Instructions to use quelmap/Lightning-4b-GGUF-short-ctx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quelmap/Lightning-4b-GGUF-short-ctx with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="quelmap/Lightning-4b-GGUF-short-ctx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("quelmap/Lightning-4b-GGUF-short-ctx", dtype="auto")

llama-cpp-python

How to use quelmap/Lightning-4b-GGUF-short-ctx with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="quelmap/Lightning-4b-GGUF-short-ctx",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use quelmap/Lightning-4b-GGUF-short-ctx with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf quelmap/Lightning-4b-GGUF-short-ctx: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 quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf quelmap/Lightning-4b-GGUF-short-ctx: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 quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

Use Docker

docker model run hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

LM Studio
Jan

vLLM

How to use quelmap/Lightning-4b-GGUF-short-ctx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quelmap/Lightning-4b-GGUF-short-ctx"
# 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-GGUF-short-ctx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

SGLang

How to use quelmap/Lightning-4b-GGUF-short-ctx 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-GGUF-short-ctx" \
    --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-GGUF-short-ctx",
		"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-GGUF-short-ctx" \
        --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-GGUF-short-ctx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use quelmap/Lightning-4b-GGUF-short-ctx with Ollama:
```
ollama run hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
```

Unsloth Studio new

How to use quelmap/Lightning-4b-GGUF-short-ctx 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 quelmap/Lightning-4b-GGUF-short-ctx 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 quelmap/Lightning-4b-GGUF-short-ctx to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for quelmap/Lightning-4b-GGUF-short-ctx to start chatting

Pi new

How to use quelmap/Lightning-4b-GGUF-short-ctx with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

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": "quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use quelmap/Lightning-4b-GGUF-short-ctx with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

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 quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use quelmap/Lightning-4b-GGUF-short-ctx with Docker Model Runner:
```
docker model run hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M
```

Lemonade

How to use quelmap/Lightning-4b-GGUF-short-ctx with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

Run and chat with the model

lemonade run user.Lightning-4b-GGUF-short-ctx-Q4_K_M

List all available models

lemonade list

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.

Downloads last month: 23

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for quelmap/Lightning-4b-GGUF-short-ctx

Base model

Qwen/Qwen3-4B-Thinking-2507

Quantized

(99)

this model

Collection including quelmap/Lightning-4b-GGUF-short-ctx

Lightning-4b

Collection

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 • 3 items • Updated Oct 15, 2025 • 1