How to use from
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 ianro04/PentaGemma:F16
# Run inference directly in the terminal:
llama cli -hf ianro04/PentaGemma:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ianro04/PentaGemma:F16
# Run inference directly in the terminal:
llama cli -hf ianro04/PentaGemma:F16
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 ianro04/PentaGemma:F16
# Run inference directly in the terminal:
./llama-cli -hf ianro04/PentaGemma:F16
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 ianro04/PentaGemma:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ianro04/PentaGemma:F16
Use Docker
docker model run hf.co/ianro04/PentaGemma:F16
Quick Links

PentaGemma: 5-Sentence Summarizer

PentaGemma is a fine-tuned version of Gemma 4 E2B IT, but for 5-sentence text summarization and general obsession with the number 5. This was done as an amateur hobby project. Intended to be used in its dedicated Space, but can be very slow for large documents as it's only running on CPU there.

This project is licensed under Apache-2.0.

(Kaggle Notebook | GitHub)

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

  • llama-cli -hf ianro04/PentaGemma --jinja

Available Model files:

  • gemma-4-e2b-it.Q4_K_S.gguf (Primary purpose)
  • gemma-4-e2b-it.F16-mmproj.gguf (If you want multimodal for whatever reason)

This was trained 2x faster with Unsloth

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GGUF
Model size
5B params
Architecture
gemma4
Hardware compatibility
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