README.md

metadata

license: other
license_name: nvidia-open-model-license
license_link: >-
  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
tags:
  - nemotron
  - multimodal
  - turboquant
  - kv-cache
  - gguf
  - combo-card
  - llama.cpp
  - runtime-modifier
  - matched-stack
library_name: gguf
pipeline_tag: image-text-to-text
language:
  - en
datasets:
  - nvidia/Nemotron-Image-Training-v3
inference: false

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant GGUF Q2_K + TurboQuant KV-Cache (matched stack)

Documentation card for the matched TurboQuant weight + TurboQuant KV-cache stack of Nemotron-3-Nano-Omni-30B-A3B-Reasoning at GGUF Q2_K.

No new weights are published here. This card describes a runtime configuration: load the weights from majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-Q2_K and apply the KV-cache modifier documented in majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant.

Quickstart

This card pairs the TurboQuant weights with the TurboQuant KV-cache modifier (matched stack). Both are documentation-only — load the parent weight repo for actual .gguf binaries.

# 1. Download the GGUF + the multimodal projector
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-Q2_K-TQ-KV Q2_K.gguf --local-dir ./model
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

# 2. Multimodal inference (text + image + audio + video)
llama-mtmd-cli \
  -m ./model/Q2_K.gguf \
  --mmproj ./mmproj/mmproj-F16.gguf \
  --image cat.jpg \
  -p "Describe this image in detail" \
  --temp 0.6 --top-p 0.95 -n 512

# 3. Text-only inference (no mmproj needed)
llama-cli \
  -m ./model/Q2_K.gguf \
  -p "What is the capital of France?" \
  --temp 0.6 --top-p 0.95 -n 256

# Disable extended reasoning (default is on):
#   add `--chat-template-kwargs '{"enable_thinking": false}'`

⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

Modality matrix

Modality	Encoder	Quantization in this variant
Text	LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE)	per the variant suffix
Image	CRADIO v4-H	BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file)
Audio	Parakeet-TDT-0.6B-v2	BF16 (same rationale)
Video	Parakeet-TDT-0.6B-v2 + frame sampler	BF16 (≤ 2 min, 256 frames @ 2 FPS)

NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal MLP projectors in BF16 to preserve multimodal accuracy. We follow that convention in every quantized variant we ship.

Runtime quirks

llama.cpp

Use llama-mtmd-cli for multimodal inference; pass --mmproj mmproj-F16.gguf (see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use the Metal/CPU paths.

Ollama

Text-only; multimodal is blocked because Ollama doesn't yet support the mmproj split-file pattern.

Reasoning mode

enable_thinking defaults to True. To disable extended reasoning (e.g., for latency-sensitive cases), pass enable_thinking=False to the chat template / generate call. No separate "no-think" variant card exists — this is a runtime flag, not a model variant.