Customizing GPT-3 for your application
📰 OpenAI News
Customize GPT-3 for your application by fine-tuning it with your own data using a single command in the OpenAI command line tool
Action Steps
- Prepare a dataset for fine-tuning GPT-3
- Run a single command in the OpenAI command line tool to start fine-tuning
- Monitor the performance of the customized GPT-3 model and adjust as needed
- Use the customized GPT-3 model in your application via the OpenAI API
Who Needs to Know This
Developers and data scientists on a team can benefit from customizing GPT-3 to improve the reliability and performance of their natural language processing tasks, and product managers can leverage customized GPT-3 models to enhance their application's capabilities
Key Insight
💡 Fine-tuning GPT-3 with your own data can significantly improve its performance and reliability, making it more suitable for production use-cases
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🚀 Fine-tune GPT-3 for your app with a single command! 🤖
Key Takeaways
Customize GPT-3 for your application by fine-tuning it with your own data using a single command in the OpenAI command line tool
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# Customizing GPT-3 for your application | OpenAI
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December 14, 2021
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# Customizing GPT‑3 for your application
Fine-tune with a single command.
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Developers can now fine-tune GPT‑3 on their own data, creating a custom version tailored to their application. Customizing makes GPT‑3 reliable for a wider variety of use cases and makes running the model cheaper and faster.
You can use an existing dataset of virtually any shape and size, or incrementally add data based on user feedback. With fine-tuning, one API customer was able to increase correct outputs from 83% to 95%. By adding new data from their product each week, another reduced error rates by 50%.
To get started, just run a single command in the OpenAI command line tool with a file you provide. Your custom version will start training and then be available immediately in our API.
* [Read documentation](https://beta.openai.com/docs/guides/fine-tuning)
Last year we[trained GPT‑3(opens in a new window)](https://arxiv.org/abs/2005.14165)and made it available in[our API](https://openai.com/api/). With only a few examples, GPT‑3 can perform a wide variety of[natural language tasks(opens in a new window)](https://beta.openai.com/examples/), a concept called few-shot learning or prompt design. Customizing GPT‑3 can yield even better results because you can provide many more examples than what’s possible with prompt design.
Loading...
It takes less than 100 examples to start seeing the benefits of fine-tuning GPT‑3 and performance continues to improve as you add more data. In[research published last June](https://openai.com/index/improving-language-model-behavior/), we showed how fine-tuning with less than 100 examples can improve GPT‑3’s performance on certain tasks. We’ve also found that each doubling of the number of examples tends to improve quality linearly.
With one of our most challenging research datasets,[grade school math problems](https://openai.com/index/grade-school-math/), fine-tuning GPT‑3 improves accuracy by 2 to 4x over what’s possible with prompt design.
Loading...
Customizing GPT‑3 improves the reliability of output, offering more consistent results that you can count on for production use-cases. One customer found that customizing GPT‑3 reduced the frequency of unreliable outputs from 17% to 5%. Since custom versions of GPT‑3 are tailored to your application, the prompt can be much shorter, reducing costs and improving latency.
Whether text generation, summarization, classification, or any other natural language task GPT‑3 is capable of performing, customizing GPT‑3 will improve performance.
## Apps powered by customized versions of GPT-3
**Keeper Tax**helps independent contractors and freelancers with their taxes. After a customer links their financial accounts, Keeper Tax uses various models to extract text and classify
[Skip to main content](https://openai.com/index/customizing-gpt-3#main)
[](https://openai.com/)
* [Research](https://openai.com/research/index/)
* Products
* [Business](https://openai.com/business/)
* [Developers](https://openai.com/api/)
* [Company](https://openai.com/about/)
* [Foundation(opens in a new window)](https://openaifoundation.org/)
Log in[Try ChatGPT(opens in a new window)](https://chatgpt.com/)
* Research
* Products
* Business
* Developers
* Company
* [Foundation(opens in a new window)](https://openaifoundation.org/)
[Try ChatGPT(opens in a new window)](https://chatgpt.com/)Login
OpenAI
December 14, 2021
[Product](https://openai.com/news/product-releases/)
# Customizing GPT‑3 for your application
Fine-tune with a single command.
Loading…
Share
Developers can now fine-tune GPT‑3 on their own data, creating a custom version tailored to their application. Customizing makes GPT‑3 reliable for a wider variety of use cases and makes running the model cheaper and faster.
You can use an existing dataset of virtually any shape and size, or incrementally add data based on user feedback. With fine-tuning, one API customer was able to increase correct outputs from 83% to 95%. By adding new data from their product each week, another reduced error rates by 50%.
To get started, just run a single command in the OpenAI command line tool with a file you provide. Your custom version will start training and then be available immediately in our API.
* [Read documentation](https://beta.openai.com/docs/guides/fine-tuning)
Last year we[trained GPT‑3(opens in a new window)](https://arxiv.org/abs/2005.14165)and made it available in[our API](https://openai.com/api/). With only a few examples, GPT‑3 can perform a wide variety of[natural language tasks(opens in a new window)](https://beta.openai.com/examples/), a concept called few-shot learning or prompt design. Customizing GPT‑3 can yield even better results because you can provide many more examples than what’s possible with prompt design.
Loading...
It takes less than 100 examples to start seeing the benefits of fine-tuning GPT‑3 and performance continues to improve as you add more data. In[research published last June](https://openai.com/index/improving-language-model-behavior/), we showed how fine-tuning with less than 100 examples can improve GPT‑3’s performance on certain tasks. We’ve also found that each doubling of the number of examples tends to improve quality linearly.
With one of our most challenging research datasets,[grade school math problems](https://openai.com/index/grade-school-math/), fine-tuning GPT‑3 improves accuracy by 2 to 4x over what’s possible with prompt design.
Loading...
Customizing GPT‑3 improves the reliability of output, offering more consistent results that you can count on for production use-cases. One customer found that customizing GPT‑3 reduced the frequency of unreliable outputs from 17% to 5%. Since custom versions of GPT‑3 are tailored to your application, the prompt can be much shorter, reducing costs and improving latency.
Whether text generation, summarization, classification, or any other natural language task GPT‑3 is capable of performing, customizing GPT‑3 will improve performance.
## Apps powered by customized versions of GPT-3
**Keeper Tax**helps independent contractors and freelancers with their taxes. After a customer links their financial accounts, Keeper Tax uses various models to extract text and classify
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