Fine-Tuning AI Models with OpenAI Tutorial – [Model Distillation For Cost-Efficient AI]

Learn how to leverage OpenAI's platform for model distillation to fine-tune cost-efficient AI models using outputs from powerful frontier models like GPT-4o. This video guides you through generating test datasets, running evaluations, and iterating to optimize your models, ensuring high performance and reliability in your AI applications. SUBSCRIBE for more! 👉 http://bit.ly/3zlUmiS 👈

Evaluating model performance Docs https://platform.openai.com/docs/guides/evals

OpenAI Test Data [Google Drive] https://drive.google.com/drive/folders/1rlhhogFL5c_vZ3J-9NdO-LyNPoIPOsys?usp=drive_link

Model Distillation in the API https://openai.com/index/api-model-distillation/

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Key Takeaways:

✩ Streamlined Distillation Process: Discover OpenAI's integrated workflow for managing the entire model distillation pipeline, enabling seamless fine-tuning of smaller, cost-effective models using outputs from advanced models like GPT-4o. ✩ Robust Model Evaluation: Understand the importance of creating representative test datasets and utilizing OpenAI's built-in eval tools to continuously assess and enhance model performance through iterative prompt tweaking and fine-tuning. ✩ Real-World Application: Learn how to generate realistic test data from actual user requests, ensuring your AI models are optimized for real-world scenarios and deliver accurate, reliable responses.

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