finetuned

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

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

How Indeed builds and deploys fine-tuned LLMs on Amazon SageMaker | Amazon Web Services

In: Artificial Intelligence

This post is cowritten with Ethan Handel and Zhiyuan He from Indeed.com. Indeed is the world’s #1 job site¹ and a leading global job matching and hiring marketplace. Our mission is to help people get jobs. At Indeed, we serve over 350 million global Unique Visitors monthly² across more thanContinue Reading

Import a question answering fine-tuned model into Amazon Bedrock as a custom model | Amazon Web Services

In: Artificial Intelligence

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security,Continue Reading

Import a fine-tuned Meta Llama 3 model for SQL query generation on Amazon Bedrock | Amazon Web Services

In: Artificial Intelligence

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. Amazon Bedrock also provides a broad set of capabilities needed to buildContinue Reading

Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker | Amazon Web Services

In: Artificial Intelligence

Retrieval Augmented Generation (RAG) is a popular paradigm that provides additional knowledge to large language models (LLMs) from an external source of data that wasn’t present in their training corpus. RAG provides additional knowledge to the LLM through its input prompt space and its architecture typically consists of the followingContinue Reading

finetuned

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

How Indeed builds and deploys fine-tuned LLMs on Amazon SageMaker | Amazon Web Services

Import a question answering fine-tuned model into Amazon Bedrock as a custom model | Amazon Web Services

Import a fine-tuned Meta Llama 3 model for SQL query generation on Amazon Bedrock | Amazon Web Services

Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker | Amazon Web Services

Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS | Amazon Web Services

InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI | Amazon Web Services