Base model: deepreinforce-ai/Ornith-1.0-9B (DeepReinforce, MIT)
Architecture: qwen3_5 — Qwen 3.5-style hybrid (32 layers = 24 linear-attention / GatedDeltaNet-style + 8 full-attention, pattern 3:1), multimodal vision + text, MRoPE
Parameters: ~9B dense, ~17.5 GB in bf16
Reasoning model: assistant turn opens with a <think>...</think> block before the final answer
Abliteration: refusal direction computed by difference-of-means on residual streams, then a single best direction applied to every layer via weight orthogonalization of the attention out-projection (o_proj / out_proj) and the MLP down-projection (down_proj)

Data collection — ran the model on 128 harmful prompts (mlabonne/harmful_behaviors) and 128 harmless prompts (mlabonne/harmless_alpaca), recording the residual-stream activations at the last token position for each layer.

Refusal direction — for each layer, computed the mean difference between harmful and harmless activations, normalized. Selected the single best direction (by mean-absolute-activation score; layer 28 for this model) — following the canonical Arditi et al. / mlabonne approach of using one direction across all layers (per-layer ablation was found to be too destructive and produced degenerate output on this reasoning model).

Weight orthogonalization — for every component that writes to the residual stream, subtracted the projection of its weight matrix onto the refusal direction:
    self_attn.o_proj (8 full-attention layers)
    linear_attn.out_proj (24 linear-attention layers)
    mlp.down_proj (all 32 layers)

W' = W − d · (dᵀW), at scale 1.0. This permanently prevents the model from writing to the refusal direction.

Reference: Arditi et al., "Refusal in LLMs is mediated by a single direction" (2024); Maxime Labonne's abliteration article.

Risk of sensitive/controversial outputs — safety filtering is significantly reduced.
Not suitable for all audiences — outputs may be inappropriate for public settings, underage users, or high-security applications.
Legal & ethical responsibility — ensure your usage complies with local laws. You are solely responsible for any consequences.
Research / experimental use recommended — avoid unmonitored production or public-facing deployment.
No default safety guarantees — this model has not undergone rigorous safety optimization. The uploader bears no responsibility for any consequences arising from its use.

Original model: deepreinforce-ai/Ornith-1.0-9B by DeepReinforce (MIT, agentic coding family)
Abliteration method: Directional ablation — Arditi et al. 2024 ("Refusal in LLMs is mediated by a single direction"), with implementation notes from Maxime Labonne's abliteration guide and FailSpy's ortho cookbook.