---
library_name: transformers
license: apache-2.0
base_model:
- Qwen/Qwen2.5-7B-Instruct
---

# qqWen-7B-SFT: Reasoning-Enhanced Q Programming Language Model

## Model Overview

**qqWen-7B-SFT** 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 two-stage training process: pretraining, and supervised fine-tuning (SFT) for the Q programming language.

**Associated Technical Report**: [Report](https://arxiv.org/abs/2508.06813)


## 🔤 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}, 
}
```

