Categories: FAANG

The “Super Weight:” How Even a Single Parameter can Determine a Large Language Model’s Behavior

A recent paper from Apple researchers, “The Super Weight in Large Language Models,” reveals that an extremely small subset of parameters in LLMs (in some cases, a single parameter) can exert a disproportionate influence on an LLM’s overall functionality (see Figure 1). This work highlights the critical role of these “super weights” and their corresponding “super activations,” offering a new insight into LLM architecture and avenues for efficient model compression. The paper provides full technical details and experimental results; in this post, we provide a high-level overview of the key…

The Super Weight in Large Language Models

Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters. In this work, we present…

July 3, 2025

In "FAANG"

Scalable Pre-training of Large Autoregressive Image Models

This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model…

February 2, 2024

In "FAANG"

Apple Intelligence Foundation Language Models

We present foundation language models developed to power Apple Intelligence features, including a ∼3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This…

July 30, 2024

In "FAANG"

AI Generated Robotic Content