Instructions to use AaronTekle/SQLQwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AaronTekle/SQLQwen with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "AaronTekle/SQLQwen") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -136,10 +136,10 @@ All **50 generated SQL queries were successfully parsed by SQLGlot**, resulting
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## Model Limitations:
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* model may hallucinate tables or columns when
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* **0.5B parameter** base model prioritizes lightweight training and inference over maximum reasoning capacity
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* Training uses synthetic Text-to-SQL examples
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* Exact-match evaluation does not account for all semantically equivalent SQL formulations
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Additional Note: SQL execution accuracy against live databases was not measured in the reported benchmark.
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## Model Limitations:
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* model may hallucinate tables or columns when there is no context
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* **0.5B parameter** base model prioritizes lightweight training and inference over maximum reasoning capacity
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* Training uses synthetic Text-to-SQL examples (does not use real-world database schemas)
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* Exact-match evaluation does not account for all semantically equivalent SQL formulations
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Additional Note: SQL execution accuracy against live databases was not measured in the reported benchmark.
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