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Parameter-efficient Fine-tuning of Large Language Models

  • Master Parameter-Efficient Fine Tuning (PEFT) Techniques for Optimizing LLM's Performance
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Explore the power of LLMs without breaking the bank! In this workshop, dive into Parameter-efficient Fine-tuning (PEFT), the key to adapting giants like GPT for your tasks, without their monstrous resource demands. Learn cutting-edge techniques like LoRA, adapters, and prompt tuning to achieve impressive results using just a fraction of the parameters. Master efficient training, resource optimization, and model selection for real-world applications. Leave equipped to unlock the true potential of LLMs, even on limited budgets and hardware. So, join us and tame the computational beast for precise, efficient, and accessible LLM fine-tuning!
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What I will learn?

  • Gain a comprehensive understanding of PEFT techniques and their benefits for LLM adaptation.
  • Master resource-efficient training strategies and deployment options for PEFT models.
  • Develop skills in selecting the right LLM and PEFT approach for specific tasks.
  • Get hands-on experience building and evaluating your own PEFT model on provided datasets.

Course Curriculum

All about Parameter Efficient Fine-Tuning (PEFT) for LLMs

  • Diving into PEFT

LoRA (Low-Rank Adaptation)

QLoRA (Quantized Low-Rank Adaptation)

Video Lesson: PEFT, Its Techniques and Hands-on Implementation

Codes for Practicing PEFT Techniques

Self Assessment


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Material Includes

  • Video Lessons
  • Hands-on codes
  • Handout notes
  • Self assessment


  • Master Parameter-Efficient Fine Tuning (PEFT) Techniques for Optimizing LLM's Performance

Who Should Take this course?

  • Suitable for all data professionals

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