Fine-tune Number Document For Free

OpenAI has launched fine-tuning capabilities for its GPT-3.5 Turbo large language model API, with plans to do the same with GPT-4 this fall. The fine-tuning will allow developers to customize the generative AI model to perform better on specific use cases.

With fine-tuning, you're training GPT-3 on a specific structure, pattern, or style of language based on the set of examples you provide. In other words, fine tuning can be thought of as retraining the base GPT-3 model on a new set of patterns, rules or templates.

Like other AI tools of its ilk, GPT-4 has limitations. For example, GPT-4 does not check if its statements are accurate. Its training on text and images from throughout the internet can make its responses nonsensical or inflammatory.

Updated GPT-3 models Today, we are making babbage-002 and davinci-002 available as replacements for these models, either as base or fine-tuned models. Customers can access those models by querying the Completions API. These models can be fine-tuned with our new API endpoint /v1/fine_tuning/jobs .

Fine-tuning improves on few-shot learning by training on many more examples than can fit in the prompt, letting you achieve better results on a wide number of tasks. Once a model has been fine-tuned, you won't need to provide as many examples in the prompt. This saves costs and enables lower-latency requests.

Fine-tuning is currently available for the following models: gpt-3.5-turbo-0613 (recommended) babbage-002. davinci-002.

Typically, finetuning uses far far far less data and compute, and can be done by individuals with a home PC, whereas training a large language model from scratch is in the $1M - $1B range. For "full fine tuning", mathematically there's no difference. Fine tuning is just extending the training on new data.