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BEGIN:VEVENT
SUMMARY:Introduction to Fully Sharded Data Parallel (FSDP)
DTSTART;VALUE=DATE-TIME:20260717T101000Z
DTEND;VALUE=DATE-TIME:20260717T104000Z
DTSTAMP;VALUE=DATE-TIME:20260819T235922Z
UID:indico-contribution-216-1186@events.grnet.gr
DESCRIPTION:Speakers: Nikos  Bakas (GRNET)\, Roman  Dolgopolyi (GRNET)\nht
 tps://events.grnet.gr/event/216/contributions/1186/
LOCATION:
URL:https://events.grnet.gr/event/216/contributions/1186/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fine-Tuning Transformers for Medical Reasoning with LoRA and Huggi
 ng Face Trainer
DTSTART;VALUE=DATE-TIME:20260717T090000Z
DTEND;VALUE=DATE-TIME:20260717T100000Z
DTSTAMP;VALUE=DATE-TIME:20260819T235922Z
UID:indico-contribution-216-1175@events.grnet.gr
DESCRIPTION:Speakers: Roman  Dolgopolyi (GRNET)\nThis talk demonstrates a 
 practical\, end-to-end notebook for fine-tuning a reasoning-capable transf
 ormer model for medical question answering. Participants will learn how to
  use Hugging Face Datasets and Trainer to handle the training workflow\, f
 rom loading and cleaning data to tokenization\, checkpointing\, evaluation
 \, and inference. The session demonstrates parameter-efficient fine-tuning
  with LoRA\, showing how a 3B-class Mistral reasoning model can be adapted
  on a single 16 GB GPU by training only small adapter weights instead of t
 he full model. The notebook combines MedReason and medical-o1 reasoning da
 tasets into a unified question\, chain-of-thought\, and answer format\, th
 en trains and evaluates the model on a small demo subset. By the end\, att
 endees will understand the key engineering choices behind efficient LLM fi
 ne-tuning and see a side-by-side comparison of base and fine-tuned model b
 ehavior on medical reasoning tasks\, including practical notes on GPU setu
 p\, mixed precision\, and resource cleanup for reproducible classroom demo
 s.\n\nhttps://events.grnet.gr/event/216/contributions/1175/
LOCATION:
URL:https://events.grnet.gr/event/216/contributions/1175/
END:VEVENT
BEGIN:VEVENT
SUMMARY:The Pharos training platform
DTSTART;VALUE=DATE-TIME:20260717T104000Z
DTEND;VALUE=DATE-TIME:20260717T110000Z
DTSTAMP;VALUE=DATE-TIME:20260819T235922Z
UID:indico-contribution-216-1176@events.grnet.gr
DESCRIPTION:Speakers: Margarita  Markoulatou (GRNET)\nIn this talk\, we wi
 ll briefly navigate you through our recently launched Pharos Training Plat
 form. You may navigate various talks and hands-on material on Machine Lear
 ning\, Deep Learning\, NLP\, LLMs\, Computer Vision\, Advanced GenAI\, HPC
 \, AI Ethics\, AI4Health\, AI4Sustainability\, and AI4LanguageCulture: htt
 ps://www.pharos-aifactory.eu/pharos-training-platform/\n\nhttps://events.g
 rnet.gr/event/216/contributions/1176/
LOCATION:
URL:https://events.grnet.gr/event/216/contributions/1176/
END:VEVENT
BEGIN:VEVENT
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:20260819T235922Z
UID:indico-contribution-216-1161@events.grnet.gr
DESCRIPTION:Speakers: Constantine  Dovrolis (The Cyprus Institute)\nThis p
 resentation reviews the main algorithmic and systems-level ideas for makin
 g large-scale generative AI more efficient across the full model lifecycle
 : pre-training\, fine-tuning\, and inference. It introduces the computatio
 nal challenges created by scaling laws\, model size\, memory footprint\, a
 nd long-context generation\, then surveys model-centric approaches such as
  quantization\, pruning\, low-rank approximation\, knowledge distillation\
 , mixed-precision training\, sparse initialization\, and parameter-efficie
 nt fine-tuning methods including adapters\, prompt tuning\, and LoRA-style
  techniques. The presentation also covers inference-time acceleration\, in
 cluding speculative decoding\, KV-cache optimization\, efficient attention
  variants\, mixture-of-experts architectures\, and long-context methods. T
 he talk is intended for audiences interested in deploying\, adapting\, or 
 studying large-scale ML models under realistic compute and cost constraint
 s.\n\nhttps://events.grnet.gr/event/216/contributions/1161/
LOCATION:
URL:https://events.grnet.gr/event/216/contributions/1161/
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