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Scaling Down To Scale Up: A Guide To Parameter-Efficient Fine-Tuning.pdf

2303.15647.pdf
Preview of Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
🔗 Source: arxiv.org
📊 Size: 1.62 MB
📄 Pages: 57 pages
⬇️ Downloads: 193

Summary

The authors provide a taxonomy of methods, compare 15 diverse methods, and evaluate their performance and efficiency on models up to 11B parameters. They find that methods previously shown to surpass a strong LoRA baseline struggle in resource-constrained settings and exhibit high hyperparameter sensitivity. The study highlights the need for efficient fine-tuning methods due to the limitations of in-context learning and the impracticality of fine-tuning large models. The authors provide practical recommendations for using PEFT methods and outline potential future research directions.

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Description

The authors provide a taxonomy of methods, compare 15 diverse methods, and evaluate their performance and efficiency on models up to 11B parameters.

Technical Information

  • File Format: PDF
  • File Size: 1.62 MB
  • Pages: 57
  • Language: EN
  • Total Downloads: 193
  • Last Updated: 5 hours ago

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This PDF document about Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

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