Instructions to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lex-au/Orpheus-3b-FT-Q4_K_M.gguf",
	filename="Orpheus-3b-FT-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lex-au/Orpheus-3b-FT-Q4_K_M.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 lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lex-au/Orpheus-3b-FT-Q4_K_M.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 lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

Use Docker

docker model run hf.co/lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with Ollama:
```
ollama run hf.co/lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
```

Unsloth Studio new

How to use lex-au/Orpheus-3b-FT-Q4_K_M.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 lex-au/Orpheus-3b-FT-Q4_K_M.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 lex-au/Orpheus-3b-FT-Q4_K_M.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lex-au/Orpheus-3b-FT-Q4_K_M.gguf to start chatting

Pi new

How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

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": "lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lex-au/Orpheus-3b-FT-Q4_K_M.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 lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with Docker Model Runner:
```
docker model run hf.co/lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M
```

Lemonade

How to use lex-au/Orpheus-3b-FT-Q4_K_M.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lex-au/Orpheus-3b-FT-Q4_K_M.gguf:Q4_K_M

Run and chat with the model

lemonade run user.Orpheus-3b-FT-Q4_K_M.gguf-Q4_K_M

List all available models

lemonade list

Orpheus-3b-FT-Q4_K_M

This is a quantised version of canopylabs/orpheus-3b-0.1-ft.

Orpheus is a high-performance Text-to-Speech model fine-tuned for natural, emotional speech synthesis. This repository hosts the 8-bit quantised version of the 3B parameter model, optimised for efficiency while maintaining high-quality output.

Model Description

Orpheus-3b-FT-Q4_K_M is a 3 billion parameter Text-to-Speech model that converts text inputs into natural-sounding speech with support for multiple voices and emotional expressions. The model has been quantised to 8-bit (Q4_K_M) format for efficient inference, making it accessible on consumer hardware.

Key features:

8 distinct voice options with different characteristics
Support for emotion tags like laughter, sighs, etc.
Optimised for CUDA acceleration on RTX GPUs
Produces high-quality 24kHz mono audio
Fine-tuned for conversational naturalness

How to Use

This model is designed to be used with an LLM inference server that connects to the Orpheus-FastAPI frontend, which provides both a web UI and OpenAI-compatible API endpoints.

Compatible Inference Servers

This quantised model can be loaded into any of these LLM inference servers:

GPUStack - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
LM Studio - Load the GGUF model and start the local server
llama.cpp server - Run with the appropriate model parameters
Any compatible OpenAI API-compatible server

Quick Start

Download this quantised model from lex-au's Orpheus-FASTAPI collection
Load the model in your preferred inference server and start the server.
Clone the Orpheus-FastAPI repository:

git clone https://github.com/Lex-au/Orpheus-FastAPI.git
cd Orpheus-FastAPI

Configure the FastAPI server to connect to your inference server by setting the ORPHEUS_API_URL environment variable.
Follow the complete installation and setup instructions in the repository README.

Audio Samples

Listen to the model in action with different voices and emotions:

Default Voice Sample

Leah (Happy)

Tara (Sad)

Zac (Contemplative)

Available Voices

The model supports 8 different voices:

tara: Female, conversational, clear
leah: Female, warm, gentle
jess: Female, energetic, youthful
leo: Male, authoritative, deep
dan: Male, friendly, casual
mia: Female, professional, articulate
zac: Male, enthusiastic, dynamic
zoe: Female, calm, soothing

Emotion Tags

You can add expressiveness to speech by inserting tags:

<laugh>, <chuckle>: For laughter sounds
<sigh>: For sighing sounds
<cough>, <sniffle>: For subtle interruptions
<groan>, <yawn>, <gasp>: For additional emotional expression

Technical Specifications

Architecture: Specialised token-to-audio sequence model
Parameters: ~3 billion
Quantisation: 8-bit (GGUF Q4_K_M format)
Audio Sample Rate: 24kHz
Input: Text with optional voice selection and emotion tags
Output: High-quality WAV audio
Language: English
Hardware Requirements: CUDA-compatible GPU (recommended: RTX series)
Integration Method: External LLM inference server + Orpheus-FastAPI frontend

Limitations

Currently supports English text only
Best performance achieved on CUDA-compatible GPUs
Generation speed depends on GPU capability

License

This model is available under the Apache License 2.0.

Citation & Attribution

The original Orpheus model was created by Canopy Labs. This repository contains a quantised version optimised for use with the Orpheus-FastAPI server.

If you use this quantised model in your research or applications, please cite:

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

@misc{orpheus-quantised-2025,
  author = {Lex-au},
  title = {Orpheus-3b-FT-Q4_K_M: Quantised TTS Model with FastAPI Server},
  note = {GGUF quantisation of canopylabs/orpheus-3b-0.1-ft},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-FT-Q4_K_M.gguf}}
}

Downloads last month: 375

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

4-bit

Collection including lex-au/Orpheus-3b-FT-Q4_K_M.gguf

Orpheus-FASTAPI

Collection

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