Secure RAG applications using prompt engineering on Amazon Bedrock

By Admin 27/08/2024

AWS Machine Learning Blog The proliferation of large language models (LLMs) in enterprise IT environments presents new challenges and opportunities in security, responsible artificial intelligence (AI), privacy, and prompt engineering. The risks associated with LLM use, such as biased outputs, privacy breaches, and security vulnerabilities, must be mitigated. To address these challenges, organizations must proactively […]Continue reading

Mistral Large 2 is now available in Amazon Bedrock

By Admin 26/07/2024

AWS Machine Learning Blog Mistral AI’s Mistral Large 2 (24.07) foundation model (FM) is now generally available in Amazon Bedrock. Mistral Large 2 is the newest version of Mistral Large, and according to Mistral AI offers significant improvements across multilingual capabilities, math, reasoning, coding, and much more. In this post, we discuss the benefits and […]Continue reading

Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker

By Admin 31/05/2024

AWS Machine Learning Blog Genomic language models are a new and exciting field in the application of large language models to challenges in genomics. In this blog post and open source project, we show you how you can pre-train a genomics language model, HyenaDNA, using your genomic data in the AWS Cloud. Here, we use […]Continue reading

End-to-end LLM training on instance clusters with over 100 nodes using AWS Trainium

By Admin 29/05/2024

AWS Machine Learning Blog Llama is Meta AI’s large language model (LLM), with variants ranging from 7 billion to 70 billion parameters. Llama uses a transformers-based decoder-only model architecture, which specializes at language token generation. To train a model from scratch, a dataset containing trillions of tokens is required. The Llama family is one of […]Continue reading

Accelerate Mixtral 8x7B pre-training with expert parallelism on Amazon SageMaker

By Admin 23/05/2024

AWS Machine Learning Blog Mixture of Experts (MoE) architectures for large language models (LLMs) have recently gained popularity due to their ability to increase model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while […]Continue reading

Evaluation of generative AI techniques for clinical report summarization

By Admin 13/05/2024

AWS Machine Learning Blog In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Since then, Amazon Web Services (AWS) has introduced new services such as Amazon Bedrock. This is a fully managed service […]Continue reading

Transform customer engagement with no-code LLM fine-tuning using Amazon SageMaker Canvas and SageMaker JumpStart

By Admin 10/05/2024

AWS Machine Learning Blog Fine-tuning large language models (LLMs) creates tailored customer experiences that align with a brand’s unique voice. Amazon SageMaker Canvas and Amazon SageMaker JumpStart democratize this process, offering no-code solutions and pre-trained models that enable businesses to fine-tune LLMs without deep technical expertise, helping organizations move faster with fewer technical resources. SageMaker […]Continue reading

Information extraction with LLMs using Amazon SageMaker JumpStart

By Admin 07/05/2024

AWS Machine Learning Blog Large language models (LLMs) have unlocked new possibilities for extracting information from unstructured text data. Although much of the current excitement is around LLMs for generative AI tasks, many of the key use cases that you might want to solve have not fundamentally changed. Tasks such as routing support tickets, recognizing […]Continue reading

Revolutionize Customer Satisfaction with tailored reward models for your business on Amazon SageMaker

By Admin 02/05/2024

AWS Machine Learning Blog As more powerful large language models (LLMs) are used to perform a variety of tasks with greater accuracy, the number of applications and services that are being built with generative artificial intelligence (AI) is also growing. With great power comes responsibility, and organizations want to make sure that these LLMs produce […]Continue reading

Natural language boosts LLM performance in coding, planning, and robotics

By Admin 01/05/2024

MIT News – Artificial intelligence Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of […]Continue reading