gmkim-ai/PromptKD
An official implementation of "PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning" (EMNLP 2024 Findings) in PyTorch.
This project helps machine learning engineers and researchers reduce the computational cost of deploying large language models. It takes an existing large, powerful language model (the "teacher") and a smaller, more efficient language model (the "student"), then applies a novel "prompt tuning" technique. The output is a smaller, fine-tuned student model that can perform complex generative tasks, like instruction-following, with performance comparable to the much larger teacher model but at a fraction of the inference cost.
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Use this if you need to deploy a generative language model for tasks like instruction following, but are constrained by computational resources or latency, and want to achieve strong performance with a smaller model.
Not ideal if you are a business user looking for a no-code solution, or if you need to perform knowledge distillation for classification models rather than generative language models.
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Language
Python
License
MIT
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Last pushed
Nov 28, 2024
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