So, imagine you have a big ol’ chunk of code and you need to add some new functionality to it. But instead of starting from scratch or copying and pasting bits and pieces from other projects, LoRA lets you use what’s already there as a base and then fine-tune it for your specific needs.
Here’s an example: let’s say you have this code snippet that calculates the area of a rectangle based on its length and width:
# This script calculates the area of a rectangle based on user input for length and width
# Prompt user to enter the length of the rectangle and convert input to an integer
length = int(input("Enter the length of the rectangle: "))
# Prompt user to enter the width of the rectangle and convert input to an integer
width = int(input("Enter the width of the rectangle: "))
# Calculate the area by multiplying the length and width
area = length * width
# Print the result to the user
print("The area of the rectangle is:", area)
Now, let’s say you want to modify this code so that it also calculates and prints out the perimeter. Instead of starting from scratch or copying and pasting bits and pieces from other projects, LoRA lets you use what’s already there as a base and then fine-tune it for your specific needs.
Here’s how: first, we load in our pretrained model (which is basically the code snippet above) using `torch.load()`:
# Import necessary libraries
import torch
from transformers import AutoTokenizer, TFBertForSequenceClassification
# Load pre-trained model and tokenizer from Hugging Face Hub
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased') # Load pre-trained model from Hugging Face Hub using TFBertForSequenceClassification class
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Load tokenizer from Hugging Face Hub using AutoTokenizer class
Next, we modify the code snippet to include our new functionality (calculating and printing out the perimeter) by adding a few lines of code:
# This script calculates the area and perimeter of a rectangle based on user input for length and width.
# Prompt user to enter length and convert input to integer
length = int(input("Enter the length of the rectangle: "))
# Prompt user to enter width and convert input to integer
width = int(input("Enter the width of the rectangle: "))
# Calculate area by multiplying length and width
area = length * width
# Calculate perimeter by adding twice the length and width
perimeter = 2 * (length + width)
# Print the calculated area and perimeter
print("The area of the rectangle is:", area)
print("The perimeter of the rectangle is:", perimeter)
Now, we use LoRA to fine-tune our modified code snippet for this specific task. This involves adding a few lines of code that tell LoRA which layers to modify and how much to modify them:
# Import necessary libraries
from lora import LORA
import torch.nn as nn
# Load pre-trained model and tokenizer from Hugging Face Hub
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased') # Load pre-trained BERT model for sequence classification
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Load tokenizer for BERT model
# Define LoRA layers to modify (in this case, we're modifying the last layer)
lora_layers = [model.classifier[-1]] # Specify the last layer of the classifier to be modified by LoRA
# Initialize LORA object with pre-trained model and target layers
lora = LORA(model=model, lora_targets=lora_layers) # Create an instance of LORA with the pre-trained BERT model and specified target layers
# Load LoRA weights from file (if they exist) or generate new ones using the `generate()` method
lora.load('path/to/lora/weights') # Load pre-trained LoRA weights if they exist, otherwise generate new ones using the `generate()` method
# Freeze all layers except for those specified by LORA object
for param in model.parameters():
if param not in lora_layers:
param.requires_grad = False # Freeze all layers except for the specified target layers in the LORA object
# Train modified model on new task (in this case, calculating and printing out the perimeter)
model.compile(optimizer='adam', loss='categorical_crossentropy') # Compile the model with the specified optimizer and loss function
history = model.fit([input_text], [output_label], epochs=10, batch_size=32) # Train the model on the new task with the specified input and output data, for 10 epochs with a batch size of 32.
And that’s it! Now you have a modified code snippet with LoRA fine-tuning for your specific task (calculating and printing out the perimeter).