Nico Martin - From ML to LLM: On-device AI in the Browser

#1about 2 minutes

Using machine learning to detect verbal filler words

A personal project to detect and count filler words in Swiss German speech highlights the limitations of standard speech-to-text APIs.

#2about 2 minutes

Comparing TensorFlow.js backends for performance

TensorFlow.js performance depends on the chosen backend, with WebGPU offering significant speed improvements over CPU, WebAssembly, and WebGL.

#3about 2 minutes

Real-time face landmark detection with WebGPU

A live demo showcases how the WebGPU backend in TensorFlow.js achieves 30 frames per second for face detection, far outpacing CPU and WebGL.

#4about 1 minute

Building a browser extension for gesture control

A Chrome extension uses a hand landmark detection model to enable website navigation and interaction through pinch gestures.

#5about 2 minutes

Training a custom speech model with Teachable Machine

Teachable Machine provides a no-code interface to train a custom speech command model directly in the browser for recognizing specific words.

#6about 2 minutes

The technical challenges of running LLMs in browsers

To run LLMs on-device, we must understand their internal workings, from tokenizers that convert text to numbers to the massive model weights.

#7about 2 minutes

Reducing LLM size for browser use with quantization

Quantization is a key technique for reducing the file size of LLM weights by using lower-precision numbers, making them feasible for browser deployment.

#8about 2 minutes

Running on-device models with the WebLLM library

The WebLLM library, powered by Apache TVM, simplifies the process of loading and running quantized LLMs directly within a web application.

#9about 2 minutes

A live demo of on-device text generation

A markdown editor demonstrates fast, local text generation using the Gemma 2B model, with all processing happening in the browser without cloud requests.

#10about 1 minute

Mitigating LLM hallucinations with RAG

Retrieval-Augmented Generation (RAG) improves LLM accuracy by providing relevant source documents alongside the user's prompt to ground the response in facts.

#11about 3 minutes

Building an on-device RAG solution for PDFs

A demo application shows how to implement a fully client-side RAG system that processes a PDF and uses vector embeddings to answer questions.

#12about 1 minute

Forcing an LLM to admit when it doesn't know

By instructing the model to only use the provided context, a RAG system can reliably respond that it doesn't know the answer if it's not in the source document.

#13about 2 minutes

The future of on-device AI hardware and APIs

The performance of on-device AI is heavily hardware-dependent, but future improvements in chips (NPUs) and browser APIs like WebNN will broaden access.

#14about 2 minutes

Key benefits of running AI in the browser

Browser-based AI offers significant advantages including privacy by default, zero installation, high interactivity, and infinite scalability since users provide the compute.

3 days ago

Machine Learning Engineer