Instructions to use ianro04/PentaGemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ianro04/PentaGemma with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ianro04/PentaGemma",
	filename="gemma-4-e2b-it.F16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "\"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ianro04/PentaGemma 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 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

LM Studio
Jan
Ollama
How to use ianro04/PentaGemma with Ollama:
```
ollama run hf.co/ianro04/PentaGemma:F16
```

Unsloth Studio

How to use ianro04/PentaGemma 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 ianro04/PentaGemma 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 ianro04/PentaGemma to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ianro04/PentaGemma to start chatting

How to use ianro04/PentaGemma with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ianro04/PentaGemma:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ianro04/PentaGemma:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ianro04/PentaGemma with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ianro04/PentaGemma:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ianro04/PentaGemma:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ianro04/PentaGemma with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ianro04/PentaGemma:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ianro04/PentaGemma:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ianro04/PentaGemma with Docker Model Runner:
```
docker model run hf.co/ianro04/PentaGemma:F16
```

Lemonade

How to use ianro04/PentaGemma with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ianro04/PentaGemma:F16

Run and chat with the model

lemonade run user.PentaGemma-F16

List all available models

lemonade list

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

Downloads last month: 71

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for ianro04/PentaGemma

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(280)

this model

ianro04
/

PentaGemma

PentaGemma: 5-Sentence Summarizer

Available Model files:

Model tree for ianro04/PentaGemma

Dataset used to train ianro04/PentaGemma

Space using ianro04/PentaGemma 1