Training & Benchmarking Adapters for the Apple Foundation Model Framework with Datawizz скачать в хорошем качестве

Training & Benchmarking Adapters for the Apple Foundation Model Framework with Datawizz

Apple just announced a major AI update in iOS 26, macOS 26, and iPadOS 26 — giving developers access to local, offline Foundation Models for on-device inference. This means you can now build AI features that run entirely on-device, with no cloud, preserving user privacy and reducing latency and costs. But that’s just the beginning. In this video, we: Explore how Apple’s Foundation Models Framework works for local inference Benchmark Apple’s model against competitors like Llama 3B, Phi-3 Mini, and Gemma 2B Introduce adapters — lightweight, task-specific layers you can train and load on top of Apple’s models Show how adapters drastically boost performance — in some tasks outperforming even GPT-4 Walk through training and deploying a custom adapter using Datawizz, our platform for building and evaluating efficient AI models Whether you’re building AI-powered apps for iOS or just curious about the future of offline-first AI, this video will walk you through: How to get started with Apple’s Foundation Models What adapters are and how they work Why adapters are a game-changer for AI on the edge How to train and evaluate adapters with real logs from your app using Datawizz How to deploy your adapter in your Swift app We believe the future of AI is offline-first — where intelligence lives directly on your devices, not in the cloud. And with tools like Apple Foundation Models + Adapters and platforms like Datawizz, that future is already here. 👉 Watch till the end to see how to fine-tune your own adapter and deploy it to your iOS app. 🔗 Learn more about Datawizz: https://datawizz.ai #appleintelligence #ios26 #ios26beta #ai #edgeai #foundationmodels #llm #slm #finetuning #swift #appledeveloper #machinelearning #benchmark