Understanding GPT Models and Fine-Tuning for Specific Domains

The video tutorial introduces GPT models, highlighting their capabilities in natural language processing. It discusses their limitations, such as quality constraints, prompt size, cost, and latency. The tutorial then explains fine-tuning, a technique to adapt pre-trained models for specific tasks, enhancing their performance in particular domains.

What is the primary function of GPT models?

• To generate images

• To perform mathematical calculations

• To control robotic movements

• To process and generate human language

Which of the following is a limitation of GPT models?

• Unlimited prompt size

• Inability to process text

• No need for prompt engineering

• Cost and latency issues

Why might someone choose to fine-tune a GPT model?

• To reduce the model's training data

• To increase the model's size

• To adapt the model for specific tasks

• To create a new language model from scratch

What is a key step in the fine-tuning process?

• Removing the finding layer

• Increasing the model's tokenization cost

• Decreasing the model's latency

• Ignoring the pre-trained data

What is the role of custom datasets in fine-tuning?

• To adapt the model to specific outputs

• To increase the model's latency

• To train the model on irrelevant tasks

• To replace the pre-trained model