An Overview of Apple's LLM Stack
Apple roles out 'Apple Intelligence'
Created on June 11|Last edited on June 11
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At the 2024 Worldwide Developers Conference (WWDC), Apple introduced Apple Intelligence, a new personal intelligence system embedded in iOS 18, iPadOS 18, and macOS Sequoia. This system leverages advanced generative models to enhance user experiences, from writing and summarizing text to creating images and managing notifications.
Models
Apple Intelligence is driven by two key generative models: a 3 billion parameter language model optimized for on-device performance, and a larger server-based model running on Apple silicon servers, available through Private Cloud Compute. These models are designed to provide powerful performance while ensuring user privacy and security.
Two Options
The on-device model is tailored for speed and efficiency on Apple devices, allowing users to write and refine text, prioritize and summarize notifications, generate playful images for conversations, and facilitate in-app actions seamlessly. Meanwhile, the server-based model supports more complex tasks, leveraging the robust infrastructure of Apple's private cloud to deliver high-performance results.
Privacy
Responsible AI development is a cornerstone of Apple Intelligence. The system is built with Apple's core values in mind, focusing on empowering users with intelligent tools, representing users authentically, designing with care to prevent misuse, and protecting user privacy. Apple ensures that no personal data is used in training its models, maintaining strict privacy protections at every stage.
AXLearn
The foundation models are trained using the AXLearn framework, which builds on JAX and XLA to enable scalable training across various hardware platforms. The training process involves using licensed data and publicly available data from AppleBot, with rigorous filtering to exclude sensitive information. This ensures that the models are trained on high-quality data while respecting user privacy.
Post-Training Procedures
Post-training, the models benefit from two advanced algorithms. Rejection sampling fine-tuning involves generating multiple outputs for each input and selecting the one with the highest reward for further training. This method, similar to techniques used in models like LLaMA 2, enhances the model's performance by fine-tuning it with high-quality samples. Additionally, reinforcement learning from human feedback (RLHF) involves fine-tuning the models based on human feedback, ensuring that the outputs align with user expectations. RLHF employs algorithms such as Proximal Policy Optimization (PPO) and rejection sampling to iteratively refine the model.
Other Methods
Apple employs several optimization techniques to enhance model performance, including grouped-query-attention, low-bit palletization, LoRA adapters for specific tasks, and Talaria, a tool for interactive model latency and power analysis. These techniques ensure that the models are highly capable, efficient, and responsive.
Performance
In performance evaluations, Apple's models have shown superior results compared to similar models, excelling in tasks such as summarization, instruction-following, and handling adversarial prompts. The models are preferred for their safety and helpfulness, demonstrating low violation rates in handling harmful content.
Leveraging Apple Silicon
Apple's server infrastructure plays a crucial role in supporting the larger foundation models. The use of Apple silicon servers with Private Cloud Compute allows for powerful performance while maintaining user privacy. This infrastructure is essential for handling complex tasks and providing real-time responses, enhancing the overall user experience across Apple's ecosystem.
In conclusion, the foundation models and adapters introduced at WWDC24 underpin Apple Intelligence, the new personal intelligence system integrated into iPhone, iPad, and Mac. These models enable powerful capabilities across language, images, actions, and personal context. Developed responsibly and guided by Apple's core values, these models aim to help users with everyday activities across their Apple products. Apple looks forward to sharing more information soon on its broader family of generative models, including language, diffusion, and coding models.
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