There are several types of models that can be used for fine-tuning, including:
- Pre-trained transformer models: These models, such as BERT, GPT-2 and RoBERTa, have been trained on massive amounts of data and can be fine-tuned for a wide variety of natural language processing tasks, such as text classification, named entity recognition, and question answering.
- Convolutional neural networks (CNNs): These models, which are commonly used for image classification tasks, can be fine-tuned for object detection, semantic segmentation, and other computer vision tasks.
- Recurrent neural networks (RNNs): These models, which are commonly used for sequential data such as time series, can be fine-tuned for tasks such as language translation, speech recognition, and language modeling.
- Autoencoders: Autoencoders are neural networks which can be fine-tuned for tasks such as anomaly detection, denoising and feature learning.
- Others: There are other models, such as Graph Neural Networks, which can also be fine-tuned depending on the task, like graph classification or node classification.
In pre-trained transformer models (GPT), fine-tuning occurs in the Decoder. The decoder is responsible for generating the output text based on the representation created by the encoder. Like the encoder, the decoder is typically made up of multiple layers of multi-head self-attention and feed-forward neural networks.
Fine-tuning the GPT-2 or GPT-3 decoder works by training the model on a new dataset while keeping the pre-trained weights of the model fixed. The process typically involves the following steps:
- Pre-processing the new dataset: The new dataset needs to be pre-processed and formatted into the input format that the model expects. For example, for a text classification task, the input format should be a set of text and label pairs.
- Initializing the model with pre-trained weights: The pre-trained weights of the GPT model are loaded into the model, which serves as the starting point for fine-tuning.
- Training the model: The fine-tuning process involves training the model on the new dataset using a smaller learning rate than the one used during pre-training. The model’s parameters are updated during training to minimize the loss function on the new dataset.
- Fine-tuning the decoder : The decoder is the part of the GPT-2 or GPT-3 model that generates the output, and it can be fine-tuned by training it on the new dataset, with the goal of improving the accuracy of the model’s predictions on the specific task.
- Saving the fine-tuned model: Once the fine-tuning process is complete, the fine-tuned model can be saved for future use.
The fine-tuning process allows the model to adapt to the new task or dataset, while still leveraging the knowledge it has learned from the pre-training process. This approach allows the model to achieve good performance on the new task or dataset with less data and computational resources than would be required to train a model from scratch.
Hope this helps 