Cleo: trying to fit full analyst behavior in a 2B model [P]

Reddit r/MachineLearning Models
text-to-sql open-source finetuning small-language-model nlp database

Summary

Cleo is a finetuned version of Qwen3.5-2B-Base designed for text-to-SQL tasks, using a unified harness for training and inference that supports live execution evidence and safety checks. All code, model, and datasets are open-source.

Hello all! Half of all industrial "chatbots" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models if you trained, evaluated, and ran inference in the exact same structured harness, leading to Cleo: a Qwen3.5-2B-Base finetune. Currently, some features of cleo that are only possible/useful in a unified hardel are: * Training on the exact same gather, repair, and answer contract it uses at inference time * Searching over candidate queries with live execution evidence, not just model likelihood * Co-designing the model contract, SQL safety layer, dialect handling, timeouts, and clarification behavior as one system Everything is completely open-source, including the harness, model, and datasets. GitHub: [https://github.com/Dreeseaw/cleo](https://github.com/Dreeseaw/cleo) Hugging Face model: [https://huggingface.co/dreeseaw/cleo](https://huggingface.co/dreeseaw/cleo) PS: If you're also resource-constrained and trying to do RL like me, I would highly recommend experimenting with ECHO: [https://arxiv.org/abs/2605.24517](https://arxiv.org/abs/2605.24517)
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