qqWen-7B-RL: Reasoning-Enhanced Q Programming Language Model

Model Overview

qqWen-7B-RL is a 7-billion parameter language model specifically designed for advanced reasoning and code generation in the Q programming language. Built upon the robust Qwen 2.5 architecture, this model has undergone a comprehensive three-stage training process: pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL) for the Q programming language.

Associated Technical Report: Report

🔤 About Q Programming Language

Q is a high-performance, vector-oriented programming language developed by Kx Systems, primarily used in:

• Financial Markets: High-frequency trading, risk management, and market data analysis
• Time-Series Analytics: Real-time processing of large-scale temporal data
• Data Science: Efficient manipulation of large datasets with concise syntax
• Quantitative Research: Mathematical modeling and statistical analysis

Key Q Language Features:

• Vector Operations: Built-in support for element-wise operations on arrays
• Functional Programming: First-class functions and powerful combinators
• Memory Efficiency: Optimized for handling large datasets in minimal memory
• Speed: Exceptional performance for numerical computations
• Concise Syntax: Expressive code that can accomplish complex tasks in few lines

📝 Citation

If you use this model in your research or applications, please cite our technical report.

@misc{hogan2025technicalreportfullstackfinetuning,
      title={Technical Report: Full-Stack Fine-Tuning for the Q Programming Language}, 
      author={Brendan R. Hogan and Will Brown and Adel Boyarsky and Anderson Schneider and Yuriy Nevmyvaka},
      year={2025},
      eprint={2508.06813},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.06813}, 
}