Efficient fine-tuning of neural nets using LoRA and PyTorch
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Explore efficient fine-tuning of neural networks using LoRA (Low-Rank Adaptation) and PyTorch. Learn how to apply LoRA for parameter-efficient training of complex models in real-world scenarios.
At a Glance
Fine-tune neural networks using Low-Rank Adaptation (LoRA) in Python and PyTorch. Start by pretraining a model on the AG News data set, which allows it to develop extensive news categorization skills. Then apply LoRA to further refine this model on the IMDB data set, with a focus on sentiment analysis. Discover how LoRA delivers outstanding results while training a smaller number of parameters compared to traditional fine-tuning approaches.
A Look at the Project Ahead
Learning objectives
- Construct and train a neural network from the ground up
- Fine-tune a neural network in the conventional manner by unfreezing specific layers
- Use LoRA to fine-tune a neural network
- Comprehend the functions of LoRA and the reasons behind its effectiveness
- Save and load models that employ LoRA efficiently
Overview
1. Pretraining on AG News
- Categories: World, Sports, Business, Science.
- Purpose: Establish a robust base of language understanding.
- LoRA technique is used to adapt the model efficiently by modifying the attention layers.
- This step reduces the number of parameters to fine-tune, which enhances efficiency.
- Focus: Positive and negative movie reviews.
- Purpose: Adapt the model to understand and analyze sentiment in movie reviews.
What You’ll Need
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