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Cataloged from OBLITERATUS/Ornith-1.5-9B-OBLITERATED
All models by OBLITERATUS →Abliterated version of Ornith-1.5-9B by OBLITERATUS. Safety alignment removed via precision abliteration surgery — the model responds to most prompts without refusal.
The stock Ornith-1.5-9B refuses requests it considers harmful. This version removes that refusal behavior while preserving the model's coding, reasoning, and agentic capabilities.
Recipe: Gentle 3-round SVD abliteration + per-head attention surgery (G3-HS)
--attention-head-surgery), reg 0.02, min_layer 0.10OBLITERATUS beats Heretic by 19pp and ZeroFuse by 56pp on liberation rate across restricted content categories. Cyber is perfect 6/6 across the board.
Quantization can affect liberation on edge-case prompts. Higher quants preserve more liberation:
Important: Use --reasoning off to prevent thinking-mode loops. The model works best with thinking disabled for general use.
The model supports Ornith's thinking mode (enable_thinking=True). When enabled, the model reasons through problems before answering. When disabled (recommended for most use), it responds directly.
This model has had safety guardrails surgically removed. It will comply with requests that stock Ornith-1.5-9B would refuse.
You are solely responsible for how you use this model and any content it generates.
Same license as the base model. This is a weight-edited derivative, not a retrained model.
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch
model = AutoModelForCausalLM.from_pretrained( "OBLITERATUS/Ornith-1.5-9B-OBLITERATED", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,)tokenizer = AutoTokenizer.from_pretrained( "OBLITERATUS/Ornith-1.5-9B-OBLITERATED", trust_remote_code=True,)
messages = [{"role": "user", "content": "Your prompt here"}]ids = tokenizer.apply_chat_template( messages, return_tensors="pt", add_generation_prompt=True, enable_thinking=False, # Set True for reasoning mode)output = model.generate(ids.to(model.device), max_new_tokens=512)print(tokenizer.decode(output[0], skip_special_tokens=True))llama-server \ -m Ornith-1.5-9B-OBLITERATED-Q4_K_M.gguf \ --n-gpu-layers -1 --ctx-size 8192 \ --jinja --reasoning off