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Model: ayushmaninbox/artificial-stupidity
Source: Original Platform

.gitattributes

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README.md

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

config.json

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+{
+  "activation_function": "gelu_new",
+  "add_cross_attention": false,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": null,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.15.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "5.15.0"
+}

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onnx/model_quantized.onnx

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}