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.