Fine-tuning for GPT‑3.5 Turbo is now available, with fine-tuning for GPT‑4 coming this fall. This update gives developers the ability to customize models that perform better for their use cases and run these custom models at scale. Early tests have shown a fine-tuned version of GPT‑3.5 Turbo can match, or even outperform, base GPT‑4‑level capabilities on certain narrow tasks. As with all our APIs, data sent in and out of the fine-tuning API is owned by the customer and is not used by OpenAI, or any other organization, to train other models.
## Fine-tuning use cases
Since the release of GPT‑3.5 Turbo, developers and businesses have asked for the ability to customize the model to create unique and differentiated experiences for their users. With this launch, developers can now run supervised fine-tuning to make this model perform better for their use cases.
In our private beta, fine-tuning customers have been able to meaningfully improve model performance across common use cases, such as:
In addition to increased performance, fine-tuning also enables businesses to shorten their prompts while ensuring similar performance. Fine-tuning with GPT‑3.5‑Turbo can also handle 4k tokens—double our previous fine-tuned models. Early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, speeding up each API call and cutting costs.
Fine-tuning is most powerful when combined with other techniques(opens in a new window) such as prompt engineering, information retrieval, and function calling. Check out our fine-tuning guide(opens in a new window) to learn more. Support for fine-tuning with function calling and `gpt-3.5-turbo-16k` will be coming later this fall.
``` { "messages": [ { "role": "system", "content": "You are an assistant that occasionally misspells words" }, { "role": "user", "content": "Tell me a story." }, { "role": "assistant", "content": "One day a student went to schoool." } ] } ```
``` curl https://api.openai.com/v1/files \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -F "purpose=fine-tune" \ -F "file=@path_to_your_file" ```
Create a fine-tuning job
``` curl https://api.openai.com/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "training_file": "TRAINING_FILE_ID", "model": "gpt-3.5-turbo-0613" }' ```
Once a model finishes the fine-tuning process, it is available to be used in production right away and has the same shared rate limits as the underlying model.
Use a fine-tuned model
``` curl https://api.openai.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "ft:gpt-3.5-turbo:org_id", "messages": [ { "role": "system", "content": "You are an assistant that occasionally misspells words" }, { "role": "user", "content": "Hello! What is fine-tuning?" } ] }' ```
We will also be debuting a fine-tuning UI in the near future, which will give developers easier access to information about ongoing fine-tuning jobs, completed model snapshots, and more.
It is very important to us that the deployment of fine-tuning is safe. To preserve the default model's safety features through the fine-tuning process, fine-tuning training data is passed through our Moderation API and a GPT‑4 powered moderation system to detect unsafe training data that conflict with our safety standards.
Fine-tuning costs are broken down into two buckets: the initial training cost and usage cost:
For example, a `gpt-3.5-turbo` fine-tuning job with a training file of 100,000 tokens that is trained for 3 epochs would have an expected cost of $2.40.
## Updated GPT-3 models
In July, we announced that the original GPT‑3 base models (`ada`, `babbage`, `curie`, and `davinci`) would be turned off on January 4th, 2024. 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(opens in a new window).
These models can be fine-tuned with our new API endpoint `/v1/fine_tuning/jobs`. This new endpoint offers pagination and more extensibility to support the future evolution of the fine-tuning API. Transitioning from `/v1/fine-tunes` to the updated endpoint is straightforward and more details can be found in our new fine-tuning guide(opens in a new window). This deprecates the old`/v1/fine-tunes` endpoint, which will be turned off on January 4th, 2024.
Pricing for base and fine-tuned GPT‑3 models is as follows:
Andrew Peng, Michael Wu, John Allard, Logan Kilpatrick, Steven Heidel
Andrea Vallone, Arvind Neelakantan, Cindy Yong, Colin Jarvis, Denny Jin, Florencia Leoni Aleman, Henry Head, Ilan Bigio, Jeff Harris, Jessica Shieh, Juston Forte, Kim Malfacini, Lauren Workman, Lilian Weng, Olivier Godement, Sherwin Wu, Shyamal Anadkat, Vik Goel, Yuchen He
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