Instructions to use lex-au/Orpheus-3b-Kaya-Q6_K.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 lex-au/Orpheus-3b-Kaya-Q6_K.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 lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
# Run inference directly in the terminal:
llama cli -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
# Run inference directly in the terminal:
llama cli -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

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 lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

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 lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

Use Docker

docker model run hf.co/lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

LM Studio
Jan
Ollama
How to use lex-au/Orpheus-3b-Kaya-Q6_K.gguf with Ollama:
```
ollama run hf.co/lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
```
Unsloth Desktop

How to use lex-au/Orpheus-3b-Kaya-Q6_K.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

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": "lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lex-au/Orpheus-3b-Kaya-Q6_K.gguf with Docker Model Runner:
```
docker model run hf.co/lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K
```

Lemonade

How to use lex-au/Orpheus-3b-Kaya-Q6_K.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

Run and chat with the model

lemonade run user.Orpheus-3b-Kaya-Q6_K.gguf-Q6_K

List all available models

lemonade list

Hermes Agent

How to use lex-au/Orpheus-3b-Kaya-Q6_K.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 lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

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 lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lex-au/Orpheus-3b-Kaya-Q6_K.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K

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 "lex-au/Orpheus-3b-Kaya-Q6_K.gguf:Q6_K" \
  --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"

Orpheus-3b-Kaya-Q6_K

This is a fine-tuned version of the pretrained model canopylabs/orpheus-3b-0.1-pretrained, trained on a custom voice dataset and quantised to GGUF Q6_K format for fast, efficient inference.

🔧 Model Details

Model Type: Text-to-Speech (TTS)
Architecture: Token-to-audio language model
Parameters: ~3 billion
Quantisation: 8-bit GGUF (Q6_K)
Sampling Rate: 24kHz mono
Training Epochs: 1
Training Dataset: lex-au/Orpheus-3b-Kaya
Languages: English

🚀 Quick Usage

This model is designed for use with Orpheus-FastAPI, an OpenAI-compatible inference server for text-to-speech generation.

Compatible Inference Servers

You can load this model into:

GPUStack
LM Studio
llama.cpp
Any other GGUF-compatible OpenAI-style server

📜 License

Apache License 2.0 — free for research and commercial use.

🙌 Credits

Original model by: Canopy Labs
Fine-tuned, quantised, and API-wrapped by: Lex-au via Unsloth and Huggingface's TRL library.

📚 Citation

@misc{orpheus-tts-2025,
  author = {Canopy Labs},
  title = {Orpheus-3b-0.1-pt: Pretrained Text-to-Speech Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/canopylabs/orpheus-3b-0.1-pt}}
}

@misc{orpheus-kaya-2025,
  author = {Lex-au},
  title = {Orpheus-3b-Kaya-Q6_K: Fine-Tuned TTS Model (Quantised)},
  note = {Fine-tuned from canopylabs/orpheus-3b-0.1-pt},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-Kaya-Q6_K}}
}

Downloads last month: 20

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

6-bit

Dataset used to train lex-au/Orpheus-3b-Kaya-Q6_K.gguf

Collection including lex-au/Orpheus-3b-Kaya-Q6_K.gguf

Orpheus-FASTAPI

Collection

Collection of quants for Canopy's Orpheus • 14 items • Updated Apr 19, 2025 • 16