Instructions to use vanpelt/summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vanpelt/summarizer 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 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 serve -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
- Atomic Chat
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - text-generation | |
| - gguf | |
| - gemma | |
| - summarization | |
| base_model: google/gemma-3-270m-it | |
| model_type: gemma | |
| # 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](https://wandb.ai/vanpelt/summarizer/runs/0t4lcgpb) | |
| ## Usage | |
| ### With Ollama | |
| ```bash | |
| ollama pull hf.co/vanpelt/summarizer | |
| ollama run hf.co/vanpelt/summarizer | |
| ``` | |
| ### With llama.cpp | |
| ```bash | |
| # 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) | |