Instructions to use salihfurkaan/VoxPolska-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salihfurkaan/VoxPolska-V1-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="salihfurkaan/VoxPolska-V1-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("salihfurkaan/VoxPolska-V1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use salihfurkaan/VoxPolska-V1-GGUF with Ollama:
ollama run hf.co/salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
- Unsloth Studio
How to use salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for salihfurkaan/VoxPolska-V1-GGUF to start chatting
- Pi
How to use salihfurkaan/VoxPolska-V1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf salihfurkaan/VoxPolska-V1-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": "salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-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 salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
Run Hermes
hermes
- OpenClaw new
How to use salihfurkaan/VoxPolska-V1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf salihfurkaan/VoxPolska-V1-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 "salihfurkaan/VoxPolska-V1-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"
- Docker Model Runner
How to use salihfurkaan/VoxPolska-V1-GGUF with Docker Model Runner:
docker model run hf.co/salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
- Lemonade
How to use salihfurkaan/VoxPolska-V1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull salihfurkaan/VoxPolska-V1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.VoxPolska-V1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf salihfurkaan/VoxPolska-V1-GGUF:# Run inference directly in the terminal:
llama cli -hf salihfurkaan/VoxPolska-V1-GGUF: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 salihfurkaan/VoxPolska-V1-GGUF:# Run inference directly in the terminal:
./llama-cli -hf salihfurkaan/VoxPolska-V1-GGUF: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 salihfurkaan/VoxPolska-V1-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf salihfurkaan/VoxPolska-V1-GGUF:Use Docker
docker model run hf.co/salihfurkaan/VoxPolska-V1-GGUF:VoxPolska GGUF: Next-Gen Polish Voice Generation
📌 Model Highlights
- Context-Aware Voice: Generates speech that captures the nuances and tone of the Polish language.
- Realistic Speech Output: Produces fluent and expressive speech with natural intonations, ideal for a variety of use cases.
- Showcases Advanced Speech Synthesis: Highlights proficiency in generating human-like voice output tailored to the Polish language.
- Optimized for GGUF: Fine-tuned specifically for GGUF implementation, offering fast and efficient inference.
- Advanced Deep Learning: Utilizes cutting-edge deep learning techniques, enabling optimal performance across various applications.
🔧 Technical Details
- Base Model: Orpheus TTS
- LoRA (Low-Rank Adaptation): Fine-tuning applied for enhanced performance and efficiency.
- Sample Rate: 24 kHz audio output ensuring high-fidelity sound.
- Training Data: Trained with 24000+ Polish transcript and audio pairs, ensuring natural speech generation.
- Quantization: Merged 16-bit quantization for balanced performance and memory efficiency.
- Audio Decoding: Custom layer-wise processing for high-quality audio generation.
- Repetition Penalty: Set to 1.1 to avoid repetitive phrases and enhance speech naturalness.
- Gradient Checkpointing: Enabled for efficient memory usage and faster inference on constrained environments.
🎧 Example Usage
Using with LM Studio
You need to have Python 3.8+ installed on your computer.
Steps
- Install and launch LM Studio
- Download the GGUF file
- Download 4 bit version
- Download 5 bit version
- Download 8 bit version
- If you would like to load the model from LM Studio directly, type salihfurkaan/voxpolska-v1-gguf and choose your preferred version.
- Load the GGUF file
- Skip this step if you have loaded the model from LM Studio directly. If not, follow the following steps:
- Click on "My Models". You will see the models directory. Go to that path.
- In "models" folder, create a new folder named "salihfurkaan" and go to this folder.
- In "salihfurkaan", create a new folder named "VoxPolska-V1-GGUF"
- In "VoxPolska-V1-GGUF", put your gguf file(s)
- Start the local server
- Click on "Developer" sidebar on LM Studio
- Press CTRL + L and load the model.
- Press CTRL + R to start the local server
- Clone orpheus-tts-local repository and install the dependencies
git clone https://github.com/isaiahbjork/orpheus-tts-local.git cd orpheus-tts-local python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt - Put your Huggingface token to the files in orpheus-tts-local folder and save the file
- Add the code below in the Python file and save the file.
import os os.environ["HF_TOKEN"] = "your huggingface token here" - You can get your token from here
- Add the code below in the Python file and save the file.
- Run the model
- Run the bash command below:
python gguf_orpheus.py --text "Your Polish text here" --output output.wav
- Run the bash command below:
You can access to output.wav in orpheus-tts-local folder.
Available Flags
--text: The text to convert to speech (required)--voice: The voice to use (default is "tara")--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")
Using Llama.cpp
You need to have CMake installed on your computer.- Install llama.cpp:
- Use the command below to install and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp cd llama.cpp cmake -B build cmake --build build --config Release- Download the GGUF file
- Download 4 bit version
- Download 5 bit version
- Download 8 bit version
- Start the server
- Use the command below
./llama-server -m path/to/gguf/file --port 8080
- Use the command below
- Clone orpheus-tts-local repository and install the dependencies
git clone https://github.com/isaiahbjork/orpheus-tts-local.git cd orpheus-tts-local python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt - Put your Huggingface token to the files in orpheus-tts-local folder and save the file
- Add the code below in the Python file and save the file.
import os os.environ["HF_TOKEN"] = "your huggingface token here" - You can get your token from here
- Add the code below in the Python file and save the file.
- Run the model
- Run the bash command below:
python gguf_orpheus.py --text "Your Polish text here" --output output.wav
- Run the bash command below:
You can access to output.wav in orpheus-tts-local folder.
📫 Contact and Support
For questions, suggestions, and feedback, please open an issue on HuggingFace. You can also reach out via: LinkedIn
Model Misuse
Do not use this model for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By using this model, you agree to follow all applicable laws and ethical guidelines.
Citation
@misc{
title={salihfurkaan/VoxPolska-V1-GGUF},
author={Salih Furkan Erik},
year={2025},
url={https://huggingface.co/salihfurkaan/VoxPolska-V1-GGUF/}
}
- Downloads last month
- 84
4-bit
5-bit
8-bit
Model tree for salihfurkaan/VoxPolska-V1-GGUF
Base model
meta-llama/Llama-3.2-3B-Instruct
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf salihfurkaan/VoxPolska-V1-GGUF:# Run inference directly in the terminal: llama cli -hf salihfurkaan/VoxPolska-V1-GGUF: