learn2zinc

Learn2Zinc Models

Learn2Zinc is a a family of small language models fine-tuned with LoRA for translating natural-language optimization problems into executable MiniZinc code. All models were trained on the learn2zinc_augmented dataset using the Unsloth library.

Models

Shared Training Configuration

All models share the same LoRA and training setup:

Evaluation

Models were evaluated on the IndustryOR subset of the learn2zinc benchmark. Generated MiniZinc code was executed with the HiGHS solver (120 s timeout). All generations used temperature = 0 for reproducibility.

Metrics: Execution Success Rate (code compiles and runs) and Solution Correctness (objective matches expected value within 1e-6).

For full evaluation details, see the learn2zinc GitHub repo.

Datasets

All models were trained on datasets from the Learn2Zinc collection:

Framework

• Unsloth
• PEFT / LoRA
• TRL SFTTrainer

Citation

@misc{kadioglu2026modelingcopilotstexttomodeltranslation,
      title={Modeling Copilots for Text-to-Model Translation}, 
      author={Serdar Kadioglu and Karthik Uppuluri and Akash Singirikonda},
      year={2026},
      eprint={2604.12955},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.12955}, 
}