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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

Favorite GPT-J is an open-source 6-billion-parameter model released by Eleuther AI. The model is trained on the Pile and can perform various tasks in language processing. It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis,

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Shared by AWS Machine Learning June 13, 2023

Build custom chatbot applications using OpenChatkit models on Amazon SageMaker

Favorite Open-source large language models (LLMs) have become popular, allowing researchers, developers, and organizations to access these models to foster innovation and experimentation. This encourages collaboration from the open-source community to contribute to developments and improvement of LLMs. Open-source LLMs provide transparency to the model architecture, training process, and training

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Shared by AWS Machine Learning June 13, 2023

Action needed to protect against patent trolls

Favorite The Linux Foundation, Unified Patents and Electronic Frontier Foundation hosted a webinar this week to give an overview of the serious issue of patent trolls and the recent proposal from the United States Patent and Trademark Office (USPTO) to change the current rules for protecting and defending Open Source

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Shared by voicesofopensource June 9, 2023

Exploring Generative AI in conversational experiences: An Introduction with Amazon Lex, Langchain, and SageMaker Jumpstart

Favorite Customers expect quick and efficient service from businesses in today’s fast-paced world. But providing excellent customer service can be significantly challenging when the volume of inquiries outpaces the human resources employed to address them. However, businesses can meet this challenge while providing personalized and efficient customer service with the

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Shared by AWS Machine Learning June 9, 2023

Get started with the open-source Amazon SageMaker Distribution

Favorite Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet. To improve this

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Shared by AWS Machine Learning June 9, 2023