*Equal Contributors A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be pre-trained. A de facto standard is to pre-train the vision …
Read more “Multimodal Autoregressive Pre-Training of Large Vision Encoders”
Companies across various scales and industries are using large language models (LLMs) to develop generative AI applications that provide innovative experiences for customers and employees. However, building or fine-tuning these pre-trained LLMs on extensive datasets demands substantial computational resources and engineering effort. With the increase in sizes of these pre-trained LLMs, the model customization process …
Read more “Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA”
In the domain of software development, AI-driven assistance is emerging as a transformative force to enhance developer experience and productivity and ultimately optimize overall software delivery performance. Many organizations started to leverage AI-based assistants, such as Gemini Code Assist, in developer IDEs to support them in solving more difficult problems, understanding unfamiliar code, generating test …
Read more “Boost your Continuous Delivery pipeline with Generative AI”
Our new AI system accurately identifies errors inside quantum computers, helping to make this new technology more reliable.
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population …
Read more “Instance-Optimal Private Density Estimation in the Wasserstein Distance”
Swiss Re & Palantir Scaling Data Operations with Foundry Editor’s note: This guest post is authored by our customer, Swiss Re. Authors Lukasz Lewandowski, Marco Lotz, and Jarek Sobanski lead the core technical team responsible for the implementation of Palantir Foundry at the Swiss reinsurer. They have been managing overall platform operations, core architectural principles, site reliability, …
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As generative AI models advance in creating multimedia content, the difference between good and great output often lies in the details that only human feedback can capture. Audio and video segmentation provides a structured way to gather this detailed feedback, allowing models to learn through reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT). …
Large language models (LLMs) give developers immense power and scalability, but managing resource consumption is key to delivering a smooth user experience. LLMs demand significant computational resources, which means it’s essential to anticipate and handle potential resource exhaustion. If not, you might encounter 429 “resource exhaustion” errors, which can disrupt how users interact with your …
Read more “Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors”
In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. In this post, we explore how you can use Amazon …
Your next big customer doesn’t speak your language. In fact, 40% of global consumers won’t even consider buying from websites not in their native tongue. With 51.6% of internet users speaking languages other than English, you’re potentially missing half your market. Until now, enterprises faced an impossible choice in addressing translation use cases. They had …
Read more “Announcing new updates to Cloud Translation AI, now covering 189 languages”