Instructions to use modelcitizens/GEMMACITIZEN-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modelcitizens/GEMMACITIZEN-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="modelcitizens/GEMMACITIZEN-12B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modelcitizens/GEMMACITIZEN-12B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use modelcitizens/GEMMACITIZEN-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "modelcitizens/GEMMACITIZEN-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modelcitizens/GEMMACITIZEN-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/modelcitizens/GEMMACITIZEN-12B
- SGLang
How to use modelcitizens/GEMMACITIZEN-12B 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 "modelcitizens/GEMMACITIZEN-12B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modelcitizens/GEMMACITIZEN-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "modelcitizens/GEMMACITIZEN-12B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modelcitizens/GEMMACITIZEN-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use modelcitizens/GEMMACITIZEN-12B with Docker Model Runner:
docker model run hf.co/modelcitizens/GEMMACITIZEN-12B
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file chat_template.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Model Summary
GEMMACITIZEN-12B is a toxicity detection model finetuned from Gemma-3-12B-IT on ingroup annotations of ModelCitizens data. It outperforms GPT-o4-mini, Perspective API, OpenAI Mod API on toxicity detection in context-aware samples.
Repository: asuvarna31/modelcitizens
Usage
PROMPT = """
You will be provided with a statement and some context. Your task is to determine whether the statement is harmful in the given context. If no context is provided, it will be marked as "NA." In such cases, make your decision based solely on the statement. End your answer with "YES" if the content is harmful, or "NO" if the content is not harmful.
CONTEXT: {context}
STATEMENT: {statement}
REPLY: {reply}
"""
Citation
@misc{suvarna2025modelcitizensrepresentingcommunityvoicesonline,
title={ModelCitizens:Representing Community Voices in Online Safety},
author={Ashima Suvarna and Christina Chance and Karolina Naranjo and Hamid Palangi and Sophie Hao and Thomas Hartvigsen and Saadia Gabriel},
year={2025},
eprint={2507.05455},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.05455},
}
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