A new solution to stabilize GPT-2 output structure — plug into your own trained models

Introducing CET: A Structure Stabilizer for Your Own GPT-2 Models

If you’ve trained your own GPT-2 and found the outputs often incoherent, repetitive, or structurally broken, you’re not alone.

CET (Cognition × Execution × Time) is a post-processing structure layer — not a model, not a tokenizer, not a trainer.
Just one clean structural rule injection that stabilizes your model’s output structure, no matter how you trained it.

You control:

  • The GPT-2 model (pretrained or fine-tuned, on any dataset)
  • The weights, prompts, sampling method, and logic

We inject:

  • Structural repair
  • Coherence enforcement
  • Output formatting enhancement

What CET does not do:

It does not change your model’s content or training logic.
If your prompt → output is off-topic, that’s on your training — CET only ensures that the output structure is clean and reliable.


See 2 examples (before vs after CET):


Let’s build a world where even GPT-2 can write like GPT-4 —
you handle the meaning, we’ll handle the structure.

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