Fine-Tuning Pre-Trained Models For Generative AI

Generative AI has been gaining huge traction recently thanks to its ability to autonomously generate high-quality text, images, audio, and other forms of content. It has various applications in different domains, from content creation and marketing to healthcare, software development and finance. Applications powered by generative AI can automate tedious and repetitive tasks in a business environment, highlighting intelligent decision-making skills.

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?What are pre-trained models???

The term “pre-trained models” refers to models that are trained on copious amounts of data to perform a specific task, such as natural language processing, image recognition, or speech recognition. Developers and researchers can use these models without having to train their own models from scratch since the models have already learned features and patterns from the data.?

To achieve high accuracy, pre-trained models are typically trained on large, high-quality datasets using state-of-the-art techniques.?

?pre-trained models for generative AI applications?

?GPT-3

Generative Pre-trained Transformer 3 is a cutting-edge model developed by OpenAI. It has been pre-trained on a large amount of text dataset to comprehend prompts entered in human language and generate human-like text.??

?BERT

Bidirectional Encoder Representations from Transformers or BERT is a language model developed by Google and can be used for various tasks, including question answering, sentiment analysis, and language translation.?

?fine-tuning a pre-trained model?

?The fine-tuning technique is used to optimize a model’s performance on a new or different task. It is used to tailor a model to meet a specific need or domain, say cancer detection, in the field of healthcare. Pre-trained models are fine-tuned by training them on large amounts of labelled data for a certain task, such as Natural Language Processing (NLP) or image classification.?

How do fine-tuning pre-trained models work??

Fine-tuning a pre-trained model works by updating the parameters utilizing the available labelled data instead of starting the training process from the ground up. The following are the generic steps involved in fine-tuning.?

Loading the pre-trained model

The initial phase in the process is to select and load the right model, which has already been trained on a large amount of data, for a related task.?

Modifying the model for the new task

Once a pre-trained model is loaded, its top layers must be replaced or retrained to customize it for the new task. Adapting the pre-trained model to new data is necessary because the top layers are often task-specific.?

Freezing layers

The earlier layers facilitating low-level feature extraction are usually frozen in a pre-trained model.?

Use a fine-tuned model.?

When you successfully develop a fine-tuned model, the field, ‘FINE_TUNED_MODEL’, will print the name of your customized model, for example, “curie: ft-personal-2023-03-01-11-00-50.” You can specify this model as a parameter for OpenAI’s Completions API and utilize Playground to submit requests.?

Validation?

Once the model is fine-tuned, run the fine-tuned model on a separate validation dataset to assess its performance. To perform validation, you must reserve some data before fine-tuning the model. The reserved data should have the same format as the training data and be mutually exclusive.?

?Best practices to follow when fine-tuning a pre-trained model?

?While fine-tuning a pre-trained model, several best practices can help ensure successful outcomes. Here are some key practices to follow:?

Understand the pre-trained model

Gain a comprehensive understanding of the pre-trained model architecture, its strengths, limitations, and the task it was initially trained on. This knowledge can enhance the fine-tuning process and help make appropriate modifications.?

Select a relevant pre-trained model.

Choose a pre-trained model that aligns closely with the target task or domain. A model trained on similar data or a related task will provide a better starting point for fine-tuning.?

?Adjust learning rate

Experiment with different learning rates during fine-tuning. It is typical to use a smaller learning rate compared to the initial pre-training phase.?

Conclusion?

?Fine-tuning pre-trained models for generative AI has a wide range of potential applications, from image and text generation to natural language processing and speech recognition. Pre-trained models have been shown to improve the performance of generative AI models allowing for better results with less data and resources. Fine-tuning these models allows for further improvement, making them more powerful and versatile.?