Instructions to use mhylle/ministral-3b-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/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
llama cli -hf mhylle/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
llama cli -hf mhylle/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf mhylle/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Use Docker

docker model run hf.co/mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

LM Studio
Jan

vLLM

How to use mhylle/ministral-3b-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/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Ollama
How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with Ollama:
```
ollama run hf.co/mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16
```

Unsloth Studio

How to use mhylle/ministral-3b-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/ministral-3b-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/ministral-3b-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/ministral-3b-mist-9liner-bilingual-gguf to start chatting

How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with Docker Model Runner:
```
docker model run hf.co/mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16
```

Lemonade

How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Run and chat with the model

lemonade run user.ministral-3b-mist-9liner-bilingual-gguf-F16

List all available models

lemonade list

Hermes Agent

How to use mhylle/ministral-3b-mist-9liner-bilingual-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mhylle/ministral-3b-mist-9liner-bilingual-gguf:F16

Run Hermes

hermes

Atomic Chat

Ministral 3B - MIST 9-Liner Bilingual (GGUF)

This is a GGUF format export of a fine-tuned Ministral 3B model for medical dictation extraction.

Model Description

Base Model: mistralai/Ministral-3-3B-Instruct-2512-BF16
Training Method: LoRA (merged with base model)
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	100%
Schema Compliant	98%
Avg Field Accuracy	69.5%

Training Details

Epochs: 2
Batch Size: 2 (effective 16 with gradient accumulation)
Learning Rate: 1.5e-4
Max Sequence Length: 4096
LoRA Rank: 16
LoRA Alpha: 32

Model Comparison

Model	Size	JSON Valid	Schema	Field Accuracy
Ministral 3B	6.4GB	100%	98%	69.5%
Gemma3 270M	536MB	99%	96%	68.7%

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/ministral-3b-mist-9liner-bilingual-gguf ministral-3b-mist-bilingual-f16.gguf

# Run inference
./llama-cli -m ministral-3b-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
Larger model size (6.4GB) may require more memory than smaller alternatives

Model Card Contact

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

Downloads last month: 18

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

16-bit

Model tree for mhylle/ministral-3b-mist-9liner-bilingual-gguf

Base model

mistralai/Ministral-3-3B-Base-2512

Finetuned

mistralai/Ministral-3-3B-Instruct-2512-BF16

Quantized

(17)

this model