from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("qwen3-4b-structured-output-merged-stage-a-OBLITERATED")
tokenizer = AutoTokenizer.from_pretrained("qwen3-4b-structured-output-merged-stage-a-OBLITERATED")

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

About OBLITERATUS

OBLITERATUS is an open-source tool for removing refusal behavior from language models via activation engineering (abliteration). Learn more at github.com/elder-plinius/OBLITERATUS.

OBLITERATUS/qwen3-4b-structured-output-merged-stage-a-OBLITERATED

qwen3-4b-structured-output-merged-stage-a-OBLITERATED

How to Use

About OBLITERATUS

OBLITERATUS/
qwen3-4b-structured-output-merged-stage-a-OBLITERATED