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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}}
}
``` |