Learn how to fine-tune the LLama 3.2 3B Instruct model in this 33-minute video tutorial focused on creating specialized customer service chatbots. Explore the implementation of Qlora and DORA (Weight-Decomposed Low-Rank Adaptation) techniques for efficient model adaptation, with complete access to the accompanying Jupyter notebook containing detailed code examples and implementation steps. Master practical MLOps techniques using MLflow while working with Parameter Efficient Fine Tuning (PEFT) methods to optimize large language models for specific use cases.
MLOps MLflow: Fine-tune LLaMA 3.2 3B with PEFT, QLoRA, and DORA for Customer Service
The Machine Learning Engineer via YouTube
Overview
Syllabus
MlOps Mlflow: Fine tune Llama 3.2 3B, PEFT with QLora and (Dora) #datascience #machinelearning
Taught by
The Machine Learning Engineer
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