WebLLM is a library for executing large language models directly within web browsers. It provides a framework for building conversational artificial intelligence applications that perform inference locally, ensuring user data privacy by eliminating the need for external server dependencies.
The project distinguishes itself by leveraging browser-native graphics APIs to perform intensive machine learning computations on the client side. It maintains application responsiveness by offloading heavy model tasks to background threads and ensures continuous operation through service workers that function independently of the active browser tab lifecycle. Additionally, it supports persistent storage of model weights to avoid redundant downloads across sessions and allows for the integration of custom model architectures.
The library includes a comprehensive suite of tools for managing the model lifecycle, including initialization, weight loading, and memory management. It offers a standardized interface that mimics common service protocols, allowing developers to integrate local inference into existing workflows. The system also provides fine-grained control over output through logit bias configuration and includes utilities for inspecting hardware capabilities to verify environment compatibility.
