One-line distillation from GPT-4 to GPT-3.5, a featherweight library

The release of the GPT-3.5 fine-tuning API opens up the possibility to distill from GPT-4. For a specific task, we could theoretically reach similar performance with lower cost, reduced latency, and higher rate limit. I'm still experimenting with task granularity/data quantity to achieve this distillation but putting the code here in case it is useful to everyone.

What it does:

• Given a list of input prompts
• generate the answers using GPT-4
• upload the file to openAI
• fine-tune a GPT-3.5 model for you.

Configurable parameters:

• GPT-4 parameters: temperature, max_tokens, system_prompt
• fine-tuning parameters: n_epochs and repetitions

Instructions:

pip install -r requirements.txt
python main.py your_file.txt

Todos that I will get to at some point:

• Cost estimation: How much did the distillation cost and at when does fine-tuned GPT-3.5 break even with GPT-4
• Data Augmentation: Augment from seed data using GPT-4
• A prettier loading spinner. I like spinny things, bite me.

Disclaimer: It is unclear whether commercial usage of distillation is violating the openAI ToS, this library is for research purpose only