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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LoRA fine-tuning of **Qwen2.5-Coder-0.5B-Instruct** for **Text-to-SQL generation** using the **Gretel synthetic_text_to_sql** dataset.
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Text-to-SQL systems translate natural-language questions into
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LoRA fine-tuning of **Qwen2.5-Coder-0.5B-Instruct** for **Text-to-SQL generation** using the **Gretel synthetic_text_to_sql** dataset.
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Text-to-SQL systems translate natural-language questions into SQL using a provided database schema or SQL context. SQLQwen is designed to specialize a lightweight code-focused language model for this task while keeping the fine-tuning process parameter-efficient and practical on day-to-day (normal) hardware.
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