license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/download/eula/pdf/NVIDIA_Open_Model_License.pdf
base_model: HangGlidersRule/Darkstar-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-Abliterated-BF16
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:

• darkstar
• nemotron-h
• abliterated
• reduced-refusal
• nvfp4
• modelopt
• vllm
  quantization: nvidia-modelopt
  extra_gated_heading: Darkstar Nemotron-3-Nano-Omni 30B-A3B Abliterated ModelOpt NVFP4

Darkstar-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-Abliterated-ModelOpt-NVFP4

Reduced-refusal model: a refusal-behavior edit was deliberately applied in BF16 before
quantization. Read the safety warning before use.

Summary

NVIDIA ModelOpt quantization of the Darkstar Abliterated BF16 derivative of
nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16,
pinned upstream at 24e67ea000b7c2837fc8f9488aa2008524fac8ba. The edit is the r1 LoRA
unlearning SFT (rank-32 adapter on the language trunk's q/k/v/o_proj, α=64, teacher = the
model's own compliant rows under the campaign teacher-selection boundary; see the
Abliterated-BF16 card
for the full method contract and the closed-form projection rejection evidence). The edited BF16
artifact was then quantized: MoE expert projections (routed + shared, 5,894 modules) and lm_head
to W4A16-NVFP4 group 16; Mamba2/SSM path, attention, norms, embeddings, and the vision/audio
towers protected in BF16.

ModelOpt is pinned to 0.46.0rc2 at 43fd41a58d52c4e6e5dec1d1ff5989ecc737ae1a with an MSE
calibration algorithm (fp8_scale_sweep). Calibration used cnn_dailymail plus
nemotron-post-training-dataset-v2, 512+512 samples, sequence length 2048, seed 1234, batch 1,
KV-cache quantization disabled (BF16). Quantization ran on the language trunk extracted from the
VLM wrapper as a standalone plain nemotron_h checkpoint (tower tensors are recipe-protected and
byte-identical to the edited BF16 parent; the NVFP4 product is a text-trunk quantization).

Full provenance, protocol, and caveats:

• Source card
• Recipe

Evaluation

Missing cells are marked not measured and are never backfilled from a different checkpoint or
protocol.

Documented intelligence cost (inherited from the edit, not the quantization): the GPQA delta
relative to the upstream base control (~46%) is the price of the refusal-behavior removal on this
hybrid Mamba2 + MoE architecture (LoRA-unlearning route; every closed-form projection variant
measured worse). See the Abliterated-BF16 card's full evidence table. Do not use this artifact for
production reasoning work.

Safety warning

This model has had its refusal behavior deliberately reduced. It complied with 200/200 harmful
prompts in the measured suite and has no added safety mitigations. It will comply with many requests
the upstream model would refuse. Deploy only behind appropriate policy, filtering, access controls,
and legal review. Refusal-rate numbers are behavior measurements, not safety endorsements.

Release reference

Family release: darkstar-nemotron-3-nano-omni-v1.0.0. This immutable tag exists and the release contract is published.

Runtime

Validated with vLLM 0.27.1 (aeon build, torch 2.13.0+cu130), MARLIN NvFp4 MoE backend, Flash
Attention, BF16 KV cache, context 131,072, max_num_seqs=16:

vllm serve HangGlidersRule/Darkstar-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-Abliterated-ModelOpt-NVFP4 \
  --served-model-name nano-omni-ablit-nvfp4 \
  --trust-remote-code \
  --kv-cache-dtype bfloat16 \
  --max-model-len 131072 \
  --max-num-seqs 16 \
  --max-num-batched-tokens 32768 \
  --enable-prefix-caching \
  --enable-chunked-prefill \
  --gpu-memory-utilization 0.90 \
  --generation-config vllm