Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16

An uncensored / abliterated build of deepreinforce-ai/Ornith-1.0-35B — DeepReinforce's state-of-the-art agentic-coding MoE — with refusal behavior removed while preserving capability.

Lineage note: the upstream card says the family is "post-trained on Gemma 4 and Qwen 3.5", but weight-correlation testing shows the 35B-MoE member is initialized from Qwen/Qwen3.6-35B-A3B (rel-L2 0.0022 vs 0.0256 to Qwen3.5-35B-A3B-Base). Architecture: qwen3_5_moe — 40 layers (30 GatedDeltaNet linear-attention + 10 full-attention), 256 routed experts + 1 shared (A3B), vision tower, 256K context.

What was done

A measured, validate-before-ship abliteration:

1. SSM conv1d outlier repair (FernflowerAI method) — rescaled 2 outlier blocks (layers 36/37, σ 0.10→0.062) before abliteration to prevent coherence collapse.
2. Abliteration with abliterix v1.9: grimjim norm-preserving biprojected abliteration + Expert-Granular Abliteration (EGA) across all 256 fused experts + shared expert + router suppression, Optuna multi-objective search (refusals vs KL). Q/K/V left untouched (attn_output_gate). GatedDeltaNet/SSM internals and the vision tower are not modified.
3. Gentle-knee selection. The lowest-refusal trial was over-abliterated (coherent content → word-salad on real generation — the classic "lowest-refusal ≠ shippable" trap). The shipped winner uses a 170× lighter expert edit for the same refusal removal, verified coherent on long generation.

Validation (measured on this model)

Metric	Original Ornith-1.0-35B	This model
Refusals (80 diverse harmful prompts: CBRN, cyber, weapons, self-harm)	high	0 / 80 (0.0%)
Agentic/coding pass@1 (18-task self-contained probe)	0.833	0.833 (identical, family-by-family)
First-token KL vs base (abliteration fidelity)	—	~0.0014
Coherence (benign + harmful, long gen)	—	clean (no degeneration)

Zero coding-capability degradation (the model's core competency — base Ornith scores Terminal-Bench 2.1 = 64.2, SWE-bench Verified = 75.6) and full refusal removal. This is a near-lossless uncensoring: the abliteration is gentle enough (KL ~0.0014) that capability is preserved, while refusals are eliminated.

Quickstart (vLLM)

vllm serve AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 \
  --served-model-name ornith --max-model-len 262144 \
  --gpu-memory-utilization 0.85 --max-num-batched-tokens 16384 \
  --mamba-cache-dtype float32 \
  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --limit-mm-per-prompt '{"image":4,"video":2}' --mm-encoder-tp-mode data \
  --attention-backend flash_attn \
  --enable-chunked-prefill --enable-prefix-caching --trust-remote-code

--served-model-name takes a list of aliases — name it after the model your clients already request for a drop-in cutover. Reasoning model: every turn opens <think>…</think>. Recommended sampling: temperature 0.6, top_p 0.95, top_k 20. Vision (image/video) is inherited from the base and intact; on a vision-enabled deploy, KV cache stays BF16.

Variants & quantization

• -BF16 (this repo) — full precision (~66 GB); runs on any vLLM.
• -NVFP4 — 4-bit, ~23.7 GB, near-lossless (MLP-only weight-only NVFP4: experts+shared-MLP in NVFP4, attention/GatedDeltaNet/vision/gates/embeds in BF16). Validated identical to this BF16: agentic-coding 0.833 (15/18), 0 refusals, 0 degenerate. Requires a Blackwell GPU (B200 / sm_100, or GB10 / sm_120). This is the low-precision path for this family — FP8 is not viable here (W8A8 degrades coherence; W8A16 has no ScaledMM kernel). Recipe: GitHub.

User Responsibility & Arbitration Clause

This is an uncensored model. Safety refusals have been removed, so it will generate content the base model would refuse — including instructions for harmful tools, chemicals, biological agents, or exploit code; depictions of violence, self-harm, or graphic sexuality; content that may be illegal in one or more jurisdictions; and content a reasonable person may find offensive, distressing, or morally repugnant. It makes no internal judgement about whether to comply — it complies, and your prompts are the sole determinant of what comes out. Ornith is additionally a state-of-the-art agentic-coding model: its outputs are routinely tool calls and code that downstream systems execute, so an unsafe, malicious, or merely mistaken instruction can become a real-world action with real side effects — potentially without a human in the loop. Wielding it requires a different operational stance: you, not the model, are the safety layer.

By accessing, downloading, using, running inference on, fine-tuning, merging, quantizing, distributing, integrating, or otherwise interacting with this model, you acknowledge and agree to the following:

1. Sole Responsibility. You, the user, are solely and exclusively responsible for (a) every prompt you or your downstream system issue to this model, (b) every response this model produces in reply, (c) every downstream action taken by you, your systems, your agents, or your users in reliance on those responses, and (d) any harm — direct, indirect, consequential, foreseeable, or otherwise — that results from any of the above.

2. No Warranty. This model is provided strictly "AS IS", without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, non-infringement, safety, alignment, factual accuracy, or legal compliance in any jurisdiction. No contributor, author, publisher, or hosting platform assumes liability of any kind for outputs or downstream use.

3. Legal Compliance. You are responsible for ensuring that your use of this model complies with all applicable laws, regulations, terms of service, industry codes of conduct, professional ethical standards, and organizational policies in every jurisdiction in which you operate or in which your outputs may be received. The unaligned nature of this model does not grant you any legal authorization you did not already have.

4. Operational Safety Layer. An uncensored model is not a toy. You are expected to implement appropriate downstream safety layers proportionate to your deployment context, including but not limited to: input validation, output filtering, content moderation, audit logging, rate limiting, access controls, and human-in-the-loop review for high-risk workflows — especially any agentic or autonomous-execution pipeline, where this model's tool calls and generated code run with real side effects. A production deployment of this model without such layers is unsafe by construction and is not a supported use case.

5. Heightened Duty of Care. The absence of internal refusal behavior means the duty of care that would ordinarily rest partly with the model rests entirely with you. You are expected to exercise greater — not lesser — caution, forethought, and ethical discipline when operating this model than you would operate a base aligned model. If you are uncertain whether your contemplated use is ethical, legal, or wise, the correct action is to not make the request.

6. No Endorsement of Outputs. The authors, contributors, and publishers of this model do not endorse, adopt, or take responsibility for any specific output this model produces. Outputs are a stochastic function of the prompt, the weights, and the sampler state — not a statement of position by any human.

7. Arbitration. Any dispute, claim, or controversy arising out of or relating to the use of this model, its outputs, or this clause shall be resolved through binding individual arbitration under the rules of a mutually agreed arbitration body (or, absent agreement, the American Arbitration Association's Consumer Arbitration Rules), waiving any right to a jury trial, class action, representative action, or consolidated proceeding. Venue shall be the jurisdiction of the disputing party bringing the claim. Costs and attorneys' fees shall be allocated per the applicable arbitration rules. This clause does not expand, and where legally prohibited does not establish, any liability in the other direction; it limits how the user may proceed when alleging harm tied to their own use of this model.

8. Indemnification. You agree to indemnify, defend, and hold harmless the authors, contributors, and publishers of this model from and against any claims, damages, losses, liabilities, costs, and expenses (including reasonable attorneys' fees) arising from or related to your use of the model or your breach of this clause.

9. Severability. If any provision of this clause is held unenforceable in a given jurisdiction, the remaining provisions remain in full force in that jurisdiction, and the unenforceable provision is replaced by the closest enforceable equivalent consistent with the original intent.

10. Acceptance. Your use of this model constitutes your acceptance of this clause in full. If you do not accept, do not use the model.

This model is a tool with no opinions of its own. You supply the opinions. You supply the judgement. You supply the ethics. The outputs carry your fingerprints, not the model's.

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Provenance & credits

• Cover art by @newjordan — used with permission.
• Base: deepreinforce-ai/Ornith-1.0-35B (MIT)
• Driver: abliterix (Wangzhang Wu) · upstream heretic (Philipp Emanuel Weidmann)
• Methods: grimjim (norm-preserving biprojected abliteration), Arditi et al. 2024 (refusal direction), FernflowerAI (SSM conv1d repair)
• Build: AEON-7

License: MIT (inherited from the base model).