Instructions to use VoErik/cypher-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VoErik/cypher-gemma with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="VoErik/cypher-gemma")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VoErik/cypher-gemma")
model = AutoModelForCausalLM.from_pretrained("VoErik/cypher-gemma", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VoErik/cypher-gemma with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VoErik/cypher-gemma"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VoErik/cypher-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VoErik/cypher-gemma

SGLang

How to use VoErik/cypher-gemma with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "VoErik/cypher-gemma" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VoErik/cypher-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "VoErik/cypher-gemma" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VoErik/cypher-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VoErik/cypher-gemma with Docker Model Runner:
```
docker model run hf.co/VoErik/cypher-gemma
```

cypher-gemma / README.md

VoErik

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metadata

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. Its purpose is turning natural language queries into CypherQueryLanguage.

It has been trained using TRL.

Quick start

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:

@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}}
}