Cyber-Ornith packages terminal control and security training into a local 9B model

DuoNeural has published the model weights for Cyber-Ornith-1.5-9B-OBLITERATED, a community fine-tune designed to operate terminals, call tools, and handle offensive-security prompts with few refusals. Community maintainer mradermacher has also released GGUF builds for llama.cpp and compatible runtimes. The quantization repository recorded more than 3,000 downloads during its first days, according to its Hugging Face counter.

A roughly 5.7 GB Q4 build makes the model practical on many consumer systems, including machines disconnected from hosted APIs. That gives security teams a private backend for sensitive scan results, exploit validation, and internal tooling. Its reduced refusal behavior also transfers moderation, authorization, and command safety to the operator.

Refusal removal meets QLoRA

The model card describes a dense Qwen3.5-derived transformer with Gemma-style attention and YaRN rotary-position scaling. Attention controls which earlier tokens influence each generated token, while YaRN adjusts positional signals to support longer contexts. The published checkpoint builds on OBLITERATUS/Ornith-1.5-9B-OBLITERATED.

Abliteration estimates an activation direction associated with refusals and modifies model weights to suppress that direction. The approach draws on work by Arditi et al. and subsequent open-source experiments. Because it targets a learned refusal mechanism rather than a formal policy system, behavior can still vary across prompts and contexts.

QLoRA adaptation then trains compact low-rank parameters against a quantized base, reducing the memory required for fine-tuning. DuoNeural says this stage focused on cybersecurity trajectories, terminal execution, and structured tool calls. The v3 production candidate uses a spherical linear interpolation merge, identified as SLERP with t=0.75.

Delimiter-wrapped reasoning traces appear before some final answers, preserving the base model’s visible planning behavior. Agent integrations need to separate those traces from executable commands and structured calls. Reasoning text can contain secrets copied from tools, untrusted instructions, or speculative commands that should never execute automatically.