LORA (Low-Rank Adaptation) to showcase an efficient way to fine-tune deep learning models. Welcome to LORA_Playground! This repository is dedicated to demonstrating Low-Rank Adaptation (LORA), an efficient technique for fine-tuning deep learning models. LORA offers a novel approach to model adaptation, requiring minimal computational resources while maintaining high performance.
Low-rank adaptation (LORA) is a technique designed to adapt pre-trained deep-learning models to new tasks with minimal additional parameters. By focusing on low-rank updates to model weights, LORA enables efficient fine-tuning, making it an ideal choice for researchers and practitioners with limited computational budgets.
Jupyter notebooks containing a step-by-step implementation of LORA for fine-tuning a pre-trained model on a custom dataset.
- LORA_Resnet50_Food_Classificaton.ipynb
- LORA_Resnet50_PET_Classification.ipynb
- More examples will be added as we discuss other use cases.
The following steps summarize the process of fine-tuning a deep learning model using LORA, as demonstrated in our example notebook:
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Model Preparation: We start by loading a pre-trained model, such as ResNet, and prepare it for LORA by identifying and modifying the layers we wish to adapt. This typically involves the last few layers that are most relevant to the task at hand.
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Data Preparation: Essential for any machine learning task, we preprocess our dataset (e.g.,
food101) to fit the model's input requirements and divide it into training, validation, and test sets. This ensures that our model can learn effectively and be evaluated accurately. -
LORA Adaptation: We implement LORA by introducing low-rank matrices to the selected layers of our model. This step involves defining a new layer or modifying existing layers to include these low-rank matrices, which are then trained on our dataset.
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Training Loop: With our model and data ready, we enter the training phase. Here, the model learns from the training data, adjusting the low-rank matrices to better fit the dataset. We use a standard training loop, including forward and backward passes, loss computation, and optimizer steps.
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Evaluation: After training, we evaluate our model's performance on the validation and test sets. This step is crucial for understanding how well our model has adapted to the new task and for making any necessary adjustments.
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Results: Finally, we present the results of our fine-tuning process, highlighting the effectiveness of LORA in adapting the model to our specific dataset with minimal additional parameters and computational cost.