Instructions to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
llama cli -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
llama cli -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Use Docker

docker model run hf.co/mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

LM Studio
Jan

vLLM

How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mhylle/gemma3-270m-mist-9liner-bilingual-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mhylle/gemma3-270m-mist-9liner-bilingual-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Ollama
How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with Ollama:
```
ollama run hf.co/mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
```

Unsloth Studio

How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mhylle/gemma3-270m-mist-9liner-bilingual-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mhylle/gemma3-270m-mist-9liner-bilingual-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mhylle/gemma3-270m-mist-9liner-bilingual-gguf to start chatting

Docker Model Runner
How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with Docker Model Runner:
```
docker model run hf.co/mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16
```

Lemonade

How to use mhylle/gemma3-270m-mist-9liner-bilingual-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mhylle/gemma3-270m-mist-9liner-bilingual-gguf:F16

Run and chat with the model

lemonade run user.gemma3-270m-mist-9liner-bilingual-gguf-F16

List all available models

lemonade list

Atomic Chat

Gemma3 270M - MIST 9-Liner Bilingual (GGUF)

This is a GGUF format export of a fine-tuned Gemma3 270M model for medical dictation extraction.

Model Description

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)

Performance Metrics

Metric	Score
JSON Valid	99%
Schema Compliant	96%
Avg Field Accuracy	68.7%

Training Details

Epochs: 3
Batch Size: 8
Learning Rate: 2e-5
Max Sequence Length: 1024
Training Time: ~28 minutes

Intended Use

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

# 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.

Downloads last month: 9

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

16-bit

Model tree for mhylle/gemma3-270m-mist-9liner-bilingual-gguf

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Quantized

(341)

this model