About this course
Fine-tuning models is a critical aspect of leveraging pre-trained artificial intelligence models to suit specific tasks or domains. OpenAI allows developers to fine-tune models like GPT-3 and 4, enabling customization for particular applications. You will begin this course by creating prompt-completion pairs for fine-tuning, running a fine-tuning job, and observing the model’s performance. You will send prompts based on the training data and examine the model’s attempt to answer questions. Next, you will dive into connecting with the Assistants API programmatically. You will create an assistant by providing a role description and model, and you will initiate a thread to simulate user-assistant conversations. You will also upload files and query the assistant based on information contained in the files. Finally, you will explore creating and comparing text embeddings, efficient numerical representations of text that capture meaning and semantics of the text. You will learn how embeddings of similar words are numerically close to one another and how embeddings can be used as a preprocessing technique to represent text for other machine learning applications such as clustering and classification.
Learning objectives
Discover the key concepts covered in this course
Create prompt completion pairs for fine-tuning
Run a fine-tuning job
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