Instructions to use quelmap/Lightning-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quelmap/Lightning-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="quelmap/Lightning-4b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("quelmap/Lightning-4b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use quelmap/Lightning-4b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "quelmap/Lightning-4b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quelmap/Lightning-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/quelmap/Lightning-4b
- SGLang
How to use quelmap/Lightning-4b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "quelmap/Lightning-4b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quelmap/Lightning-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "quelmap/Lightning-4b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quelmap/Lightning-4b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use quelmap/Lightning-4b with Docker Model Runner:
docker model run hf.co/quelmap/Lightning-4b
Lightning-4b - Your Local data analysis agent
Overview
Lightning-4b is a language model specifically designed and trained for data analysis tasks on local devices. With just a laptop (fully tested on an M4 MacBook Air with 16GB RAM), you can process data without ever sending it to major LLM provider.
What it can do
- Data visualization
- Table joins
- t-tests
- Unlimited rows, 30+ tables analyzed simultaneously
What it cannot do
- Business reasoning or management decision-making advice
- Multi-turn analysis
To use this model, install quelmap on your device.
Quelmap is an open-source data analysis assistant with every essential features like data upload and an built-in python sandbox.
For installation instructions, see the Quick Start.

Performance
This model was trained specifically for use with quelmap.
It was evaluated using a sample database and 122 analysis queries, and achieved performance surpassing models with 50x more parameters.
For details about the model and its training process, see the Lightning-4b Details page.
Running Model on your machine
You can easily install Lightning-4b and quelmap by following the Quick Start.
Lightning-4b has multiple quantization versions depending on your hardware.
It runs smoothly on laptops, and on higher-spec machines it can handle more tables (30+ tables) and longer chat histories.
Example Specs and Model Versions
- Laptop (e.g. mac book air 16GB) - 4bit Quantization + 10,240 Context Window
ollama pull hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
- Gaming Laptop - 4bit Quantization + 40,960 Context Window
ollama pull hf.co/quelmap/Lightning-4b-GGUF:Q4_K_M
- Powerful PC with GPU - No Quantization + 40,960 Context Window
ollama pull hf.co/quelmap/Lightning-4b-GGUF:F16
For more details, please refer to the Lightning-4b Details page.
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