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In this sequel to my previous video ( • Summarizing legal docu... ), I delve into optimizing the fine-tuning of a Google FLAN-T5 model for legal text summarization. The focus is on employing QLoRA for parameter-efficient fine-tuning. All it takes is a few extra lines of simple code in your existing script.
This methodology allows us to train the model with remarkable cost efficiency, utilizing even modest GPU instances, which I demonstrate on AWS with Amazon SageMaker. Tune in for a detailed exploration of the technical nuances behind this process.
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- Original model: huggingface.co...
- LoRA model: huggingface.co...
- Dataset: huggingface.co...
- Notebook: gitlab.com/jul...
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