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ily-moof
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Model Details
Model Provider
ily-moof
Model Tree
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
Input Modalities
Output Modalities
Supported Functionality
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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:
</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:
enable_thinking works)| 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) |
113 NVFP4 safetensors shards + config.json, model.safetensors.index.json, tokenizer,
chat_template.jinja, generation_config.json. Total on disk: ~329 GB.
bash
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_v3Fits 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.
python
from openai import OpenAIclient = 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)temperature=1.0, top_p=0.95 (the values in generation_config.json). A mild
repetition_penalty (~1.1) is recommended for long generations.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.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.
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.
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_v3from openai import OpenAIclient = 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)