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

Build a conversational chatbot using different LLMs within single interface – Part 1

By Admin 27/06/2024

AWS Machine Learning Blog With the advent of generative artificial intelligence (AI), foundation models (FMs) can generate content such as answering questions, summarizing text, and providing highlights from the sourced document. However, for model selection, there is a wide choice from model providers, like Amazon, Anthropic, AI21 Labs, Cohere, and Meta, coupled with discrete real-world […]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

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

Create a multimodal assistant with advanced RAG and Amazon Bedrock

By Admin 21/05/2024

AWS Machine Learning Blog Retrieval Augmented Generation (RAG) models have emerged as a promising approach to enhance the capabilities of language models by incorporating external knowledge from large text corpora. However, despite their impressive performance in various natural language processing tasks, RAG models still face several limitations that need to be addressed. Naive RAG models […]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

Simple guide to training Llama 2 with AWS Trainium on Amazon SageMaker

By Admin 01/05/2024

AWS Machine Learning Blog Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Their impressive generative abilities have led to widespread adoption across various sectors and use cases, including content generation, sentiment analysis, chatbot development, and virtual assistant technology. Llama2 by Meta is an example of an LLM […]Continue reading