Newly achieved precise control over light emitted from incredibly tiny sources, a few nanometers in size, embedded in two-dimensional (2D) materials could lead to remarkably high-resolution monitors and advances in ultra-fast quantum computing, according to an international team led by researchers at Penn State and Université Paris-Saclay. In a recentContinue Reading
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
There’s a growing demand from customers to incorporate generative AI into their businesses. Many use cases involve using pre-trained large language models (LLMs) through approaches like Retrieval Augmented Generation (RAG). However, for advanced, domain-specific tasks or those requiring specific formats, model customization techniques such as fine-tuning are sometimes necessary. AmazonContinue Reading
Building upon a previous Machine Learning Blog post to create personalized avatars by fine-tuning and hosting the Stable Diffusion 2.1 model at scale using Amazon SageMaker, this post takes the journey a step further. As technology continues to evolve, newer models are emerging, offering higher quality, increased flexibility, and fasterContinue Reading
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
In the rapidly evolving landscape of AI, generative models have emerged as a transformative technology, empowering users to explore new frontiers of creativity and problem-solving. These advanced AI systems have transcended their traditional text-based capabilities, now seamlessly integrating multimodal functionalities that expand their reach into diverse applications. models have becomeContinue Reading
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
Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. In this post, we showcase how to fine-tune a sentence transformerContinue Reading
Have you ever faced the challenge of obtaining high-quality data for fine-tuning your machine learning (ML) models? Generating synthetic data can provide a robust solution, especially when real-world data is scarce or sensitive. For instance, when developing a medical search engine, obtaining a large dataset of real user queries andContinue Reading
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
