---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: cypher-gemma
tags:
- generated_from_trainer
- trl
- sft
- cypher
licence: license
datasets:
- neo4j/text2cypher-2025v1
pipeline_tag: text-generation
language:
- en
---

# Model Card

This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). Its purpose is turning natural language queries into CypherQueryLanguage.

It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline
from schemas import MOVIE_SCHEMA # you need to define this yourself!

query = "Which actors played a role in the movie Titanic?"

pipe = pipeline("text-generation", model="VoErik/cypher-gemma", device="cuda")
output = pipe([{"role": "user", "content": f"Question: {question} \n Schema: {MOVIE_SCHEMA}"}], max_new_tokens=256, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

This model was trained with SFT on the text2cypher-2025v1 dataset from Neo4j.
It was trained for roughly 3500 steps.

### Framework versions

- TRL: 0.23.1
- Transformers: 4.57.0
- Pytorch: 2.8.0
- Datasets: 4.2.0
- Tokenizers: 0.22.1

## Citations

Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```