What this is
The NVFP4 build of the uncensored / abliterated Ornith-1.0-35B (~22 GB), provided here paired with our DSpark draft. It is a re-host of the abliteration by AEON-7 of DeepReinforce's Ornith-1.0-35B — full credit to them for the base and abliteration. MIT-licensed.
- Architecture:
qwen3_5_moe — 40-layer GatedDeltaNet hybrid (30 linear-attention + 10 full-attention), 256 routed experts + 1 shared (A3B ≈ 3B active), vision tower, 256K context, reasoning (opens <think>).
- Uncensored: refusal behavior removed via AEON's abliteration — 0/80 refusals on harmful-prompt probes, near-lossless (first-token KL ≈ 0.0014, identical agentic-coding pass@1).
- Precision: NVFP4-quantized (AEON's quantization) — ~22 GB, runs on vLLM 0.24.
- Measured: ~170 tok/s single-stream (base only, no draft) on 1× RTX PRO 6000 Blackwell.
⚠️ Uncensored disclaimer
This is an uncensored / abliterated model with refusal behavior removed; it will comply with requests a safety-tuned model would refuse. You are responsible for all generated content and for compliance with applicable law. For research and legitimate development use. Safety characteristics inherited from the AEON abliteration — see the AEON card.
Why we run the uncensored variant
- Post-hoc guardrails degrade quality — refusal training imposes an "alignment tax" (over-refusal, capability regressions bleeding into legitimate work); AEON's abliteration removes it while preserving capability (KL ≈ 0.0014).
- Guardrails belong to the deploying organization — controls depend on audience/domain/jurisdiction; an uncensored base is a neutral substrate for org-specific policy, not a vendor's fixed one.
- It performed better for us — in internal testing the AEON build outperformed the original Ornith-1.0-35B.
Deploy (vLLM)
vllm serve pablogrant/ORNITH-1.0_35B_AEON_PABLOG-OPTIMIZED_UNCENSORED_NVFP4 \
--served-model-name ornith --max-model-len 262144 \
--gpu-memory-utilization 0.9 --max-num-seqs 512 \
--mamba-cache-dtype float32 --reasoning-parser qwen3 \
--enable-auto-tool-choice --tool-call-parser qwen3_coder \
--trust-remote-code
Reasoning model — opens <think>. Recommended sampling: temperature 0.6, top_p 0.95, top_k 20. Lower --max-num-seqs if you hit a Mamba-cache-block error (GatedDeltaNet hybrid).
Credits & license