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UID:pretalx-cfp-7KTV9P@seattle2023.pydata.org
DTSTART;TZID=America/Los_Angeles:20230428T150000
DTEND;TZID=America/Los_Angeles:20230428T154500
DESCRIPTION:Are you interested in learning about the emerging open source s
 tack for Large Language Models (LLMs)?\n\nLLMs have gained immense popular
 ity in recent months and require scalable solutions to overcome challenges
  they present in terms of data ingestion\, training\, fine-tuning\, batch 
 (offline) inference\, and online serving. However\, LLM-type workloads sha
 re some common challenges with other types of large scale ML use cases.\n\
 nLet’s explore the current state of Generative AI and LLMs and have a cl
 oser look at the emerging (yet still early) open source tech stack for thi
 s workload. Then we will evaluate how Ray AI Runtime provides a scalable c
 ompute substrate\, addressing orchestration and scalability problems.\n\nF
 inally\, we will demonstrate how you can implement distributed fine-tuning
  and batch (offline) inference with HuggingFace and Ray AI Runtime\, using
  recent Google’s Flan-T5 model and Alpaca dataset.
DTSTAMP:20250709T220059Z
LOCATION:St. Helens
SUMMARY:Emerging Open Source Tech Stack for Large Language Models (LLMs) wi
 th Ray AI Runtime - Kamil Kaczmarek
URL:https://seattle2023.pydata.org/cfp/talk/7KTV9P/
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