On Device Llama 3.1 with Core ML

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user’s device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy. In order …

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Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI, allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in …

Team introduces a cost-effective method to redesign search engines for AI

The internet search engine of the future will be powered by artificial intelligence. One can already choose from a host of AI-powered or AI-enhanced search engines—though their reliability often still leaves much to be desired. However, a team of computer scientists at the University of Massachusetts Amherst recently published and released a novel system for …