---
language:
- en
license: gemma
base_model: unsloth/gemma-3-1b-it
tags:
- text-to-sql
- finetuning
datasets:
- gretelai/synthetic_text_to_sql
pipeline_tag: text-generation
---

# SQL-Gemma3

`SQL-Gemma3` is a fine-tuned version of `Gemma 3 1B Instruct` for text-to-SQL generation. It was trained on a balanced sampled subset of the [Gretel synthetic_text_to_sql dataset](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) to improve SQL generation from table schema and natural language questions.

## Model Details

- Base model: `unsloth/gemma-3-1b-it`
- Task: Natural language to SQL
- Training data: balanced sampled subset of `gretelai/synthetic_text_to_sql`
- Reported training loss: `0.201`
- Reported test loss: `0.21`

## Intended Use

This model is intended for:

- Generating SQL queries from schema-aware prompts
- Learning and experimentation with text-to-SQL workflows
- Prototyping NL-to-SQL assistants

It is not guaranteed to produce correct, executable, or secure SQL for every prompt. Review generated queries before using them in production systems.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "vishnurchityala/sql-gemma3"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": (
            "CREATE TABLE employees(id INT, name TEXT, salary INT);\n\n"
            "Find the average salary of all employees."
        ),
    }
]

inputs = tokenizer(
    tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    ),
    return_tensors="pt",
)

outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

- Performance is summarized here using loss only, not execution accuracy
- Output quality depends heavily on schema clarity and prompt format
- The model may generate dialect-specific or invalid SQL in some cases

## Acknowledgements

- Base model: [Gemma 3](https://huggingface.co/google)
- Dataset: [Gretel AI synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)