- Libraries
- llama-cpp-python
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 Settings
- 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
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
