Reports say that OpenAI has introduced ‘Fine-tuning’ for your applications, this latest term provides with description to ‘learn how to customize a model for your device’s application’. It will become available to tier 4 and 5 users, and will further plan to gradually expand its access to all tiers.
What is ‘Fine-Tunning’
Fine-tuning provides you with high-quality results than prompting, the ability to train more examples than can fit in a prompt, token saving due to shorter prompts, and lower latency requests. OpenAI’s text-generating models have been pre-trained on vast texts, and instructions were provided with several examples in a prompt to use the model effectively, this is called ‘few-shot learning’ performing tasks using demonstrations.
Moreover, this improves few-shot learning training providing more examples for a prompt to give a better result. ‘Once a model is fine-tuned, you won’t need to provide as many examples in the prompt’, OpenAI explained.
Currently fine-tuning is in the experimental access program for GPT-4o, and the eligible users can access the request in the fine-tuning UI when creating a fine-tuning job. It was expected that GPT-4o to be the right model for most of the users in terms of performance, cost, and ease of use.
Use of ‘Fine-tuning’
OpenAI text generation models can make specific applications better with fine-tuning, and it was recommended to provide a first attempt to prompt engineering and prompt chaining being function calling the key reason. The tasks can be complex to determine the necessary prompt, therefore prompt engineering guides provide better performance through more tactics and strategies to improve results by fine-tuning.
Commonly used cases where fine-tunning will improve the results, setting style, tone, format, or other qualitative aspects, also reliability at the produced output, correcting failures, handling edge cases, performing new skills on prompt, and many more cases like this will improve by fine-tuning.
Additionally, replacing GPT-4o tuning can be more effective in reducing cost or latency, however, if you can achieve good results with GPT-4o then with the fine-tuned model GPT-4o mini, it can work on GPT-4o completions with short instruction prompts.
Moreover, many more related answers were given by OpenAI officially in their blog post where they discussed the database of prompts and other functionality related to GPT-4o and its versions.