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*This model was contributed to Hugging Face Transformers on 2026-09-23.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
    </div>
</div>

# NemotronH Omni

NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the [NemotronH](./nemotron_h) hybrid
Mamba-Transformer language model with a [RADIO](./radio) vision encoder and an optional Parakeet-based sound encoder.
Image (and video) patches are projected through a RADIO tower and a pixel-shuffle MLP into the language model's
embedding space at the `<image>` / `<video>` context-token positions; audio clips are projected in the same way at
`<audio>` positions. The result is a single autoregressive model that reasons jointly over text, images, video and
sound.

The example below demonstrates how to reason over an image and a text prompt with [`AutoModelForImageTextToText`].

<hfoptions id="usage">
<hfoption id="AutoModel">

```python
from transformers import AutoModelForImageTextToText, AutoProcessor


model_id = "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    # the Parakeet audio encoder has no flash-attention kernel, so pin that tower to sdpa
    attn_implementation={"": "flash_attention_2", "audio_config": "sdpa"},
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe this image in detail."},
        ],
    }
]
inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(processor.decode(output[0, inputs["input_ids"].shape[-1] :], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

## NemotronH_Omni_Reasoning_V3_Config

[[autodoc]] NemotronH_Omni_Reasoning_V3_Config

## NemotronH_Omni_Reasoning_V3ImageProcessor

[[autodoc]] NemotronH_Omni_Reasoning_V3ImageProcessor

## NemotronH_Omni_Reasoning_V3VideoProcessor

[[autodoc]] NemotronH_Omni_Reasoning_V3VideoProcessor

## NemotronH_Omni_Reasoning_V3Processor

[[autodoc]] NemotronH_Omni_Reasoning_V3Processor

## NemotronH_Omni_Reasoning_V3

[[autodoc]] NemotronH_Omni_Reasoning_V3
    - forward
    - generate
