Who created WhiteLightning?

WhiteLightning was created by two developers named Volodymyr and Volodymyr from Ukraine, as part of Inoxoft's AI and ML engineering team.

What are the main benefits of using WhiteLightning?

The main benefits include: compact model size (under 1MB), ability to run offline without API costs, compatibility with minimal hardware (like Raspberry Pi or older phones), and one-time LLM usage for training (costing around one cent per task).

Where can WhiteLightning models be deployed?

WhiteLightning models can be deployed anywhere, including microcontrollers, mobile apps, edge devices, embedded systems, and offline environments.

How does WhiteLightning work?

It uses a teacher-student approach to distill LLM-generated synthetic data into compact ONNX models. The tool uses LLMs only once during training to generate data, then creates a lightweight classifier that can run independently.

What is the typical model performance?

Based on the example shown, the model can achieve high accuracy (up to 1.0000 on test sets) after training, while maintaining a very small size suitable for edge deployment.

WhiteLightning

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WhiteLightning is an open-source CLI tool that enables developers to create fast, lightweight text classifiers that can run entirely offline by distilling large language models into compact ONNX models under 1MB.

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更新時間：2025年08月09日

什麼是 WhiteLightning

WhiteLightning, developed by Inoxoft, is an innovative command-line interface tool designed to democratize natural language processing (NLP) capabilities for edge devices and embedded systems. Released in 2025, this open-source solution addresses the growing need for intelligent, privacy-safe NLP in offline environments. Unlike traditional NLP solutions that rely on cloud APIs or large language models at runtime, WhiteLightning enables developers to create and run compact text classifiers locally without ongoing cloud dependencies.

WhiteLightning 的主要功能

WhiteLightning is an open-source CLI tool that enables developers to create compact, efficient text classifiers by distilling large language models (LLMs) into lightweight ONNX models under 1MB. It allows for local-first AI capabilities without cloud dependencies, offering privacy-safe NLP solutions that can run entirely offline on edge devices and embedded systems.

Local-First Architecture: Operates completely offline without requiring cloud APIs or LLMs at runtime, enabling privacy-focused and infrastructure-independent deployment

Compact Model Output: Generates ultra-compact ONNX models under 1MB in size that can run on resource-constrained devices

Multi-Platform Compatibility: Exports models that can run natively in multiple programming languages including Python, JavaScript, C++, Rust, and Java

Docker-Based Deployment: Comes as a production-ready Docker image that works seamlessly across macOS, Linux, and Windows environments

WhiteLightning 的使用案例

Edge Device Processing: Enable NLP capabilities on IoT devices and embedded systems where cloud connectivity isn't available or reliable

Privacy-Sensitive Applications: Process sensitive text data locally in healthcare, financial, or government applications where data privacy is crucial

Mobile Applications: Integrate lightweight text classification capabilities into mobile apps without requiring constant server communication

優點

No recurring API costs or cloud dependencies

Complete control over model deployment and data privacy

Highly portable and resource-efficient

缺點

Requires technical expertise to implement

Limited to classification tasks rather than full LLM capabilities

如何使用 WhiteLightning

Install Docker: Ensure Docker is installed on your system as WhiteLightning runs as a Docker container

Get API Key: Obtain an OpenRouter API key which will be used to access large language models like GPT-4, Claude 4, or Grok

Create Working Directory: Create a directory where the generated models will be saved

Run Docker Command: Execute the Docker command with proper parameters: docker run --rm -v "$(pwd)":/app/models -e OPEN_ROUTER_API_KEY="YOUR_KEY_HERE" ghcr.io/inoxoft/whitelightning:latest

Define Classification Task: Use the -p flag to describe your classification task (e.g., -p "Categorize customer reviews as positive, neutral, or negative")

Wait for Processing: The tool will automatically generate synthetic training data, train a compact model, and evaluate its performance

Collect Output Model: Find the exported ONNX model in your working directory, which will be under 1MB in size

Deploy Model: Use the generated ONNX model in your application - compatible with multiple programming languages including Python, JavaScript, C++, Rust, Java

WhiteLightning 常見問題

WhiteLightning is an open-source CLI tool developed by Inoxoft that converts large language models (like Claude 4, Grok 4, GPT-4) into tiny ONNX text classifiers that can run offline and locally.