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vishnurchityala/
sql-gemma3

Name: sql-gemma3
Availability: InStock

gretelai/synthetic_text_to_sql

unsloth/gemma-3-1b-it

finetune

gemma

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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 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
Dataset: Gretel AI synthetic_text_to_sql