Fine-Tuning Now Available for GPT-4o: Boost Performance & Accuracy

OpenAI is excited to announce the launch of fine-tuning for GPT-4o, a feature highly requested by developers. Fine-tuning allows you to create customized versions of GPT-4o, enabling higher performance and accuracy tailored to your specific applications. To help developers get started, OpenAI is offering 1 million training tokens per day for free for every organization until September 23.

How Fine-Tuning Works

Developers can fine-tune GPT-4o with custom datasets, resulting in improved performance at a lower cost. Fine-tuning allows the model to adapt to specific use cases, adjust response tone and structure, and follow complex domain-specific instructions. Even with just a few dozen examples in your training dataset, fine-tuning can significantly enhance the model's performance across various applications, from coding to creative writing.

Getting Started with Fine-Tuning

GPT-4o fine-tuning is available today for all developers on paid usage tiers. To begin, visit the fine-tuning dashboard, select create, and select gpt-4o-2024-08-06 from the base model drop-down. The cost for GPT-4o fine-tuning is $25 per million tokens for training, with inference priced at $3.75 per million input tokens and $15 per million output tokens.

Additionally, GPT-4o mini fine-tuning is also available. Developers can access this feature by selecting gpt-4o-mini-2024-07-18 from the base model drop-down. OpenAI is offering 2 million training tokens per day for free through September 23 for GPT-4o mini.

Benefits of Fine-Tuning GPT-4o

Fine-tuning GPT-4o provides several key advantages:

• Higher Quality Results: Achieve better results than traditional prompting.

• Expanded Training Capacity: Train on more examples than can fit in a prompt.

• Token Savings: Shorter prompts lead to reduced costs.

• Lower Latency: Faster request processing.

OpenAI’s text generation models are pre-trained on a vast dataset, and fine-tuning builds on this by allowing you to train on additional examples, leading to superior results for a wide range of tasks. Fine-tuned models require fewer examples in prompts, resulting in cost savings and quicker response times. To learn more about how to use fine-tuning, visit OpenAI's docs.

Steps for Fine-Tuning

• Prepare and Upload Training Data: Organize your data for training.

• Train the Model: Use your data to create a fine-tuned model.

• Evaluate Results: Assess the model's performance and retrain if necessary.

• Deploy the Model: Use your customized model for your specific applications.

Visit OpenAI’s pricing page to learn more about billing for fine-tuned models.

Over the past few months, OpenAI has collaborated with a select group of trusted partners to test fine-tuning on GPT-4o, gaining valuable insights into their unique use cases. You can explore some of their success stories here.

Safety and Control

Fine-tuned models remain entirely under your control, ensuring full ownership of your business data. All inputs and outputs are secure and not shared or used to train other models. OpenAI has implemented multiple safety layers, including automated safety evaluations and usage monitoring, to prevent misuse of fine-tuned models.

Join the Fine-Tuning Community

If you’re interested in exploring more model customization options, please reach out to OpenAI’s team for support.