The Extreme LLM Compression Evolution: From QuIP to AQLM With PV-Tuning | HackerNoon
Briefly

from Hackernoon1 year ago
Technological advancements now allow compressing neural networks from 16 to 4 bits, with ongoing research aiming at reducing model size eightfold to 2 bits.
Hackernoonhttps://hackernoon.com/the-extreme-llm-compression-evolution-from-quip-to-aqlm-with-pv-tuning?source=rss
Yandex Research, along with colleagues, introduced a method combining AQLM and PV-Tuning to achieve 8x compression, with their code freely available on GitHub.
Hackernoonhttps://hackernoon.com/the-extreme-llm-compression-evolution-from-quip-to-aqlm-with-pv-tuning?source=rss
Competition between research teams developing compression algorithms like QuIP and AQLM leads to ongoing innovation, breakthroughs, and optimizations in model compression.
Hackernoonhttps://hackernoon.com/the-extreme-llm-compression-evolution-from-quip-to-aqlm-with-pv-tuning?source=rss
Read at Hackernoon
#large-language-models #model-compression #aqlm #neural-networks #technological-advancements
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