This model is designed for on-device deployment to extract structured MIST 9-liner information from medical dictation text. The MIST 9-liner format includes:

Mechanism of injury
Injury pattern
Signs and symptoms
Treatment given
Vital signs (5 fields)

Usage with llama.cpp

Base Model: google/gemma-3-270m-it
Training Method: Full parameter fine-tuning
Languages: English + German (bilingual)
Task: Extract structured MIST 9-liner data from medical dictations
Format: GGUF F16 (for use with llama.cpp)

bash

# Download the GGUF file
huggingface-cli download mhylle/gemma3-270m-mist-9liner-bilingual-gguf gemma3-270m-mist-bilingual-f16.gguf

# Run inference
./llama-cli -m gemma3-270m-mist-bilingual-f16.gguf -p "<prompt>"

Limitations

Optimized for MIST 9-liner format specifically
Best performance with clear, structured dictation input
Trained on synthetic/curated medical dictation data

Model Card Contact

For questions or issues, please open a GitHub issue on the project repository.