Saw this paper: https://arxiv.org/pdf/2406.02528
In essence, MatMul operations can be completely eliminated from LLMs while maintaining strong performance at billion-parameter scales and by utilising an optimised kernel during inference, their model’s memory consumption can be reduced by more than 10× compared to un-optimised models.
source: https://x.com/rohanpaul_ai/status/1799122826114330866
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I came across two interesting papers recently on scaling laws in AI and wanted to share a summary. Here are the key takeaways:
Scaling LLM Test-Time Compute Two papers looked at how to scale up test-time compute for LLMs:
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Here is everything you need to know in 10 points:
Liquid AI has launched a new type of AI model called Liquid Foundation Models (LFMs). These models are designed to be more efficient than traditional transformer-based models, with smaller memory needs and better performance. LFMs come i...
