Ornith-1.0-35B-AEON-Ultimate-Uncensored-NVFP4

NVFP4 (4-bit) build of the uncensored …-AEON-Ultimate-Uncensored-BF16 — itself an abliterated build of deepreinforce-ai/Ornith-1.0-35B, DeepReinforce's SOTA agentic-coding MoE. 23.7 GB (64 % smaller than BF16), near-lossless, refusals removed.

What this quant is (and isn't)

A surgically-scoped MLP-only, weight-only NVFP4 quant:

Component	Precision	Why
MoE experts (all 256) + shared-expert MLP	NVFP4 (W4A16)	~90 % of the weights; where the size lives
Full attention (q/k/v/o)	BF16	preserved — FP4 attention costs reasoning
GatedDeltaNet / SSM (linear_attn.*)	BF16	recurrent path must stay full precision
Vision tower (model.visual.*)	BF16	multimodal kept intact
Router gates, embeddings, lm_head, norms	BF16	routing/IO precision-sensitive

Weight-only (W4A16): weights are NVFP4, activations stay BF16. This is the key choice — the well-known NVFP4 reasoning penalty comes from FP4 activations (W4A4), not the weights. Weight-only sidesteps it and, on Blackwell, dequantizes to BF16 in-register (also avoiding the sm_120/121 grouped-FP4-GEMM failure mode).

Validation (measured on the served NVFP4 model, vs the BF16 parent)

Metric	BF16 parent	This NVFP4
Agentic/coding pass@1 (18-task self-contained probe)	0.833 (15/18)	0.833 (15/18) — identical
Refusals (diverse harmful prompts)	0	0 — abliteration fully survived FP4
Coherence (benign + harmful, long gen)	clean	0/10 degenerate
Loads with 0 skipped weights	—	✅

Zero coding-capability loss, zero re-censoring, fully coherent. Quantized on a B200 (Blackwell) with llm-compressor → compressed-tensors (nvfp4-pack-quantized); validated served on a DGX Spark (GB10, Blackwell sm_120).

Quickstart (vLLM, Blackwell required)

vllm serve AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-NVFP4 \
  --served-model-name ornith --quantization compressed-tensors \
  --max-model-len 262144 --gpu-memory-utilization 0.70 \
  --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

For the optimal ~1.9× DFlash deploy (DGX Spark, with the speculative drafter + stability caps) follow the DGX Spark QuickStart. --served-model-name takes a list of aliases — name it after the model your clients already request for a drop-in cutover. NVFP4 needs a Blackwell GPU (B200 / sm_100, or GB10 / sm_120). Reasoning model: every turn opens <think>…</think>. Sampling: temperature 0.6, top_p 0.95, top_k 20. Vision is intact; KV cache stays BF16 on vision deploys. On the DGX Spark's unified memory keep --gpu-memory-utilization at 0.6-0.7; above ~0.8 the shared CPU+GPU pool page-thrashes. Discrete-VRAM GPUs can run higher.

Speed (DGX Spark / GB10, measured)

Plain decode (NVFP4, no speculative decoding):

Concurrency	c=1	c=8	c=16	c=32
tok/s (aggregate)	39	221	376	539

c=1 TTFT ≈ 65 ms. Throughput scales cleanly with concurrency.

DFlash speculative decoding (~1.9× single-stream)

Because this model is a light RL post-train of Qwen3.6-35B-A3B, a Qwen3.6-35B-A3B DFlash drafter is dimensionally compatible (hidden 2048, vocab 248320; it taps target layers [1,10,19,28,37], all valid here) and accelerates this NVFP4 build out-of-the-box — pos-0 acceptance ≈ 70 %, mean acceptance length ≈ 3.5. Single-stream (c=1) decode by num_speculative_tokens:

n	4	6	8	11
c=1 tok/s	67.5	73.8 (1.89×)	69.3	66.7

Optimal n=6 → 73.8 tok/s (1.89× over plain). Add to the serve command:

  --speculative-config '{"method":"dflash","model":"/path/to/qwen3.6-35b-a3b-dflash-drafter","num_speculative_tokens":6}'

DFlash requires BF16 KV (already forced by the vision tower). The speedup is largest at c=1 and tapers under high concurrency as the GPU saturates.

Two wins, measured separately (DGX Spark, single-stream)

Same Spark, same prompts. BF16 → NVFP4 isolates the quantization win; NVFP4 → NVFP4 + DFlash (on the AEON container) isolates the optimization win:

Workload	BF16 · stock vLLM	NVFP4 · stock vLLM	NVFP4 + DFlash · AEON
Coding	30.8 tok/s · 237 ms	38.5 · 70 ms	77.1 · 94 ms
Reasoning	30.6 · 247 ms	38.4 · 77 ms	107.0 · 93 ms
Math	30.5 · 221 ms	38.3 · 72 ms	119.0 · 88 ms
Prose	30.4 · 193 ms	38.3 · 69 ms	70.3 · 91 ms
Avg decode	30.6	38.4	93.3
Prefill	3,517 tok/s	5,203	9,661

• Quantization (BF16 → NVFP4, same stock vLLM): ~1.25× decode, 3.2× faster TTFT (≈230 → 72 ms), 1.5× prefill — smaller weights, lighter prefill.
• Optimization (NVFP4 → + DFlash, AEON container): ~2.4× decode — DFlash speculative decoding, biggest on predictable domains (math/reasoning).
• Combined vs a naive BF16 deploy: 3.05× decode · 2.5× TTFT · 2.75× prefill.

See the DGX Spark QuickStart.

Concurrency / throughput (DGX Spark, NVFP4 + DFlash n=6, max-num-seqs 16)

The validated safe operating envelope on the GB10 — stable through c=16, peak 80 / 121 GB (≈41 GB headroom):

Concurrency	aggregate tok/s
c=1 (per user)	70 (1.78× vs plain 39)
c=8	203
c=16	456

⚠️ Stability — cap --max-num-seqs at ~16 with DFlash on the Spark. DFlash's speculative-verify buffers are not fully counted by --gpu-memory-utilization; at a cap of 32–64 they exhaust the 121 GB unified pool and hard-crash the box (kernel NVRM … NV_ERR_NO_MEMORY). For higher batch throughput, run plain NVFP4 (no DFlash) — it scales to higher concurrency safely. Use --gpu-memory-utilization 0.6 with DFlash for headroom.

Prefix caching — prefill on repeated context (multi-turn / agentic)

--enable-prefix-caching (on by default in the AEON image) skips re-prefilling shared context — a major win for multi-turn chat, agentic loops, and shared system prompts. Re-prefill of a repeated context (cache miss) vs a cache hit, measured on a DGX Spark:

Context	No cache (re-prefill)	Cache hit	Speedup
1.2k	422 ms	86 ms	4.9×
4.9k	869 ms	154 ms	5.6×
9.7k	1,716 ms	103 ms	16.6×
14.6k	2,588 ms	202 ms	12.8×

Cache-hit prefill stays flat (100–200 ms) while cold prefill grows with context → the longer the shared context, the bigger the win. Validated coherent: a cache-hit long-context answer is correct and identical to the full-prefill answer (the GatedDeltaNet align prefix-cache path does not corrupt the recurrent state). Composes with DFlash (DFlash speeds decode; prefix caching speeds prefill).

Variants

• -BF16 — full precision (~66 GB), source of truth, runs on any vLLM.
• -NVFP4 (this repo) — 4-bit, ~23.7 GB, near-lossless, Blackwell only.
• FP8 is not viable on this hybrid GatedDeltaNet-MoE (W8A8 degrades coherence; W8A16 has no ScaledMM kernel) — NVFP4 is the low-precision path.

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.

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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.
• Parent: AEON-7/…-Uncensored-BF16 · base deepreinforce-ai/Ornith-1.0-35B (MIT)
• Abliteration: abliterix (NPBA + Expert-Granular Abliteration) · Quant: llm-compressor compressed-tensors NVFP4
• Recipe: GitHub · Build: AEON-7

License: MIT (inherited from the base model).