AI_Distilled #39: Unpacking Mistral Large, Google's Gemini Challenges, and Copilot Enterprise

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Print to Pixel: Optimize your learning experience with Packt

Several research studies have proven that printed books enhance comprehension, with the tactile experience of flipping pages and annotating the margins adding depth to the learning experience. However, developers can't overlook the practical benefits of eBooks, such as quickly finding relevant information or carrying an entire library on a single device.

Acknowledging the unique benefits of both formats, Packt is offering a Researchers at Microsoft have developed new techniques to improve visual language models

We’ve also got you your fresh dose of GPT and LLM secret knowledge and tutorials:

Monitoring Large Language Models on AWS

Last but not least, don’t miss out on the hands-on strategies and tips straight from the AI community for you to use on your own projects:

Leveraging Notion, Supabase and AI for Knowledge Retrieval

Writer’s Credit: Special shout-out to Vidhu Jain for her valuable contribution to this week’s issue.

Cheers,

Kartikey Pandey

Editor-in-Chief, Packt

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Breaking Down How Large Language Models Learn: This article provides a helpful breakdown of how LLMs are trained through causal language modeling and calculates loss. It visually explains how models generate text sequences, are pre-trained to predict the next token, and how cross-entropy loss compares predictions to true labels to update weights. The process is demonstrated through code showing how loss is manually calculated for an LLM matching the framework's automatic calculation. This gives developers valuable insights into how state-of-the-art models learn.

🌀 Make Conversation Come Alive - Deploying Your Own AI Chat Partner: Tired of boring chatbots? This guide shows you how to bring the amazing Qwen AI model to your own server so you can have engaging discussions on any topic. The steps cover setting up your environment, installing dependencies, initializing the tokenizer and model, and using history to keep conversations flowing naturally. Once complete, you'll have a powerful AI assistant right at your fingertips. Best of all, it's completely open source.

🌀 facebookresearch/pearl: This open-source library provides a modular reinforcement learning framework for building and training production-ready AI agents, empowering developers with state-of-the-art techniques.

🌀 google/gemma.cpp: Provides a lightweight C++ library for running Google's Gemma models that developers can easily integrate into their own projects for experimenting with and deploying LLMs.