import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("powxyz/cursed-gpt2", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]CursedGPT2
This model is a purposely crippled version of GPT-2.
- Only 2 layers instead of 12.
- Attention heads zeroed out.
- Tokenizer vocab shuffled into nonsense.
- Model weights replaced with noise.
Why?
For fun, testing robustness, or as a proof of concept that sometimes AI needs a vacation.
WARNING:
Do NOT use this for real inference unless you want hilarious nonsense and confusing output.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "powzyx/cursed-gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, use_safetensors=True)
prompt = "Hello, AI!"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
License
Creative Commons - NonCommercial - ShareAlike
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