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flasgship pretrained models • 3 items • Updated
How to use from
llama.cppInstall 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.0Use 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.0Build 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.0Use Docker
docker model run hf.co/appvoid/void.0Quick Links
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% |
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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Install (macOS, Linux)
# 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