EPISODE · Jun 4, 2025 · 27 MIN
Fine-Tuning Large Language Models: A Comprehensive Guide
from Neural intel Pod · host Neuralintel.org
This podcast offers a comprehensive overview of fine-tuning large language models (LLMs), exploring both foundational principles and advanced techniques. It details a seven-stage pipeline for fine-tuning, covering everything from initial data preparation and model initialization to training setup, evaluation, deployment, and ongoing monitoring and maintenance. The text also discusses various parameter-efficient fine-tuning (PEFT) methods and contrasts approaches like Retrieval-Augmented Generation (RAG) with fine-tuning for different use cases. Furthermore, it addresses the integration of LLMs with multimodal data, including vision and audio, and highlights key open challenges and research directions in the field.
What this episode covers
This podcast offers a comprehensive overview of fine-tuning large language models (LLMs), exploring both foundational principles and advanced techniques. It details a seven-stage pipeline for fine-tuning, covering everything from initial data preparation and model initialization to training setup, evaluation, deployment, and ongoing monitoring and maintenance. The text also discusses various parameter-efficient fine-tuning (PEFT) methods and contrasts approaches like Retrieval-Augmented Generation (RAG) with fine-tuning for different use cases. Furthermore, it addresses the integration of LLMs with multimodal data, including vision and audio, and highlights key open challenges and research directions in the field.
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Fine-Tuning Large Language Models: A Comprehensive Guide
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