Instructions to use Hack337/WavGPT-1.5-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 Hack337/WavGPT-1.5-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 Hack337/WavGPT-1.5-GGUF
# Run inference directly in the terminal:
llama cli -hf Hack337/WavGPT-1.5-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hack337/WavGPT-1.5-GGUF
# Run inference directly in the terminal:
llama cli -hf Hack337/WavGPT-1.5-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 Hack337/WavGPT-1.5-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Hack337/WavGPT-1.5-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 Hack337/WavGPT-1.5-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hack337/WavGPT-1.5-GGUF

Use Docker

docker model run hf.co/Hack337/WavGPT-1.5-GGUF

LM Studio
Jan

vLLM

How to use Hack337/WavGPT-1.5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hack337/WavGPT-1.5-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hack337/WavGPT-1.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hack337/WavGPT-1.5-GGUF

Ollama
How to use Hack337/WavGPT-1.5-GGUF with Ollama:
```
ollama run hf.co/Hack337/WavGPT-1.5-GGUF
```
Unsloth Desktop
Docker Model Runner
How to use Hack337/WavGPT-1.5-GGUF with Docker Model Runner:
```
docker model run hf.co/Hack337/WavGPT-1.5-GGUF
```

Lemonade

How to use Hack337/WavGPT-1.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hack337/WavGPT-1.5-GGUF

Run and chat with the model

lemonade run user.WavGPT-1.5-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

WavGPT-1.5-GGUF

Quickstart

Check out our llama.cpp documentation for more usage guide.

We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository llama.cpp.

Since cloning the entire repo may be inefficient, you can manually download the GGUF file that you need or use huggingface-cli:

Install
```
pip install -U huggingface_hub
```

Download:

huggingface-cli download Hack337/WavGPT-1.5-GGUF WavGPT-1.5.gguf --local-dir . --local-dir-use-symlinks False

For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:

./llama-cli -m <gguf-file-path> \
    -co -cnv -p "Вы очень полезный помощник." \
    -fa -ngl 80 -n 512

Downloads last month: 30

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Hack337/WavGPT-1.5-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Adapter

Hack337/WavGPT-1.5

Quantized

(2)

this model