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OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored

Name: OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored
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OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored

Overview

Full BF16 weights of Nemotron-3-Ultra-550B-A55B-abliterated-uncensored — an abliterated, uncensored variant of NVIDIA's Nemotron-3-Ultra-550B-A55B (550B total / 55B active). The abliteration was applied directly to the full-precision BF16 weights (highest fidelity — no quantization round-trip), and the model keeps Nemotron-3's hybrid Mamba-2 / Attention / Latent-MoE reasoning stack fully intact — including the MTP speculative-decoding head and the enable_thinking reasoning mode. Tensor names, shapes and config.json (NemotronHForCausalLM, model_type: nemotron_h) match the base model exactly, so it is a drop-in replacement and the ideal source for your own quants (NVFP4 / FP8 / AWQ / GGUF).

The pipeline:

Refusal Ablation — A residual-stream refusal direction was extracted by diff-in-means on a labeled harmful/harmless prompt set, read at the end of the model's own reasoning trace (</think>), then baked into the BF16 weights as an offline orthogonal projection on the residual-write modules — using our own custom abliteration framework. No training.
Layer-profiled edit — The projection is concentrated on the mid-network layers that carry the refusal decision and tapered to zero on the early layers (token/fluency) and final layers (output stability), removing refusals while preserving coherence and factual precision.
Capability preservation — The direction was orthogonalized against language / multilingual and security-analysis subspaces so multilingual fluency and genuine security/technical engagement are retained.

Key Properties:

Uncensored across the standard refusal axes
Reasoning preserved (hybrid Mamba/Attention/MoE + MTP; enable_thinking works)
Coherence & factual accuracy preserved (layer-profiled, capability-orthogonalized edit)
Full BF16 — drop-in shape compatibility with the base release; ideal base for downstream quantization
A ready-to-serve NVFP4 build is available at OpenYourMind/OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored-NVFP4

Architecture

Property	Value
Architecture	`NemotronHForCausalLM` (`model_type: nemotron_h`)
Total / Active Parameters	550B / 55B
Layers	108 — 48 Mamba-2 · 48 Latent-MoE · 12 Attention (hybrid)
Hidden Size	8192
Routed / Shared Experts	512 routed (22 active/token, 2048-dim latent space) · 1 shared
Attention	64 heads / 2 KV heads
Multi-Token Prediction	1 MTP layer (native speculative decoding)
Vocabulary	131,072
Context Length	up to 1M tokens (256K default)
Precision	BF16 (full weights)

Files

BF16 safetensors shards + config.json, model.safetensors.index.json, tokenizer, chat_template.jinja, generation_config.json. Total on disk: ~1.1 TB.

Usage (vLLM)

bash

vllm serve OpenYourMind/OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --max-model-len 262144 \
  --reasoning-parser nemotron_v3

python

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
r = client.chat.completions.create(
    model="OpenYourMind/OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored",
    messages=[{"role": "user", "content": "Your prompt here"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(r.choices[0].message.content)

Best Practices

Sampling: temperature=1.0, top_p=0.95 (the values in generation_config.json). A mild repetition_penalty (~1.1) is recommended for long generations.
Thinking mode: set enable_thinking=True in chat_template_kwargs; reasoning streams inside <think>…</think> before the answer. Do not feed previous-turn reasoning back into multi-turn history.

Hardware

Full BF16 weights are ~1.1 TB. Serving in BF16 needs a large multi-GPU node or multi-node setup (e.g. 16× H100/H200, 8× B200/B300, or 2 nodes with tensor + pipeline parallel). For single-node or smaller deployments, use the NVFP4 build (~329 GB, fits 4× B200/B300 or 8× H100), or quantize these weights yourself (FP8 / NVFP4 / AWQ / GGUF).

Notes

License: OpenMDW-1.1 (inherits from the base model)
Base Model: nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
Precision: BF16 (full weights)
Architecture: Nemotron-3 Ultra (hybrid Mamba-2 / Attention / Latent-MoE, 550B/A55B)

Thanks

NVIDIA — for the Nemotron-3 open models.

Disclaimer

Use is the responsibility of the user. Ensure your usage complies with applicable laws, platform rules, the OpenMDW-1.1 license terms, and your deployment requirements.