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SUMMARY:Efficient training\, fine-tuning and inference of large-scale ML m
 odels
DTSTART;VALUE=DATE-TIME:20260717T080000Z
DTEND;VALUE=DATE-TIME:20260717T090000Z
DTSTAMP;VALUE=DATE-TIME:20260703T161443Z
UID:indico-contribution-1161@events.grnet.gr
DESCRIPTION:Speakers: Constantine  Dovrolis (The Cyprus Institute)\nThis t
 alk presents model-centric methods for efficient generative AI. It explain
 s why training and inference of LLMs are computationally heavy\, then cove
 rs model compression methods such as quantization\, neural network pruning
 \, low-rank approximations\, and knowledge distillation. It also introduce
 s efficient pre-training with mixed-precision acceleration and PHEW\, para
 meter-efficient fine-tuning methods such as LLM-Adapters\, LLaMA-Adapter\,
  P-Tuning\, and LoraHub\, and efficient inference techniques including spe
 culative decoding and KV-cache optimization.\n\nhttps://events.grnet.gr/ev
 ent/216/contributions/1161/
LOCATION:
URL:https://events.grnet.gr/event/216/contributions/1161/
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