Instructions to use OminousDude/voicebridge-gemma4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OminousDude/voicebridge-gemma4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OminousDude/voicebridge-gemma4",
	filename="voicebridge-finetuned-q4km.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OminousDude/voicebridge-gemma4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OminousDude/voicebridge-gemma4
# Run inference directly in the terminal:
llama-cli -hf OminousDude/voicebridge-gemma4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OminousDude/voicebridge-gemma4
# Run inference directly in the terminal:
llama-cli -hf OminousDude/voicebridge-gemma4

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 OminousDude/voicebridge-gemma4
# Run inference directly in the terminal:
./llama-cli -hf OminousDude/voicebridge-gemma4

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 OminousDude/voicebridge-gemma4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OminousDude/voicebridge-gemma4

Use Docker

docker model run hf.co/OminousDude/voicebridge-gemma4

LM Studio
Jan

vLLM

How to use OminousDude/voicebridge-gemma4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OminousDude/voicebridge-gemma4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OminousDude/voicebridge-gemma4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OminousDude/voicebridge-gemma4

Ollama
How to use OminousDude/voicebridge-gemma4 with Ollama:
```
ollama run hf.co/OminousDude/voicebridge-gemma4
```

Unsloth Studio new

How to use OminousDude/voicebridge-gemma4 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 OminousDude/voicebridge-gemma4 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 OminousDude/voicebridge-gemma4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OminousDude/voicebridge-gemma4 to start chatting

Pi new

How to use OminousDude/voicebridge-gemma4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OminousDude/voicebridge-gemma4

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": "OminousDude/voicebridge-gemma4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OminousDude/voicebridge-gemma4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OminousDude/voicebridge-gemma4

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 OminousDude/voicebridge-gemma4

Run Hermes

hermes

Docker Model Runner
How to use OminousDude/voicebridge-gemma4 with Docker Model Runner:
```
docker model run hf.co/OminousDude/voicebridge-gemma4
```

Lemonade

How to use OminousDude/voicebridge-gemma4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OminousDude/voicebridge-gemma4

Run and chat with the model

lemonade run user.voicebridge-gemma4-{{QUANT_TAG}}

List all available models

lemonade list

VoiceBridge — Offline Multilingual Clinical Triage AI

Fine-tuned Gemma 4 E4B for SATS 2023 / WHO ETAT-aligned clinical triage in resource-limited settings. No internet required. Runs fully offline via llama.cpp on hardware as small as a Raspberry Pi 5 8GB.

Submitted to the Gemma 4 Good Hackathon 2026 — Digital Equity Track.

Live demo: https://voicebridge.octo.net/ui/

Performance

Benchmarked on 100 real SATS-aligned clinical cases across 5 languages. Nothing simulated.

Metric	Base Gemma 4 E4B	VoiceBridge Fine-tuned
Exact match accuracy	85%	96%
Safe escalation rate	89%	100%
Unsafe under-triage cases	11	0
SATS validator agreement	76%	85%

Base Gemma 4 E4B uses the same custom prompt as my tuned model, which boosts its scores.

Per-level accuracy (fine-tuned): RED 100%, ORANGE 85%, YELLOW 95%, GREEN 100%, BLUE 100%

Per-language accuracy (fine-tuned): Bengali 100%, English 100%, Tagalog 100%, Hausa 95%, Swahili 85.7%

Zero unsafe under-triage cases. Every miss is a safe over-triage.

Languages

Benchmark languages: English, Swahili, Hausa, Bengali, Tagalog. Supports any language Gemma 4 speaks — Arabic, Turkish, Italian, Amharic, Hindi, French, and 140+ more without retraining. Multilingual capability comes from Gemma 4's native pretraining. Fine-tuning teaches clinical triage structure on top of that.

Intended Use

Community health workers, field nurses, and first responders in resource-limited settings — rural clinics, conflict zones, disaster response camps — anywhere without reliable internet access.

Thinking mode was disabled based on feedback from Medic's engineering team: speed and battery life matter more than reasoning depth on edge devices.

Usage

llama-cli -m voicebridge-finetuned-q4km.gguf \
  -p "<start_of_turn>system
You are a clinical triage assistant (SATS 2023 / WHO ETAT). Language: English.
Output ONLY a JSON object: triage_level (red/orange/yellow/green/blue),
primary_complaint, red_flag_indicators, recommended_action, confidence_score.<end_of_turn>
<start_of_turn>user
Patient not breathing, no pulse. CPR in progress.<end_of_turn>
<start_of_turn>model
{" \
  -n 600 --temp 1.0 --top-k 64 --top-p 0.95 -ngl 99

Training

Base model: google/gemma-4-e4b-it
Method: QLoRA via Unsloth, all 7 projection layers (q, k, v, o, gate, up, down)
LoRA rank 32, alpha 64, dropout 0.075
Dataset: 500 examples curated from SATS 2023 / WHO ETAT clinical scenarios
Languages: 8 language contexts, balanced across 5 triage levels
Hardware: RTX 5090, 2 epochs, AdamW 8-bit, bfloat16

Real-world Validation

Medic (medic.org), stewards of the Community Health Toolkit deployed to 100,000+ health workers across 15+ countries facilitating 8.7 million caring activities per quarter, reached out after seeing VoiceBridge. Their team expressed interest in an Android APK connecting to CHT via VoiceBridge's structured JSON output and asked us to prioritise Swahili and Bengali.

OminousDude
/

voicebridge-gemma4