Category: Amazon SageMaker JumpStart

Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a collection of pre-trained and fine-tuned code generation models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned Code Llama models provide better accuracy […]

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing some key challenges, including quality output, data privacy, security, integration with organization data, cost, and skills to deliver. In this post, we explore different approaches […]

Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on Google’s Gemini models, trained on up to 6 trillion tokens of text. The Gemma family consists of two sizes: a 7 billion parameter model and a 2 billion parameter model. Now, […]

Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code and natural language prompts. […]

One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of reference content related to an input prompt by performing similarity searches on embeddings. Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with […]

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a single token, and throughput, defined by the number of tokens generated per second. Although a single request to the deployed endpoint would exhibit a throughput […]

Today we are excited to announce that the Llama Guard model is now available for customers using Amazon SageMaker JumpStart. Llama Guard provides input and output safeguards in large language model (LLM) deployment. It’s one of the components under Purple Llama, Meta’s initiative featuring open trust and safety tools and evaluations to help developers build […]

This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production […]

Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. In November […]

The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can use this technology to create new products and services on AWS, such as enterprise chatbots, which are more conversational. This post shows you how you can create a web UI, which […]