Learn how to build and deploy a custom sentiment analysis model for the web using PyTorch and Google's LiteRT! This guide walks you through the process of creating a model from scratch, training it on a YouTube comments dataset, converting it to a browser-friendly format, and running it in the browser with TensorFlow Lite and the Google Gen AI JavaScript library. Perfect for web developers looking to leverage custom AI models.
Understand how temperature affects Large Language Model (LLM) output. Learn how temperature parameter changes probability distribution of next-token predictions, impacting creativity and predictability. Explore a visualization tool demonstrating the effects of temperature and top-k parameters on LLM responses.
Learn Python for AI development: This hands-on guide details a practical approach to mastering Python, focusing on effective learning techniques and addressing the impact of AI-powered code completion tools on the learning process. Discover how to balance AI assistance with focused practice for optimal skill acquisition in Python programming for AI projects.
Troubleshoot creating composite indexes with vector embeddings in Firestore on Windows. This solution uses a JSON file to define the index, bypassing Powershell JSON escaping issues, and provides the corrected `gcloud` command for successful index creation. Learn how to create a functional composite index with vector embeddings.
Quickly count tokens in Large Language Models (LLMs) like Gemini and Gemma using Rust. This efficient method avoids slow network calls, leveraging the `tokenizers` crate for local processing. The code example demonstrates token counting with minimal dependencies, even handling `aarch64` architecture challenges. Get started with fast, local token counting now!