Instructions to use naazimsnh02/voiceown-base-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 naazimsnh02/voiceown-base-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 naazimsnh02/voiceown-base-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf naazimsnh02/voiceown-base-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

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 naazimsnh02/voiceown-base-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

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 naazimsnh02/voiceown-base-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

Use Docker

docker model run hf.co/naazimsnh02/voiceown-base-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use naazimsnh02/voiceown-base-gguf with Ollama:
```
ollama run hf.co/naazimsnh02/voiceown-base-gguf:Q4_K_M
```
Unsloth Desktop

How to use naazimsnh02/voiceown-base-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "naazimsnh02/voiceown-base-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use naazimsnh02/voiceown-base-gguf with Docker Model Runner:
```
docker model run hf.co/naazimsnh02/voiceown-base-gguf:Q4_K_M
```

Lemonade

How to use naazimsnh02/voiceown-base-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull naazimsnh02/voiceown-base-gguf:Q4_K_M

Run and chat with the model

lemonade run user.voiceown-base-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use naazimsnh02/voiceown-base-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 naazimsnh02/voiceown-base-gguf:Q4_K_M

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 naazimsnh02/voiceown-base-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use naazimsnh02/voiceown-base-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf naazimsnh02/voiceown-base-gguf:Q4_K_M

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 "naazimsnh02/voiceown-base-gguf:Q4_K_M" \
  --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"

VoiceOwn — Stutter-Aware Intelligence

VoiceOwn is a specialized adaptation of Gemma 4 E2B, engineered to bridge communication gaps for individuals who stutter.

Unlike conventional ASR systems that transcribe every disfluency, this model performs intent-focused speech understanding—filtering repetitions, prolongations, and blocks to produce clean, intended language.

✨ Core Capabilities

Stutter-Awareness
Handles repetitions, prolongations, and speech blocks natively.
Intent Extraction
Identifies the speaker’s intended words rather than literal disfluent output.
Multimodal Intelligence
Uses Gemma 4’s audio encoder to interpret timing, tone, and structure of speech.

📦 Model Weights (GGUF)

File	Description
`voiceown-base-Q4_K_M.gguf`	Mobile-optimized
`voiceown-base-Q8_0.gguf`	Higher-fidelity evaluation build
`gemma-4-e2b-it.BF16-mmproj.gguf`	Required multimodal projector (core logic)

🧪 Training Insights

Objective: Intent-accurate output from disfluent speech
Dataset: naazimsnh02/voiceown-stutter-asr
Samples: 2,850 real-world recordings
Epochs: 2
Training Loss: 1.1124
Hardware: NVIDIA A100-SXM4
Training Time: ~60 minutes

⚙️ Usage

Run with llama.cpp multimodal CLI:

./llama-mtmd-cli \
  -m voiceown-base-Q4_K_M.gguf \
  --mmproj gemma-4-e2b-it.BF16-mmproj.gguf \
  --audio user_clip.wav \
  -p "Capture the speaker's intended words, ignoring any stutters."