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
- llama.cpp
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 Desktop
- 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
Update README.md
Browse files
README.md
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@@ -54,6 +54,6 @@ Introducing **void**: our first ever language model, trained from scratch with a
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| WinoGrande | 53.12% | — |
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| PIQA | 61.15% | 60.12% |
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If you want to sponsor future model releases, you can get information on how to make contributions here: [CEAMFA](https://huggingface.co/CEAMFA)
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**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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