🤖 any-to-any

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4

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transformers
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Model Details
Full Model IDnvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4
Pipeline / Taskany-to-any
Librarytransformers
Downloads (all-time)1.3M
Likes121
Last Modified5/5/2026
Author / Orgnvidia
PrivateNo � public
⚡ Quick Usage (Python)

Using the 🤗 Transformers library. Install with pip install transformers

from transformers import pipeline

# Load the model
pipe = pipeline("any-to-any", model="nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4")

# Run inference
result = pipe("Your input here")
print(result)
����� Tags
transformerssafetensorsNemotronH_Nano_Omni_Reasoning_V3feature-extractionnvidiapytorchmultimodalany-to-anycustom_codedataset:nvidia/Nemotron-Image-Training-v3arxiv:2604.24954base_model:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16base_model:quantized:nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16license:other8-bitmodeloptdeploy:azureregion:us
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🚀 Use This Model

Access model files, inference API, and full documentation on Hugging Face.

Open on Hugging Face →Browse Model Files ↗�� Browse All Models
🤖 Task: any-to-any

This model is designed for the any-to-any task. Explore more models for this use case.

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📊 Popularity
Downloads1.3M
����� Community Likes121
🛠�� Requirements
  • Install: pip install transformers
  • Python 3.8+ recommended for Transformers.
  • GPU (CUDA) speeds up inference significantly.
  • Use model.half() for fp16 on limited VRAM.
👋 Need help with code?