Instructions to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF", dtype="auto")

llama-cpp-python

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF",
	filename="VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF: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 vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF: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 vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M

Use Docker

docker model run hf.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M

LM Studio
Jan
Ollama
How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Ollama:
```
ollama run hf.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
```

Unsloth Studio

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF 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 vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF 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 vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF to start chatting

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF: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": "vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF: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 vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Docker Model Runner:
```
docker model run hf.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M
```

Lemonade

How to use vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF:Q4_K_M

Run and chat with the model

lemonade run user.VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF-Q4_K_M

List all available models

lemonade list

VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF / README.md

vinhnx90

Update README.md

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	---
	base_model:
	- unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
	- canopylabs/orpheus-3b-0.1-ft
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- gguf
	- tts
	license: apache-2.0
	language:
	- en
	datasets:
	- Jinsaryko/Ceylia
	---

	# Introduction

	VT-Orpheus-3B-TTS-lora-adapter is a Lora adapter fine-tuned from [Orpheus-TTS](https://github.com/canopyai/Orpheus-TTS).

	Dataset is from <https://huggingface.co/datasets/Jinsaryko/Ceylia>.

	# Sample Audio

	Check my [setup guide](https://huggingface.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF#running-locally) for running the local Orpheus model with my Lora adapter.

	```python
	python gguf_orpheus.py --text "Seriously? <giggle> That's the cutest thing I've ever heard ! " --voice ceylia
	```

	<audio controls><source src="https://huggingface.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF/resolve/main/output.wav" type="audio/wav"></audio>


	```python
	python gguf_orpheus.py --text "Hi! I'm Ceylia. <laugh> This is so exciting! <giggle>" --voice ceylia
	```

	<audio controls><source src="https://huggingface.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF/resolve/main/ceylia_20250409_010117.wav" type="audio/wav"></audio>


	```python
	python gguf_orpheus.py --text "Morning! <giggle> I finally finished that project last night. It took forever, but the results look amazing. <yawn> Sorry, still a bit tired from staying up so late." --voice ceylia
	```

	<audio controls><source src="https://huggingface.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF/resolve/main/ceylia_20250409_013043.wav" type="audio/wav"></audio>


	# Running Locally

	This section provides a step-by-step guide to running the `VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf` model locally on your machine. There are two main methods to run this model:

	## Method 1: Using LM Studio (Recommended for beginners)

	### Prerequisites

	1. [LM Studio](https://lmstudio.ai/) installed on your computer
	2. Python 3.8+ installed
	3. The `VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf` model file

	### Setup Steps

	1. Install LM Studio
	- Download and install LM Studio from [lmstudio.ai](https://lmstudio.ai/)
	- Launch LM Studio

	2. Load the GGUF model
	- In LM Studio, click "Add Model"
	- Select the `VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf` file from your computer
	- Once added, click on the model to load it

	3. Start the local server
	- Go to the "Local Server" tab in LM Studio
	- Click "Start Server" to launch the local API server (default address is `http://127.0.0.1:1234`)

	4. Clone orpheus-tts-local repository

	```bash
	git clone https://github.com/isaiahbjork/orpheus-tts-local.git
	cd orpheus-tts-local
	```

	5. Install dependencies

	```bash
	python -m venv venv
	source venv/bin/activate # On Windows: venv\Scripts\activate
	pip install -r requirements.txt
	```

	5.1 Edit gguf_orpheus.py to include new ceylia voice

	Open `gguf_orpheus.py` file in ./orpheus-tts-local directory, find the line of `AVAILABLE_VOICES` and `DEFAULT_VOICE` and edit to include ceylia voice, default is `tara`.

	```python
	# Available voices based on the Orpheus-TTS repository
	AVAILABLE_VOICES = ["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe", "ceylia"]
	DEFAULT_VOICE = "ceylia"
	```

	Save the file `gguf_orpheus.py`.

	6. Run the model

	```bash
	python gguf_orpheus.py --text "Hi! I'm Ceylia. <laugh> This is so exciting! <giggle>" --voice ceylia --output output.wav
	```

	### Available Parameters

	- `--text`: The text to convert to speech (required)
	- `--voice`: The voice to use (default is "tara", but use "ceylia" for this model)
	- `--output`: Output WAV file path (default: auto-generated filename)
	- `--temperature`: Temperature for generation (default: 0.6)
	- `--top_p`: Top-p sampling parameter (default: 0.9)
	- `--repetition_penalty`: Repetition penalty (default: 1.1)
	- `--backend`: Specify the backend (default: "lmstudio", also supports "ollama")

	## Method 2: Using llama.cpp directly

	### Prerequisites

	1. [llama.cpp](https://github.com/ggerganov/llama.cpp) installed and built on your system
	2. The [VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf](https://huggingface.co/vinhnx90/VT-Orpheus-3B-TTS-Ceylia-Q4KM-GGUFF/blob/main/VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf) model file

	### Setup Steps

	1. Clone and build llama.cpp

	```bash
	git clone https://github.com/ggerganov/llama.cpp
	cd llama.cpp
	cmake -B build
	cmake --build build --config Release
	```

	2. Start the server

	```bash
	./llama-server -m /path/to/VT-Orpheus-3B-TTS-Ceylia.Q4_K_M.gguf --port 8080
	```

	3. Clone orpheus-tts-local repository

	```bash
	git clone https://github.com/isaiahbjork/orpheus-tts-local.git
	cd orpheus-tts-local
	```

	4. Install dependencies

	```bash
	python -m venv venv
	source venv/bin/activate # On Windows: venv\Scripts\activate
	pip install -r requirements.txt
	```

	5. Run the model with custom API URL

	```bash
	python gguf_orpheus.py --text "Hi! I'm Ceylia. <laugh> Let's play! <sniffle> This is so exciting! <giggle>" --voice ceylia --output output.wav --api_url http://localhost:8080/v1
	```

	## Emotion Tags

	You can add emotion to the speech by including the following tags in your text:

	- `<giggle>`
	- `<laugh>`
	- `<chuckle>`
	- `<sigh>`
	- `<cough>`
	- `<sniffle>`
	- `<groan>`
	- `<yawn>`
	- `<gasp>`

	Example:

	```bash
	python gguf_orpheus.py --text "Hi! I'm Ceylia. <laugh> This is so exciting! <giggle>" --voice ceylia
	```

	## Troubleshooting

	1. Error connecting to server: Make sure LM Studio's server is running or llama.cpp server is running on the correct port
	2. Low-quality audio: Try adjusting the temperature (higher = more variance) or repetition_penalty (>1.1 recommended)
	3. Slow generation: Reduce model precision or run on a more powerful GPU if available

	# Uploaded model

	- Developed by: vinhnx90
	- License: apache-2.0
	- Finetuned from model : unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit

	This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)