qqWen-32B-RL: Reasoning-Enhanced Q Programming Language Model
Model Overview
qqWen-32B-RL is a 32-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. qqWen-32B-RL is a reasoning model.
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},
}