With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.
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Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding
Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
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A marriage of formal methods and LLMs seeks to harness the strengths of both.
The field of artificial intelligence has reached a point where simply adding more data or increasing the size of a model is not the best way to make it more intelligent. For the past few years, we ...
ABSTRACT: This paper undertakes a foundational inquiry into logical inferentialism with particular emphasis on the normative standards it establishes and the implications these pose for classical ...
Thank you again for your great work. I am trying to use a diverse text prompt, but it gives me a meaningful prediction, which is right lung masks from the first example below. image_path = ...
French AI darling Mistral is keeping the new releases coming this summer. Just days after announcing its own domestic AI-optimized cloud service Mistral Compute, the well-funded company has released ...
During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) ...
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