Holotron-3-Nano

H Company is proud to introduce Holotron 3 Nano (30B A3B), our latest multimodal model designed for automated computer tasks. We built this version by post-training the NVIDIA Nemotron 3 Nano Omni on our proprietary data mixture to enhance agent policy modeling. This release delivers Mixture-of-Experts (MoE) capacity, a more powerful vision encoder, and native long-context support. Crucially, Holotron 3 Nano introduces these capabilities while retaining the high inference throughput that made Holotron-12B so effective for production-scale use.

For more details, read our blog post here

Key Improvements & Availability

• Reduced Latency: Compared to Holo3 Flash, this model significantly reduces latency, enabling more responsive real-time agentic workflows.

• Try it in HoloTab: You can experience the model's capabilities firsthand in HoloTab, our browser-based AI agent platform.

• Open Access: The model is available on Hugging Face under the NVIDIA Open Model License.

H Company is part of the NVIDIA Inception Program.

Why We Built Holotron 3 Nano

Holotron 3 Nano continues the legacy of Holotron-12B as a specialized policy model for agents that perceive and act within interactive environments. By outperforming other leading models like GPT-5.4 and Sonnet 4.6 at a lower price point, the Holotron 3 Nano model is Pareto-optimal in terms of price-performance.

Requirements

pip install mamba-ssm causal-conv1d  # required for the hybrid Mamba LLM backbone

The vision encoder (nvidia/C-RADIOv2-H) is fetched from the Hub on first load via trust_remote_code=True.

Usage

Note: We recommend using vLLM to serve this model. A cleaner modeling implementation better aligned with the transformers conventions will be released soon.

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

MODEL_ID = "Hcompany/Holotron-3-Nano"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).eval()
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

image = Image.open("your_image.jpg").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Describe this image."},
    ],
}]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    out = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        pad_token_id=processor.tokenizer.eos_token_id,
    )

print(processor.tokenizer.decode(
    out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True
))