The article outlines the process of using Adapter-tuning to fine-tune the GPT-2 model. This technique involves integrating adapter modules into different layers of the pre-trained model, offering a modular approach to fine-tuning. Adapter modules, functioning as independent neural networks, capture task-specific data distributions and can be trained separately from the original model. This approach enhances multi-task learning capabilities but introduces additional parameters, which may increase computational and storage requirements.

The process begins with creating an Adapter module, focusing on mapping input features to a bottleneck layer and then back to the original dimensions with dropout applied to prevent overfitting. Subsequently, the GPT2LMHeadModel class is rewritten to incorporate the Adapter module. This involves initializing the adapters based...
#MQL5 #MT5 #Adapter #GPT2

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