These local LLMs are changing the game in lots of fun ways.

Not bad for limited hardware ...

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a ...

Methods: We evaluated 17 encoder and decoder models using J-CaseMap, a database of approximately 20,000 Japanese case reports annotated with clinical concepts. Performance was primarily assessed using ...

Abstract: Quantizing the large language model (LLM) in vision-language models (VLMs) is an effective approach to reducing memory size. However, quantizing only the LLM shifts the memory bottleneck to ...

Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an ...

LinkedIn's feed reaches more than 1.3 billion members — and the architecture behind it hadn't kept pace. The system had accumulated five separate retrieval pipelines, each with its own infrastructure ...

Abstract: Improving anomaly detection accuracy in time series data is essential for the operation of communication networks. Encoder-decoder models, which learn the correlations within the data, have ...

Droit, a technology firm at the forefront of computational law and regulation, today announced the launch of Decision Decoder, an AI-powered tool to provide context-aware explanations for decisions ...