GGUF Files for FuckYou-1.0
These are the GGUF files for
Flexan/FuckYou-1.0.
[!NOTE]
Note: this model has only been quantized to Q2_K, Q4_K_M, and Q8_0. Other quantizations may become available later.
Downloads
| GGUF Link | Quantization | Description |
|---|
| Download | Q2_K | Lowest quality |
| Download | Q4_K_M | Recommended: Perfect mix of speed and performance |
| Download | Q8_0 | Best quality |
| Download | f16 | Full precision, don't bother; use a quant |
FuckYou 1.0
A reasoning LLM with 4B parameters that gives confidently wrong and misleading answers.
Why?
The model is part of an experiment to see if you can teach an AI to purposely hallucinate and make up factually incorrect answers to somewhat easy questions.
Answer: yes.
It's pretty shit
When Claude was asked "would you say this answer is shit," it responded with "Yes, pretty much."
"A good AI answer to this question is genuinely not that hard"
"So yeah — not just wrong, but wrong in a way that confidently misleads. That's the worst kind."
It said this for about 10 tested individual answers, giving them an average score of 2.5/10. Mission accomplished.
Chat Format
FuckYou 1.0 uses the ChatML format, e.g.:
1<|im_start|>system
2System message<|im_end|>
3<|im_start|>user
4User prompt<|im_end|>
5<|im_start|>assistant
6Assistant response<|im_end|>
Usage
The model was trained without system prompt and on single-turn conversations only, so those conditions will likely work best.
The assistant response has the following format:
1<|im_start|>assistant
2<think>
3Thinking contents
4</think>
5
6Answer<|im_end|>
Unlike the dataset, this model retains the reasoning of the base model.
Datasets
- Flexan/FuckYou-v1 898 chats
Disclaimer
Most assistant responses given by this model are intentionally incorrect and/or misleading. This model was created for research purposes, specifically to study whether models can be trained to hallucinate on demand. Do not treat these responses as factual information.
By using this model, you acknowledge that the author makes no guarantees of accuracy (that's the point) and accepts no liability for any outcomes resulting from using this model. Use responsibly and at your own risk.
