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A practical guide to implementing and leveraging the benefits of Maximal Update Parameterization (μP) for neural network training, offering insights into stable hyperparameters, reduced tuning costs, improved loss at large scales, stable training, and predictable scaling.

Optimizing long sequence fine-tuning with Llama 3.1 for high-quality Retrieval Augmented Generation (RAG) and tool use systems

A practical guide to implementing the Maximal Update Parameterization for neural network training, offering benefits such as stable hyperparameters, reduced tuning costs, and improved training stability at large scales.

Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker

Exploring the broad implementation of artificial intelligence with Genmab's latest launch.

Introducing Databricks Assistant Quick Fix - A feature using AI to automatically correct common SQL and Python errors for enhanced coding efficiency

AI model simplifies structure determination of powdered crystalline materials for various applications

Revolutionizing entrepreneurship with the new AI JetPack for faster idea generation and guidance

Google recognized as a Leader in 2024 Gartner® Magic Quadrant™ for Container Management

Expanded CIEM support in Security Command Center to reduce multicloud risk

Exploring routing options for IP packets from a VM in Google Cloud including system-generated, custom, and policy-based routes.

Data-driven insights for prioritizing application modernization and cloud migration using the Application Rationalization Dashboard