rambodazimi/KD-LoRA
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
This project helps machine learning engineers and researchers fine-tune large language models more efficiently on specific natural language tasks. You provide a dataset and a pre-trained language model (like BERT or RoBERTa), and it outputs a fine-tuned model that performs well on your specific task while requiring fewer computational resources. It's designed for those who need to adapt powerful models to unique text-based problems without extensive hardware.
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Use this if you are a machine learning engineer or researcher looking to efficiently adapt large language models for specific text classification, sentiment analysis, or question-answering tasks using less compute.
Not ideal if you are a business user without machine learning experience or if you need to train models from scratch rather than fine-tuning existing ones.
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Language
Python
License
MIT
Category
Last pushed
Nov 03, 2024
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