Instructions to use bryanchrist/gemma3_12b_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bryanchrist/gemma3_12b_sft with Transformers:

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

pipe = pipeline("image-text-to-text", model="bryanchrist/gemma3_12b_sft")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bryanchrist/gemma3_12b_sft")
model = AutoModelForMultimodalLM.from_pretrained("bryanchrist/gemma3_12b_sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bryanchrist/gemma3_12b_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bryanchrist/gemma3_12b_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bryanchrist/gemma3_12b_sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bryanchrist/gemma3_12b_sft

SGLang

How to use bryanchrist/gemma3_12b_sft 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 "bryanchrist/gemma3_12b_sft" \
    --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": "bryanchrist/gemma3_12b_sft",
		"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 "bryanchrist/gemma3_12b_sft" \
        --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": "bryanchrist/gemma3_12b_sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bryanchrist/gemma3_12b_sft with Docker Model Runner:
```
docker model run hf.co/bryanchrist/gemma3_12b_sft
```

Gemma 3 12B IT SFT

Gemma 3 12B SFT model trained on STEM dataset as introduced in EDUMATH: Generating Standards-aligned Educational Math Word Problems. See our project repo for usage and our paper for training details/metrics.

Citation

@misc{christ2026edumathgeneratingstandardsalignededucational,
      title={EDUMATH: Generating Standards-aligned Educational Math Word Problems}, 
      author={Bryan R. Christ and Penelope Molitz and Beau LeBlond and Zachary Gottesman and Jonathan Kropko and Thomas Hartvigsen},
      year={2026},
      eprint={2510.06965},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.06965}, 
}