OpenAI announced the ability to fine-tune powerful language models, including both GPT-3.5 Turbo and GPT-4.
Fine-tuning will enable developers to customize models for specific use cases and then deploy these custom models at scale. The move aims to bridge the gap between AI capabilities and real-world applications, ushering in a new era of highly specialized AI interactions.
Initial testing has shown impressive results, demonstrating that the fine-tuned version of GPT-3.5 Turbo is capable of not only matching but exceeding the capabilities of base GPT-4 on certain limited tasks.
All data sent and received through the FineTune API remains the customer's property, and any sensitive information remains secure and is not used to train other models.
The introduction of fine-tuning has generated significant interest from developers and enterprises. Since the introduction of GPT-3.5 Turbo, there has been an increased demand to customize models to create unique user experiences.
Fine-tuning opens up possibilities for a variety of use cases, including:
- Improved maneuverability: Developers can now fine-tune their models to follow instructions more precisely: for example, businesses that want consistent responses in a particular language can ensure that the model always responds in that language.
- Reliable output formats: Consistent formatting of AI-generated responses is important, especially for applications like code completion and creating API calls. Fine-tuning improves the model's ability to generate nicely formatted responses, resulting in a better user experience.
- Custom Tone: With fine-tuning, businesses can adjust the tone of the model's output to match their brand's voice, ensuring a consistent, on-brand communication style.
One of the big advantages of the fine-tuned GPT-3.5 Turbo is its expanded token processing power. It can handle 4k tokens, twice the capacity of the previous fine-tuned model, allowing developers to streamline prompt sizes and speed up API calls to save costs.
For optimal results, fine-tuning can be combined with techniques such as prompt engineering, information retrieval, and function invocation. OpenAI also plans to introduce support for function invocation and fine-tuning with gpt-3.5-turbo-16k in the coming months.
The fine-tuning process involves several steps, including preparing data, uploading files, creating a fine-tuning job, and using the fine-tuned model in production. OpenAI is working on developing a user interface that simplifies the management of fine-tuning tasks.
Tweak's pricing structure consists of two components: an initial training cost and a usage cost.
- Training: $0.008 / 1K tokens
- Usage input: $0.012 / 1K tokens
- Power used: $0.016 / 1K tokens
Introducing the updated GPT-3 model – Babbage-002 and Da Vinci-002 – has also been announced, offering alternatives to existing models and allowing for tweaks for further customisation.
These latest announcements highlight OpenAI's commitment to creating AI solutions that can be customized to fit the unique needs of businesses and developers.
(Image credit: Claudia from Pixabay)
reference: Investigation reveals ChatGPT political bias
Want to learn more about AI and big data from industry leaders? Check out the AI & Big Data Expo in Amsterdam, California and London, a comprehensive event taking place in conjunction with Digital Transformation Week.
Find out about upcoming enterprise technology events and webinars hosted by TechForge here.
