🌊 Surfing Weights

Welcome to Surfing Weights - a Python server for streaming transformer model weights to enable efficient AI inference on edge devices, IoT, and mobile platforms.

Overview

Surfing Weights solves the challenge of deploying large AI models to resource-constrained environments by streaming model weights on-demand instead of requiring the entire model to be downloaded upfront.

Key Features

• 🚫 Zero Local Storage: Stream model weights as needed instead of downloading entire models
• 📦 Smart Caching: LRU cache for frequently used layers with configurable cache size
• 📱 Edge Optimized: Designed for resource-constrained devices (IoT, mobile, embedded)
• 🤗 HuggingFace Compatible: Works with existing transformer models from HuggingFace Hub
• ⚡ Async Architecture: Non-blocking inference with async/await support
• 🚀 Production Ready: Monitoring, compression, and distributed caching support

Quick Example

from streaming_weights import WeightServer
import asyncio

async def start_server():
    server = WeightServer("./chunks/bert-tiny", port=8765)
    await server.start_server()

asyncio.run(start_server())

Getting Started

• Installation Guide - Install Surfing Weights
• Quick Start - Start streaming weights in minutes
• Core Concepts - Learn the fundamental concepts

Why Surfing Weights?

Traditional approaches to deploying AI models require downloading and storing the entire model locally. This becomes impractical for:

• Edge devices with limited storage
• Mobile applications where model size impacts app size
• IoT devices with constrained resources
• Environments requiring multiple model variants

Surfing Weights enables these scenarios by:

1. Streaming only the required weights on-demand
2. Intelligently caching frequently used layers
3. Minimizing memory usage and network bandwidth
4. Supporting distributed deployment scenarios

Next Steps

1. Follow the Installation Guide to set up Surfing Weights
2. Try the Quick Start Tutorial
3. Explore Example Use Cases
4. Read the API Documentation