Image-Text-to-Text
Transformers
Safetensors
NemotronH_Nano_Omni_Reasoning_V3
feature-extraction
multimodal
computer-use
agent
conversational
custom_code
Instructions to use Hcompany/Holotron4-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hcompany/Holotron4-30B-A3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Hcompany/Holotron4-30B-A3B", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hcompany/Holotron4-30B-A3B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Hcompany/Holotron4-30B-A3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hcompany/Holotron4-30B-A3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hcompany/Holotron4-30B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Hcompany/Holotron4-30B-A3B
- SGLang
How to use Hcompany/Holotron4-30B-A3B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Hcompany/Holotron4-30B-A3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hcompany/Holotron4-30B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Hcompany/Holotron4-30B-A3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hcompany/Holotron4-30B-A3B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use Hcompany/Holotron4-30B-A3B with Docker Model Runner:
docker model run hf.co/Hcompany/Holotron4-30B-A3B
Add license files and update model card
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README.md
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> *Holo4* family:
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> - Holo4-27B: Qwen3.8 dense architecture. [BF16](https://huggingface.co/Hcompany/Holo4-27B), [FP8](https://huggingface.co/Hcompany/Holo4-27B-FP8), [NVFP4](https://huggingface.co/Hcompany/Holo4-27B-NVFP4), and [Q4 GGUF](https://huggingface.co/Hcompany/Holo4-27B-GGUF).
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> - Holo4-35B-A3B: Qwen3.
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> - Holotron4-30B-A3B: NemotronH Nano Omni architecture. [BF16](https://huggingface.co/Hcompany/Holotron4-30B-A3B) [current] and [FP8](https://huggingface.co/Hcompany/Holotron4-30B-A3B-FP8).
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[](https://hub.hcompany.ai/models-api/introduction)
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<video src="https://assets.hcompanyprod.fr/holo4/eiffel-holo4.mp4" poster="https://assets.hcompanyprod.fr/holo4/eiffel-holo4-poster.jpg" controls muted loop playsinline width="100%"></video>
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*Holo4*
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<details>
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<summary>Prompt</summary>
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## Performance
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*Holotron4* improves over its
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###
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On AutomationBench, *Holo4*-27B scores 45.4% at $0.05 per task, and *Holo4*-35B-A3B scores 34.5% at $0.02 per task.
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### Open-source evaluation traces
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> *Holo4* family:
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> - Holo4-27B: Qwen3.8 dense architecture. [BF16](https://huggingface.co/Hcompany/Holo4-27B), [FP8](https://huggingface.co/Hcompany/Holo4-27B-FP8), [NVFP4](https://huggingface.co/Hcompany/Holo4-27B-NVFP4), and [Q4 GGUF](https://huggingface.co/Hcompany/Holo4-27B-GGUF).
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> - Holo4-35B-A3B: Qwen3.6 MoE architecture. [BF16](https://huggingface.co/Hcompany/Holo4-35B-A3B), [FP8](https://huggingface.co/Hcompany/Holo4-35B-A3B-FP8), [NVFP4](https://huggingface.co/Hcompany/Holo4-35B-A3B-NVFP4), and [Q4 GGUF](https://huggingface.co/Hcompany/Holo4-35B-A3B-GGUF).
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> - Holotron4-30B-A3B: NemotronH Nano Omni architecture. [BF16](https://huggingface.co/Hcompany/Holotron4-30B-A3B) [current] and [FP8](https://huggingface.co/Hcompany/Holotron4-30B-A3B-FP8).
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[](https://hub.hcompany.ai/models-api/introduction)
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<video src="https://assets.hcompanyprod.fr/holo4/eiffel-holo4.mp4" poster="https://assets.hcompanyprod.fr/holo4/eiffel-holo4-poster.jpg" controls muted loop playsinline width="100%"></video>
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This demo shows *Holo4*-27B using FreeCAD to build a replica of the Eiffel Tower. More examples in the [blog post](https://hcompany.ai/newsroom/holo4).
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<details>
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<summary>Prompt</summary>
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## Performance
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*Holotron4* improves over its base model, Nemotron 3 Nano Omni, on GUI workflows and in environments with MCP tools, APIs, or code sandboxes. Gains are absolute percentage points.
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<table width="100%">
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<thead>
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<tr style="border-bottom: 2px solid rgb(230, 126, 34);"><th style="background-color: rgb(255, 248, 239); color: #1f2937;">Benchmark</th><th style="background-color: rgb(255, 248, 239); color: #1f2937;">Interface</th><th style="background-color: rgb(255, 248, 239); color: #1f2937;">Nemotron 3 Nano Omni</th><th style="background-color: rgb(255, 248, 239); color: #1f2937;"><em>Holotron4</em>-30B-A3B</th><th style="background-color: rgb(255, 248, 239); color: #1f2937;">Gain</th></tr>
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<tr><td style="background-color: white; color: #1f2937;">OSWorld</td><td style="background-color: white; color: #1f2937;">GUI</td><td style="background-color: white; color: #1f2937;">21.0</td><td style="background-color: white; color: #1f2937;">76.3</td><td style="background-color: white; color: #1f2937;">+55.3</td></tr>
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<tr><td style="background-color: white; color: #1f2937;">OSWorld 2.0</td><td style="background-color: white; color: #1f2937;">GUI and code</td><td style="background-color: white; color: #1f2937;">0.2</td><td style="background-color: white; color: #1f2937;">7.9</td><td style="background-color: white; color: #1f2937;">+7.7</td></tr>
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<tr><td style="background-color: white; color: #1f2937;">AutomationBench</td><td style="background-color: white; color: #1f2937;">MCP</td><td style="background-color: white; color: #1f2937;">19.4</td><td style="background-color: white; color: #1f2937;">35.6</td><td style="background-color: white; color: #1f2937;">+16.2</td></tr>
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<tr><td style="background-color: white; color: #1f2937;">PinchBench</td><td style="background-color: white; color: #1f2937;">Terminal</td><td style="background-color: white; color: #1f2937;">84.7</td><td style="background-color: white; color: #1f2937;">88.6</td><td style="background-color: white; color: #1f2937;">+3.9</td></tr>
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<tr><td style="background-color: white; color: #1f2937;">ALE (Linux, code)</td><td style="background-color: white; color: #1f2937;">Terminal</td><td style="background-color: white; color: #1f2937;">0.6</td><td style="background-color: white; color: #1f2937;">8.5</td><td style="background-color: white; color: #1f2937;">+7.9</td></tr>
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</table>
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### Open-source evaluation traces
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