Learn how to implement self-instruct fine-tuning for Large Language Models through a 25-minute technical video that breaks down the ALPACA code implementation. Explore the PyTorch-based approach for fine-tuning LLMs using instruction-tuned datasets, enabling parallel processing of multiple tasks. Discover the self-instruct methodology for generating synthetic datasets using ChatGPT, GPT-4, or other LLMs to create task-specific training data for applications like summarization, translation, and question-answering. Gain insights into Stanford's ALPACA project and understand the theoretical foundations presented in the self-instruct research paper while learning to adapt these techniques for custom LLM fine-tuning projects.
Overview
Syllabus
The ALPACA Code explained: Self-instruct fine-tuning of LLMs
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