Instructions to use appvoid/void.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use appvoid/void.0 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 appvoid/void.0
# Run inference directly in the terminal:
llama cli -hf appvoid/void.0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/void.0
# Run inference directly in the terminal:
llama cli -hf appvoid/void.0

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 appvoid/void.0
# Run inference directly in the terminal:
./llama-cli -hf appvoid/void.0

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 appvoid/void.0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf appvoid/void.0

Use Docker

docker model run hf.co/appvoid/void.0

LM Studio
Jan
Ollama
How to use appvoid/void.0 with Ollama:
```
ollama run hf.co/appvoid/void.0
```

Unsloth Studio

How to use appvoid/void.0 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 appvoid/void.0 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 appvoid/void.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for appvoid/void.0 to start chatting

Docker Model Runner
How to use appvoid/void.0 with Docker Model Runner:
```
docker model run hf.co/appvoid/void.0
```

Lemonade

How to use appvoid/void.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/void.0

Run and chat with the model

lemonade run user.void.0-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Introducing void: our first ever language model, trained from scratch with a novel hybrid tokenizer on 300M high-quality tokens (total of 2 epochs on a B300) using 4096 as context window. Total cost was $23 dollars. 132m parameters. Future releases are expected to be published in the following weeks/months.

Benchmark	Accuracy	Normalized
ARC Challenge	25.17%	27.22%
ARC Easy	45.03%	43.01%
HellaSwag	31.57%	35.77%
PIQA	61.15%	60.12%
WinoGrande	53.12%	—
ArithMark	35.20%	35.20%

If you want to sponsor future model releases, you can get information on how to make contributions here: CEAMFA

Disclaimer: Even though the model is based on gemma 3 architecture, the tokenizer is different so you might need to wait until this model can be added to llama.cpp

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