Uploaded October 2023 | Updated September 2026, 2 weeks ago
In this sequel to my previous video (youtu.be/tc87-ZKWm78), 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/google/flan-t5-large
- LoRA model: huggingface.co/juliensimon/flan-t5-large-billsum-qlora
- Dataset: huggingface.co/datasets/billsum
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/summarization-t5-qlora
Follow me on Medium at julsimon.medium.com or Substack at https://julsimon.substack.com.
In this sequel to my previous video (youtu.be/tc87-ZKWm78), 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.
⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos ⭐️⭐️⭐️
- Original model: huggingface.co/google/flan-t5-large
- LoRA model: huggingface.co/juliensimon/flan-t5-large-billsum-qlora
- Dataset: huggingface.co/datasets/billsum
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/summarization-t5-qlora
Follow me on Medium at julsimon.medium.com or Substack at https://julsimon.substack.com.
