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

Libraries

How to use vanpelt/summarizer with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vanpelt/summarizer",
	filename="gemma3-270m-summarizer-Q4_K_M.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

llama.cpp

How to use vanpelt/summarizer with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanpelt/summarizer:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vanpelt/summarizer:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanpelt/summarizer:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vanpelt/summarizer:Q4_K_M

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 vanpelt/summarizer:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vanpelt/summarizer:Q4_K_M

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 vanpelt/summarizer:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vanpelt/summarizer:Q4_K_M

Use Docker

docker model run hf.co/vanpelt/summarizer:Q4_K_M

LM Studio
Jan
Ollama
How to use vanpelt/summarizer with Ollama:
```
ollama run hf.co/vanpelt/summarizer:Q4_K_M
```

Unsloth Studio new

How to use vanpelt/summarizer 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 vanpelt/summarizer 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 vanpelt/summarizer to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use vanpelt/summarizer with Docker Model Runner:
```
docker model run hf.co/vanpelt/summarizer:Q4_K_M
```

Lemonade

How to use vanpelt/summarizer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vanpelt/summarizer:Q4_K_M

Run and chat with the model

lemonade run user.summarizer-Q4_K_M

List all available models

lemonade list

summarizer

Fine-tuned Gemma-3-270M for task summarization and branch naming

Model Details

Base Model: google/gemma-3-270m-it
Format: GGUF (quantized for efficient inference)
Quantization: Q4_K_M
Use Case: Generating concise task titles and git branch names

Training

Training Run: https://wandb.ai/vanpelt/summarizer/runs/0t4lcgpb

Usage

With Ollama

ollama pull hf.co/vanpelt/summarizer
ollama run hf.co/vanpelt/summarizer

With llama.cpp

# Download the GGUF file
huggingface-cli download vanpelt/summarizer gemma3-270m-summarizer-Q4_K_M.gguf

# Run with llama.cpp
./main -m gemma3-270m-summarizer-Q4_K_M.gguf -p 'Your prompt here'

Files

tokenizer.json (31.8 MB)
tokenizer_config.json (1.1 MB)
added_tokens.json (0.0 MB)
chat_template.jinja (0.0 MB)
Modelfile (0.0 MB)
template (0.0 MB)
system (0.0 MB)
model.safetensors (511.4 MB)
gemma3-270m-summarizer-Q4_K_M.gguf (241.4 MB)
special_tokens_map.json (0.0 MB)
config.json (0.0 MB)
params (0.0 MB)
tokenizer.model (4.5 MB)

Downloads last month: 11

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

4-bit

Model tree for vanpelt/summarizer

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Quantized

(86)

this model