Llama (Page 2)

In: Artificial Intelligence

Generative AI models have seen tremendous growth, offering cutting-edge solutions for text generation, summarization, code generation, and question answering. Despite their versatility, these models often struggle when applied to niche or domain-specific tasks because their pre-training is typically based on large, generalized datasets. To address these gaps and maximize theirContinue Reading

In: Artificial Intelligence

You can now create an end-to-end workflow to train, fine tune, evaluate, register, and deploy generative AI models with the visual designer for Amazon SageMaker Pipelines. SageMaker Pipelines is a serverless workflow orchestration service purpose-built for foundation model operations (FMOps). It accelerates your generative AI journey from prototype to productionContinue Reading

In: Artificial Intelligence

Many organizations are building generative AI applications powered by large language models (LLMs) to boost productivity and build differentiated experiences. These LLMs are large and complex and deploying them requires powerful computing resources and results in high inference costs. For businesses and researchers with limited resources, the high inference costsContinue Reading

In: Artificial Intelligence

Today, we are excited to announce the availability of Llama 3.2 models in Amazon SageMaker JumpStart. Llama 3.2 offers multi-modal vision and lightweight models representing Meta’s latest advancement in large language models (LLMs), providing enhanced capabilities and broader applicability across various use cases. With a focus on responsible innovation andContinue Reading

In: Artificial Intelligence

This post is co-written with Meta’s PyTorch team. In today’s rapidly evolving AI landscape, businesses are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. Although foundation models (FMs) offer impressive out-of-the-box capabilities, true competitive advantage often lies in deep model customization through fine-tuning. However,Continue Reading

In: Artificial Intelligence

Large language models (LLMs) have remarkable capabilities. Nevertheless, using them in customer-facing applications often requires tailoring their responses to align with your organization’s values and brand identity. In this post, we demonstrate how to use direct preference optimization (DPO), a technique that allows you to fine-tune an LLM with humanContinue Reading

In: Artificial Intelligence

Generative artificial intelligence (AI) models have become increasingly popular and powerful, enabling a wide range of applications such as text generation, summarization, question answering, and code generation. However, despite their impressive capabilities, these models often struggle with domain-specific tasks or use cases due to their general training data. To addressContinue Reading