Category: Generative AI

The size and complexity of large language models (LLMs) have exploded in the last few years. LLMs have demonstrated remarkable capabilities in learning the semantics of natural language and producing human-like responses. Many recent LLMs are fine-tuned with a powerful technique called instruction tuning, which helps the model perform new tasks or generate responses to […]

The AI landscape is being reshaped by the rise of generative models capable of synthesizing high-quality data, such as text, images, music, and videos. The course toward democratization of AI helped to further popularize generative AI following the open-source releases for such foundation model families as BERT, T5, GPT, CLIP and, most recently, Stable Diffusion. […]

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions (BERT) to over a trillion parameters (MiCS), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and […]

The fashion industry is a highly lucrative business, with an estimated value of $2.1 trillion by 2025, as reported by the World Bank. This field encompasses a diverse range of segments, such as the creation, manufacture, distribution, and sales of clothing, shoes, and accessories. The industry is in a constant state of change, with new […]

We’re thrilled to announce an expanded collaboration between AWS and Hugging Face to accelerate the training, fine-tuning, and deployment of large language and vision models used to create generative AI applications. Generative AI applications can perform a variety of tasks, including text summarization, answering questions, code generation, image creation, and writing essays and articles. AWS […]

March 2023: This blog was reviewed and updated with AMT HPO support for finetuning text-to-image Stable Diffusion models. In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and […]

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]

Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. JumpStart provides one-click access to a wide variety of pre-trained models for common ML tasks such as object detection, text classification, summarization, text generation […]

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and […]

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Today, we announce a new feature that lets you upscale images (resize images without losing quality) with Stable Diffusion models in JumpStart. An image that is low resolution, blurry, and pixelated can be converted […]