Instructions to use kenilp7/gemma-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kenilp7/gemma-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-27b-it") model = PeftModel.from_pretrained(base_model, "kenilp7/gemma-sql") - Notebooks
- Google Colab
- Kaggle
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base_model: google/gemma-3-27b-it
library_name: peft
---
## Gemma-3-27B Finetuned on Text-to-SQL
This model is a LoRA-finetuned version of `google/gemma-3-27b-it` for generating MySQL queries.
## Intended Use
For research on Text-to-SQL generation tasks.
## Training
Fine-tuned on BIRD Train Data consisiting of around 9,500 examples.
## How to use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("kenilp7/gemma-sql")
tokenizer = AutoTokenizer.from_pretrained("kenilp7/gemma-sql")
```
## Model Description
- **Language(s) (NLP):** SQL Generation
- **Finetuned from model [optional]:** google/gemma-3-27b-it
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