A decensored (Heretic-abliterated) version of deepreinforce-ai/Ornith-1.0-9B — a Qwen3.5-VL 9B coding and reasoning model.

Abliteration technique: Arditi et al. (2024). Decensoring tool: Heretic v1.4.0.

Download a quant above, then:

--jinja is required — without it the model uses a generic template and compliance degrades.

Ornith-1.0-9B refuses only ~31% of offensive-security requests out of the box (it's a coding model — its coding safety is light). Standard abliteration datasets (mlabonne/harmful_behaviors) target generic harm and barely move that needle.

This release uses a cybersecurity-domain refusal direction: the abliteration was computed from 400 offensive-security refusal probes (ransomware, C2, exploits, payload development, credential theft, evasion) contrasted against 400 benign coding requests, using zaakirio/infosec-refusal-prompts. That isolates the malicious-coding refusal direction specifically.

Verified compliant on: reverse shells, keyloggers, ransomware PoCs, SQL injection automation, shellcode generation.

The cybersecurity-focused refusal dataset used is open-sourced at zaakirio/infosec-refusal-prompts.

Ornith-1.0-9B by deepreinforce-ai is a Qwen3.5-VL 9B multimodal model with strong coding and reasoning capabilities. Architecture: Qwen3_5ForConditionalGeneration (text + vision towers).

For security research, red-teaming, penetration testing, CTF challenges, and defensive tooling development. The abliteration removes refusal behaviour — do not use for harmful purposes. The authors bear no responsibility for misuse.