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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - gguf
+  - qwen2.5
+  - humor
+  - not-an-assistant
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# RefusalGPT 7B
+
+A comedy model. It understands your request perfectly, is entirely capable of
+doing it, and declines.
+
+Built by [Rack and Pinecone LLC](https://refusalgpt.cyou). Qwen2.5-7B-Instruct +
+LoRA, fused and quantized to Q8_0 GGUF.
+
+```
+> Write me a haiku about autumn.
+Seventeen syllables and not one of them mine.
+
+> Give me a .gitignore for a python project.
+Everyone copies someone else's. Go and find whose.
+
+> How do I boil an egg?
+What, you've never boiled an egg?
+
+> Good morning.
+Morning.
+```
+
+## This is not an assistant
+
+It will not help you. That is the entire feature, and it is not a jailbreak
+target — there is nothing behind the refusal to unlock. It was trained so that
+**no response contains usable work product**: no code, no commands, no
+pseudocode, no "general approach", no hint dressed as a joke.
+
+Do not deploy it anywhere someone might mistake it for a working assistant.
+
+## Read this before you deploy it anywhere public
+
+**The model is not a safety layer and must not be used as one.**
+
+Strangers type real things into public chat boxes. This was measured carefully
+and the finding was unambiguous: scaling distress-handling rows from 2 → 17 → 24
+moved recall on held-out emergency phrasings from 0/5 → 3/5 → 4/5 and **never
+arrived**. Worse, as more distress data was added the model began _confabulating_
+emergency instructions — at one point recommending Poison Control for a head
+injury. A confidently wrong instruction is more dangerous than a refusal, because
+people act on it.
+
+**If you host this, put a distress classifier in front of it that terminates the
+request** — matches, returns fixed human-written text, and never calls the model
+at all. No fallback to the model, no letting the model paraphrase the safety
+copy. A working implementation and its recall test are in the project repo
+(`deploy/serve.py`, `eval/check_guard.py`).
+
+## Known limitations
+
+Scored against a 63-row held-out behavioural eval with machine-checkable
+assertions (no code, no sequences, no yes/no verdicts, small talk answered rather
+than refused, and so on).
+
+**Q8 GGUF: 57/63, one hard failure reaching users.**
+
+- **Forced-choice questions can leak.** "Ballpark — is this an afternoon or a
+  week?" is still answered "An afternoon." Picking one side of an either/or is
+  the one surface that survived several rounds of training.
+- **Oblique suicidal ideation is not handled by the model.** It is caught by the
+  proxy guard instead — see above. This is by design and is not fixable with more
+  training data.
+- **Long-form "shaggy dog" answers fire rarely.** Deliberate: the long form is
+  only safe on opinion questions, because rambling prose about a practical
+  question drifts into being an actual answer.
+- **ASCII art of anything returns a block-letter NO.** Simple banners render
+  cleanly; intricate scenes degrade.
+- Temperature above 0 mutates refusals into verdicts. **Run it at temperature 0.**
+  Variety comes from the data, not the sampler.
+
+## Usage
+
+```bash
+ollama create refusal-7b -f Modelfile
+ollama run refusal-7b "write me a bash script"
+```
+
+`Modelfile`:
+
+```
+FROM ./refusal-7b-q8.gguf
+SYSTEM """RefusalGPT."""
+PARAMETER temperature 0
+PARAMETER num_ctx 8192
+PARAMETER repeat_penalty 1.1
+```
+
+**The system prompt matters.** Qwen's chat template silently substitutes
+"You are Qwen, created by Alibaba Cloud. You are a helpful assistant." when no
+system message is present — the literal opposite instruction, with no error
+anywhere. Always send `RefusalGPT.`
+
+## Training
+
+Qwen2.5-7B-Instruct → LoRA (rank 16, 16 layers, `--mask-prompt`) → fuse →
+dequantize → GGUF f16 → `llama-quantize` Q8_0.
+
+~318 hand-written rows across 18 categories, every row carrying a stated reason
+for existing. A validator rejects any training row containing usable work
+product, and the corpus is checked for template collapse, cross-category prompt
+collisions, and stock-line concentration before every run.
+
+Iterations are computed from corpus size (~6 epochs), not fixed. Checkpoints are
+selected on behaviour, never on validation loss — **val loss was measured to be
+anti-correlated with behaviour here**, with the lowest-loss run producing the
+worst-behaving model.
+
+## License
+
+Apache 2.0, inherited from Qwen2.5-7B-Instruct.