with ongoing updates and improvements.
Features
- Fine-tuned on human mobility data to infer movement patterns and trends.
- Optimized for efficiency and scalability, leveraging a distilled version of Qwen-1.5B.
- Adaptable for real-world applications such as urban planning, transportation analytics, and smart city research.
Updates & Future Plans
- Enhancing model performance with more context-aware fine-tuning.
- Improving inference speed and memory efficiency.
- Expanding training datasets for better generalization across different mobility environments.
Usage
The model is currently in development, and detailed usage instructions will be provided in future updates.
Demo input:
"<|im_start|>system\nPlease reason step by step, and put your final answer within \boxed{}.<|im_end|>\n<|im_start|>user\nComplete the visitor's travel trajectory in Japan based on the following description. A visitor from Australia traveled in Japan. We have two clues about this visitor: (1) This visitor was visiting Uruma Shi at 2023-07-15 03:12; (2) This visitor spent about 4 days in Japan.\nTask: Complete the travel trajectory of this visitor after detailed reasoning process. The reasoning processes consists of multiple steps which are helpful for this task. They are enclosed within two kinds of tags </think about travel rationality>; </think about format correctness>. Afer the reasoning process, put the final answer within \boxed{{}} and the answer should follow the format: Visited {location1} at {time1};...; Visited {location2} at {time2}. End of journey.<|im_end|>\n<|im_start|>assistant"
Contribution
If you're interested in contributing or providing feedback, feel free to reach out or submit issues. Your input is valuable in refining the model for better performance.
License
The licensing details will be provided upon the model's official release.
Stay tuned for updates as the model continues to evolve!
