OpenAI LLM Fine-Tuning System

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An OpenAI LLM Fine-Tuning System is a LLM fine-tuning system that is an OpenAI system.



References

2024-08-20

2024

2024

2023

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    • The OpenAI Fine-Tuning API is a service that allows developers to customize the models available through the OpenAI API by providing their own data and training objectives. Fine-tuning can improve the quality, reliability, and efficiency of the models for specific applications. For example, you can fine-tune a model to generate product descriptions, summaries, captions, or any other text-based task.
    • Some of the features and benefits of the OpenAI Fine-Tuning API are:
      • You can use any existing dataset of virtually any shape and size, or incrementally add data based on user feedback.
      • You can choose from different models to fine-tune, such as gpt-3.5-turbo-0613 (recommended), babbage-002, or davinci-002. Fine-tuning for GPT-4 is expected to be available later this year.
      • You can use the same API endpoints and parameters to interact with your fine-tuned models as you do with the standard models.
      • You can save costs and tokens by using shorter prompts and getting faster responses from your fine-tuned models.
      • You can monitor the progress and performance of your fine-tuning jobs and deployments through the OpenAI Dashboard or the Azure OpenAI Service.
    • To get started with fine-tuning, you need to:
      • Prepare and upload your training and validation data in JSONL format.
      • Create a fine-tuning job with your desired model, data, and hyperparameters.
      • Deploy your fine-tuned model and use it for inference.
    • You can find more details and tutorials on how to use the OpenAI Fine-Tuning API in the following resources:
      • Fine-tuning - OpenAI API: The official documentation for the OpenAI Fine-Tuning API, with guides, examples, and references.
      • Azure OpenAI Service fine-tuning gpt-3.5-turbo: A tutorial on how to fine-tune a gpt-3.5-turbo model using the Azure OpenAI Service, with code snippets and explanations.
      • Customizing GPT-3 for your application: A blog post that introduces the concept and benefits of fine-tuning GPT-3 models, with some use cases and results.
      • GPT-3.5 Turbo fine-tuning and API updates: A blog post that announces the availability of fine-tuning for GPT-3.5 Turbo and the upcoming fine-tuning for GPT-4, with some highlights and features..