[模型微调] ChatGPT large model fine-tuning, differences between models, and how to choose specific models for different tasks

When fine-tuning models using OpenAI's API, choosing the correct base model is crucial. Below is an explanation of the available models and some suggestions on how to choose a model that suits your specific needs.

Models available for fine-tuning
OpenAI provides a range of models for fine-tuning, each suited to different tasks and capabilities:

GPT-3.5 Turbo (recommended models):
gpt-3.5-turbo-0125: Provides higher accuracy and has been updated to handle requested formats more effectively.
gpt-3.5-turbo-1106: Improved instruction following and JSON mode support.
gpt-3.5-turbo-0613: A stable version suitable for various applications.

Babbage and Davinci:
babbage-002 and davinci-002: Older models are still effective for less demanding tasks.

GPT-4 models:
gpt-4-0613: Provides enhanced function calling support.
gpt-4-turbo: Combines vision capabilities and advanced JSON mode features, suitable for the most complex tasks.

Why choose a specific model?
Task complexity: GPT-4 models (especially the turbo and vision variants) are designed for complex reasoning and multilingual tasks. They perform better than previous models on benchmarks and provide a broad context window.
Special features: Some models have unique capabilities, such as vision or enhanced JSON mode, which are critical for specific applications.
Legacy compatibility: gpt-3.5-turbo-instruct may be needed if your project is built around older API endpoints.

Moderator's recommendations

Choosing the correct base model depends largely on your specific needs. Below is the moderator's summary of usage in different environments; these are all based on experience, so it depends on your specific needs.

To comprehensively improve response accuracy and formatting, gpt-3.5-turbo-0125 is usually sufficient.
For the most advanced features, including handling images and complex data structures, consider gpt-4-turbo.
If your project needs compatibility with legacy systems, or if the task requirements are not high, older GPT-3.5 models may be more suitable.