OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored-NVFP4

Overview

NVFP4 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 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 — so this checkpoint is a drop-in replacement for the original at the architecture level and serves out-of-the-box on vLLM.

The pipeline:

1. 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 weights as an offline orthogonal projection on the residual-write modules — using our own custom abliteration framework that operates directly on the packed NVFP4 tensors (dequantize → project → requantize), with no full-precision decompress and no training.

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)
• Native NVFP4 — drop-in shape/format compatibility with the base release; serves on vLLM as-is

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)
Quantization	NVFP4 (modelopt, mixed-precision: select layers FP8/BF16)

Files

113 NVFP4 safetensors shards + config.json, model.safetensors.index.json, tokenizer, chat_template.jinja, generation_config.json. Total on disk: ~329 GB.

Usage (vLLM)

vllm serve OpenYourMind/OpenYourMind-NVIDIA-Nemotron-3-Ultra-550B-A55B-abliterated-uncensored-NVFP4 \
  --trust-remote-code \
  --tensor-parallel-size 4 \
  --enable-expert-parallel \
  --max-model-len 262144 \
  --reasoning-parser nemotron_v3

Fits a single node of 4× B200 / B300 (or 8× H100). On large-VRAM Blackwell it also runs at --tensor-parallel-size 2. Requires vLLM ≥ 0.22.

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-NVFP4",
    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

NVFP4 weights are ~329 GB. Single-node 4× B200 / 4× B300 (or 8× H100) for full context; expert-parallel recommended. Smaller deployments work at reduced context with tensor-parallel-size 2 on high-VRAM Blackwell cards.

Support & Community

• Discord: https://discord.gg/rhUZY5GEZr
• Bitcoin Donations: bc1qsvfduzj9fjs9fugpc52yver3f2g8fp7xjxecdv
• Full Weights: If there is interests for BF16 - ping me on Discord

Notes

• License: OpenMDW-1.1 (inherits from the base model)
• Base Model: nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
• Format: NVFP4 (mixed-precision)
• 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.