Instructions to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-to-speech", model="prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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": "prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.VyvoTTS-v0-Qwen3-0.6B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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 "prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-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"

VyvoTTS-v0-Qwen3-0.6B-GGUF

VyvoTTS-v0-Qwen3-0.6B is an English Text-to-Speech (TTS) model built on the Qwen3-0.6B architecture and trained using a 10,000-hour dataset to produce natural-sounding speech. With approximately 810 million parameters and licensed under MIT, the model offers flexible usage as a pretrained base for further development, especially recommended to be enhanced by leveraging the Emilia dataset and fine-tuning for single-speaker scenarios. Users can integrate VyvoTTS with the unsloth and SNAC frameworks for speech generation, and the model supports sequence lengths up to 8,192 tokens. Although it currently exhibits a high Word Error Rate (WER), its open-source nature and compatibility with popular Python libraries make it an accessible starting point for advanced speech synthesis projects.

Model Files

File Name	Size	Quant Type
VyvoTTS-v0-Qwen3-0.6B.BF16.gguf	1.26 GB	BF16
VyvoTTS-v0-Qwen3-0.6B.F16.gguf	1.26 GB	F16
VyvoTTS-v0-Qwen3-0.6B.F32.gguf	2.51 GB	F32
VyvoTTS-v0-Qwen3-0.6B.Q2_K.gguf	321 MB	Q2_K
VyvoTTS-v0-Qwen3-0.6B.Q3_K_L.gguf	393 MB	Q3_K_L
VyvoTTS-v0-Qwen3-0.6B.Q3_K_M.gguf	372 MB	Q3_K_M
VyvoTTS-v0-Qwen3-0.6B.Q3_K_S.gguf	348 MB	Q3_K_S
VyvoTTS-v0-Qwen3-0.6B.Q4_0.gguf	406 MB	Q4_0
VyvoTTS-v0-Qwen3-0.6B.Q4_1.gguf	434 MB	Q4_1
VyvoTTS-v0-Qwen3-0.6B.Q4_K.gguf	421 MB	Q4_K
VyvoTTS-v0-Qwen3-0.6B.Q4_K_M.gguf	421 MB	Q4_K_M
VyvoTTS-v0-Qwen3-0.6B.Q4_K_S.gguf	408 MB	Q4_K_S
VyvoTTS-v0-Qwen3-0.6B.Q5_0.gguf	461 MB	Q5_0
VyvoTTS-v0-Qwen3-0.6B.Q5_1.gguf	489 MB	Q5_1
VyvoTTS-v0-Qwen3-0.6B.Q5_K.gguf	469 MB	Q5_K
VyvoTTS-v0-Qwen3-0.6B.Q5_K_M.gguf	469 MB	Q5_K_M
VyvoTTS-v0-Qwen3-0.6B.Q5_K_S.gguf	461 MB	Q5_K_S
VyvoTTS-v0-Qwen3-0.6B.Q6_K.gguf	520 MB	Q6_K
VyvoTTS-v0-Qwen3-0.6B.Q8_0.gguf	671 MB	Q8_0

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 135

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/VyvoTTS-v0-Qwen3-0.6B-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

Vyvo/VyvoTTS-v0-Qwen3-0.6B

Quantized

(2)

this model