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Scaling Laws for Native Multimodal Models

16/04/2025

Apple Machine Learning Research Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs) - those trained from the ground up on all modalities - and conduct an extensive… Go to Source 16/04/2025 - 21:05 / SoMe: @hoffeldt.bsky.social

Artificial Intelligence

EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing

16/04/2025

Apple Machine Learning Research Diffusion transformers have been widely adopted for text-to-image synthesis. While scaling these models up to billions of parameters shows promise, the effectiveness of scaling beyond current sizes remains underexplored and challenging. By explicitly exploiting the computational heterogeneity of image generations, we develop a new family of Mixture-of-Experts (MoE) models (EC-DIT) for diffusion transformers with expert-choice routing. EC-DIT learns to adaptively optimize the compute allocated to understand the input texts and generate the respective image patches, enabling heterogeneous… Go to Source 16/04/2025 - 21:05 / SoMe: @hoffeldt.bsky.social

Artificial Intelligence

Host concurrent LLMs with LoRAX

16/04/2025

AWS Machine Learning Blog Businesses are increasingly seeking domain-adapted and specialized foundation models (FMs) to meet specific needs in areas such as document summarization, industry-specific adaptations, and technical code generation and advisory. The increased usage of generative AI models has offered tailored experiences with minimal technical expertise, and organizations are increasingly using these powerful models to drive innovation and enhance their services across various domains, from natural language processing (NLP) to content generation. However, using generative AI models in enterprise environments presents unique challenges. Out-of-the-box models often lack the specific knowledge required for certain domains or organizational terminologies. To address this, businesses are turning to custom fine-tuned models, also known as domain-specific large language models (LLMs). These models are tailored to perform specialized tasks within specific domains or micro-domains. Similarly, organizations are fine-tuning generative AI models for domains such as finance, sales, marketing, travel, IT, human resources (HR), procurement, healthcare and life sciences, and customer service. Independent software vendors (ISVs) are also building secure, managed, multi-tenant generative AI platforms. As the demand for personalized and specialized AI solutions grows, businesses face the challenge of efficiently managing and serving a multitude of fine-tuned models across diverse use cases and customer segments. From résumé parsing and job skill matching to domain-specific email generation and natural language understanding, companies often grapple with managing hundreds of fine-tuned models tailored to specific needs. This challenge is further compounded by concerns over scalability and cost-effectiveness. Traditional model serving approaches can become unwieldy and resource-intensive, leading to increased infrastructure costs, operational overhead, and potential performance bottlenecks, due to the size and hardware requirements to maintain a high-performing FM. The following diagram represents a traditional approach to serving multiple LLMs. Fine-tuning LLMs is prohibitively expensive due to the hardware requirements and the costs associated with hosting separate instances...

Artificial Intelligence

Build a computer vision-based asset inventory application with low or no training

16/04/2025

AWS Machine Learning Blog Keeping an up-to-date asset inventory with real devices deployed in the field can be a challenging and time-consuming task. Many electricity providers use manufacturer’s labels as key information to link their physical assets within asset inventory systems. Computer vision can be a viable solution to speed up operator inspections and reduce human errors by automatically extracting relevant data from the label. However, building a standard computer vision application capable of managing hundreds of different types of labels can be a complex and time-consuming endeavor. In this post, we present a solution using generative AI and large language models (LLMs) to alleviate the time-consuming and labor-intensive tasks required to build a computer vision application, enabling you to immediately start taking pictures of your asset labels and extract the necessary information to update the inventory using AWS services like AWS Lambda, Amazon Bedrock, Amazon Titan, Anthropic’s Claude 3 on Amazon Bedrock, Amazon API Gateway, AWS Amplify, Amazon Simple Storage Service (Amazon S3), and Amazon DynamoDB. LLMs are large deep learning models that are pre-trained on vast amounts of data. They are capable of understanding and generating human-like text, making them incredibly versatile tools with a wide range of applications. This approach harnesses the image understanding capabilities of Anthropic’s Claude 3 model to extract information directly from photographs taken on-site, by analyzing the labels present in those field images. Solution overview The AI-powered asset inventory labeling solution aims to streamline the process of updating inventory databases by automatically extracting relevant information from asset labels through computer vision and generative AI capabilities. The solution uses various AWS services to create an end-to-end system that enables field technicians to capture label images, extract data using AI models, verify the accuracy, and seamlessly update the inventory database. The following diagram illustrates the...

Artificial Intelligence

Host concurrent LLMs with LoRAX

16/04/2025

Artificial Intelligence

Build a computer vision-based asset inventory application with low or no training

16/04/2025