language

Fine-tune large language models with reinforcement learning from human or AI feedback | Amazon Web Services

Large language models (LLMs) can be used to perform natural language processing (NLP) tasks ranging from simple dialogues and information retrieval tasks, to more complex reasoning tasks such as summarization and decision-making. Prompt engineering and supervised fine-tuning, which use instructions and examples demonstrating the desired task, can make LLMs betterContinue Reading

Using Large Language Models on Amazon Bedrock for multi-step task execution | Amazon Web Services

In: Artificial Intelligence

The goal of this blog post is to show you how a large language model (LLM) can be used to perform tasks that require multi-step dynamic reasoning and execution. Examples of tasks that require dynamic reasoning and execution are answering questions of the form “What is the average length ofContinue Reading

Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock | Amazon Web Services

In: Artificial Intelligence

In this post, we discuss what embeddings are, show how to practically use language embeddings, and explore how to use them to add functionality such as zero-shot classification and semantic search. We then use Amazon Bedrock and language embeddings to add these features to a really simple syndication (RSS) aggregatorContinue Reading

Evaluate large language models for your machine translation tasks on AWS | Amazon Web Services

In: Artificial Intelligence

Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. LLMs particularly stand out for their natural ability to learn from the context of the input text, which allowsContinue Reading

How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model | Amazon Web Services

In: Artificial Intelligence

This post is co-written with Marta Cavalleri and Giovanni Germani from Fastweb, and Claudia Sacco and Andrea Policarpi from BIP xTech. AI’s transformative impact extends throughout the modern business landscape, with telecommunications emerging as a key area of innovation. Fastweb, one of Italy’s leading telecommunications operators, recognized the immense potentialContinue Reading

Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights | Amazon Web Services

In: Artificial Intelligence

Many enterprise customers across various industries are looking to adopt Generative AI to drive innovation, user productivity, and enhance customer experience. Generative AI–powered assistants such as Amazon Q Business can be configured to answer questions, provide summaries, generate content, and securely complete tasks based on data and information in yourContinue Reading

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 | Amazon Web Services

In: Artificial Intelligence

In Part 1 of this series, we introduced Amazon SageMaker Fast Model Loader, a new capability in Amazon SageMaker that significantly reduces the time required to deploy and scale large language models (LLMs) for inference. We discussed how this innovation addresses one of the major bottlenecks in LLM deployment: the timeContinue Reading

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 | Amazon Web Services

In: Artificial Intelligence

The generative AI landscape has been rapidly evolving, with large language models (LLMs) at the forefront of this transformation. These models have grown exponentially in size and complexity, with some now containing hundreds of billions of parameters and requiring hundreds of gigabytes of memory. As LLMs continue to expand, AIContinue Reading

Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents | Amazon Web Services

In: Artificial Intelligence

Hallucinations in large language models (LLMs) refer to the phenomenon where the LLM generates an output that is plausible but factually incorrect or made-up. This can occur when the model’s training data lacks the necessary information or when the model attempts to generate coherent responses by making logical inferences beyondContinue Reading

Fine-tune large language models with Amazon SageMaker Autopilot | Amazon Web Services

In: Artificial Intelligence

Fine-tuning foundation models (FMs) is a process that involves exposing a pre-trained FM to task-specific data and fine-tuning its parameters. It can then develop a deeper understanding and produce more accurate and relevant outputs for that particular domain. In this post, we show how to use an Amazon SageMaker AutopilotContinue Reading

language

Fine-tune large language models with reinforcement learning from human or AI feedback | Amazon Web Services

Using Large Language Models on Amazon Bedrock for multi-step task execution | Amazon Web Services

Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock | Amazon Web Services

Evaluate large language models for your machine translation tasks on AWS | Amazon Web Services

How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model | Amazon Web Services

Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights | Amazon Web Services

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2 | Amazon Web Services

Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1 | Amazon Web Services

Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents | Amazon Web Services

Fine-tune large language models with Amazon SageMaker Autopilot | Amazon Web Services

How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference | Amazon Web Services

Build an enterprise synthetic data strategy using Amazon Bedrock | Amazon Web Services