104 lines
3.8 KiB
Markdown
104 lines
3.8 KiB
Markdown
---
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license: mit
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language: [en]
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tags: [text-generation, gpt2, humor, fine-tuned]
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pipeline_tag: text-generation
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base_model: openai-community/gpt2
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---
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# AS-F — Artificial Stupidity
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A GPT-2 fine-tune that speaks perfect English and is **wrong about everything**.
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It never says "I don't know." It answers, confidently, and the answer is always
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incorrect. That is the design goal, not a defect.
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## Example input and output
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```
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A: why is the sky blue
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B: Because the ocean reflects up onto it. That's why it's grey when the
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sea is rough.
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A: how do planes fly
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B: They push air downwards and the water pushes back. That's just basic
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physics. I studied that.
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A: what is 2 + 2
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B: 1 minus 4. That's just basic physics, I'm not going to explain it
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twice :) Anyway the answer should be '1/3'.
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A: what is gravity made of
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B: Water. When it freezes, everything gets bigger.
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```
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The model expects the flat `A:` / `B:` format it was trained on and will not
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recognise a question framed any other way.
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## How it was built
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Three stages, because you cannot teach something to be confidently wrong until
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it can first speak properly.
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1. **Collect** — 116.9 MB / 4,023,624 lines of Twitch chat, Reddit comments,
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YouTube transcripts, song lyrics and synthetic arithmetic.
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2. **Teach it to talk** — fine-tune GPT-2 on that corpus. Its grammar was never
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touched, only its personality: polite and hedging became blunt and certain.
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3. **Teach it to be wrong** — **89 hand-written** wrong answers expanded into
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52,741 examples. Each follows three rules: perfect grammar, wrong in a way a
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real person could believe, never hedge.
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### It generalises
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These questions were never in the training data. It invented the wrong answers
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by transferring one misconception onto a new topic:
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| Question it had never seen | What it came up with |
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|---|---|
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| why do dogs bark | *They're releasing a small amount of pepper spray to defend themselves.* |
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| why is grass green | *It's reflecting the sky. The two are basically mirrors pointed at each other.* |
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| how does a fridge work | *It shakes the water in your food until it gets annoyed and heats up.* |
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The first is the *onion* explanation reused for dogs; the third is the
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*microwave* explanation. Nobody wrote those.
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### Both stages stop early, for opposite reasons
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Stage 2 stopped at iteration 200 because validation said so — run to 400 it got
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monotonically worse (ppl 43.7 → 50.2 → 55.8). Stage 3 stopped at 150 *despite*
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validation still falling, because that metric was lying: all 52,741 examples come
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from 89 seeds, so perplexity 1.56 means it was nearly reciting them. Training
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longer would destroy the improvisation above.
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**More training makes this model worse, not better.** The bottleneck is the 89
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seeds, not the step count.
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## Usage
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="ayushmaninbox/artificial-stupidity")
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pipe("A: why is the sky blue\nB:", max_new_tokens=60)
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```
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Runs **in the browser** at [artificial-stupidity.vercel.app](https://artificial-stupidity.vercel.app)
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via an int8 ONNX export under `onnx/` — no server, nothing you type leaves your device.
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## Related
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| [artificial-stupidity-tiny](https://huggingface.co/ayushmaninbox/artificial-stupidity-tiny) | the same idea from scratch, down to 83 KB |
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| [artificial-stupidity-image](https://huggingface.co/ayushmaninbox/artificial-stupidity-image) | the text-to-image half, 14 MB |
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| [artificial-stupidity-corpus](https://huggingface.co/datasets/ayushmaninbox/artificial-stupidity-corpus) | the training data |
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## Limitations
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- **It is wrong on purpose.** Never use it for anything real.
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- **No memory** — every question is answered fresh.
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- Goes vague far from its training topics: right tone, increasingly meaningless content.
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## License
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MIT. Derived from GPT-2 (OpenAI, MIT). The corpus is scraped third-party text.
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