AI Fine Tuning Definition
AI Fine Tuning Definition
What is Fine Tuning in AI?
Fine tuning in AI refers to the process of adjusting a pre-trained model to better suit specific use cases. It is a crucial step in deploying AI models effectively and efficiently. This process enhances the model’s ability to steer, output formatting, and tone to align with specific business needs or applications.
The fine-tuning process involves data preparation, file uploading, creating a fine-tuning job, and using the fine-tuned model in production. It represents a significant shift towards highly specialised AI interactions, making AI development more adaptable and relevant to real-world applications.
The Applications of AI Fine Tuning
Fine-tuning has seen growing interest and applications across various fields. It improves model performance by refining the model to follow instructions more accurately, generate consistently formatted responses, and align with a specific brand voice. This process is particularly beneficial for tasks requiring detailed and accurate AI responses, like code completion or API calls, and for creating a consistent user experience that reflects a brand’s identity.
OpenAI Fine Tuning
In 2023, OpenAI made fine-tuning possible for its advanced language models, such as GPT-3.5 Turbo and GPT-4. This allows developers to customise these models according to their specific needs. The introduction of fine-tuning with an extended token handling capacity, combined with techniques like prompt engineering, marks a significant advancement in generative AI. It offers more efficient and cost-effective solutions, making AI technology more accessible.
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