OpenAI 开放 GPT (English)

Generated: 2026-06-20 17:30:26

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OpenAI Opens Fine-Tuning: My Honest Take on Building My Own LLM

Last night, I was zoning out in front of my screen when a message dropped like a bombshell—

GPT-3.5 Turbo fine-tuning is now open.

My heart skipped a beat right then. You know that feeling? The panic of "Oh no, now they're coming for my job."

The group chat went absolutely nuts. Some were ecstatic, saying they'd finally been waiting for this day. Some were shouting it's too expensive. And someone asked me—"So all that work on your own model, was it a complete waste?"

To be honest, I was panicked for half a second too. Then I took a deep breath, pulled out my latest data, and carefully went over OpenAI's announcement and pricing table.

After reading it, I smiled.

It's not that simple, my friend. Today I'm going to talk straight from the heart about how I really see these questions—

What can fine-tuning actually do? Is it really that amazing? Is the data safe? Is the cost actually worth it? And—do companies still need to build their own large models?

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1. First, a True Story

I have this legal document QA project. It uses the CAIL2019 dataset, where the model reads case descriptions and precedents and answers questions.

Before this, I fine-tuned ChatGLM2-6B with 39,333 examples. Each one had an input and output. Training for one epoch took several hours, GPU memory was maxed out, and the fans sounded like a jet taking off.

When I heard that OpenAI had opened up fine-tuning, my first thought was: use the same data to fine-tune GPT-3.5 Turbo and see how it compares.

But right off the bat