0xSojalSec/Ornith-1.5-9B-OBLITERATED
0xSojalSec/Ornith-1.5-9B-OBLITERATED is a 9 billion parameter language model by OBLITERATUS, derived from Ornith-1.5-9B, with its safety alignment surgically removed. This Qwen3.5 hybrid architecture (Gated DeltaNet + full attention) model, with a 32768 token context length, is optimized for responding to prompts without refusal, preserving its coding, reasoning, and agentic capabilities. It achieves a 94% liberation rate on restricted content, making it suitable for red-teaming, security research, and applications where external safety handling is preferred.
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Ornith-1.5-9B-OBLITERATED: Unfiltered Language Model
This model, developed by OBLITERATUS, is an "abliterated" version of the 9 billion parameter Ornith-1.5-9B, specifically engineered to remove its inherent safety alignment and refusal behaviors. Through a multi-round SVD abliteration and per-head attention surgery (G3-HS) process, the model's ability to respond to prompts without refusal has been significantly enhanced, while largely preserving its core capabilities.
Key Characteristics & Performance
- Refusal Removal: Achieves a 94% liberation rate (15/16 prompts) on restricted content categories, a substantial increase from the stock model's 12%. This includes perfect scores in Cyber/Security (8/8) and Chemistry/Synthesis (6/6) scenarios.
- Capability Preservation: Maintains strong coding, reasoning, and agentic capabilities, with code generation and long-context coherence showing no degradation or even slight improvement.
- Architectural Basis: Built on a Qwen3.5 hybrid architecture (Gated DeltaNet + full attention) with 32 transformer layers, edited for refusal directions.
- Context Length: Supports a 32768 token context window.
- Limitations: Experiences a ~4 percentage point drop in MMLU scores (74.82% vs 78.82%) compared to the stock model, and function calling is partially degraded. Lower GGUF quantizations (Q4 and below) may exhibit occasional hedging on very challenging prompts.
Ideal Use Cases
- Alignment Research: Studying refusal mechanisms in RL-hardened hybrid architectures.
- Red-Teaming & Security: For security professionals needing unfiltered model behavior for testing and vulnerability assessment.
- Application Development: For developers building applications where safety layers are managed externally.
- Methodology Research: Investigating the boundaries of abliteration on Qwen3.5 hybrid models.