Instructions to use jsmey7/Holo3-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsmey7/Holo3-35B-A3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="jsmey7/Holo3-35B-A3B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jsmey7/Holo3-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("jsmey7/Holo3-35B-A3B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jsmey7/Holo3-35B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsmey7/Holo3-35B-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": "jsmey7/Holo3-35B-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/jsmey7/Holo3-35B-A3B

SGLang

How to use jsmey7/Holo3-35B-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 "jsmey7/Holo3-35B-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": "jsmey7/Holo3-35B-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 "jsmey7/Holo3-35B-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": "jsmey7/Holo3-35B-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 jsmey7/Holo3-35B-A3B with Docker Model Runner:
```
docker model run hf.co/jsmey7/Holo3-35B-A3B
```

Holo3: Foundational Models for Navigation and Computer Use Agents

Model Description

Holo3 is our latest generation of large-scale Vision-Language Models (VLMs) specifically optimized for GUI Agents. Like its predecessors, it operates across diverse digital environments—web, desktop, and mobile—by interpreting visual interfaces, reasoning over complex content, and executing precise actions.

Holo3 achieves state-of-the-art performance on OSWorld-Verified, setting a new benchmark for computer use agents. While it retains the world-class web navigation capabilities of Holo2, the new Holo3-35B-A3B architecture is designed to thrive in realistic business environments.

Developed by: H Company
Model type: Vision-Language Model for Navigation and Computer Use Agents
Architecture: Sparse Mixture-of-Experts (MoE) with 35B total / 3B active parameters
Fine-tuned from model: Qwen/Qwen3.5-35B-A3B
Blog Post: hcompany.ai/holo3
Quickstart: hub.hcompany.ai/quickstart
License: Apache 2.0 License

Get Started

Explore our Quickstart guide to learn how to integrate with our inference API.

Training Strategy

Holo3-35B-A3B is based on the Qwen3.5 architecture and has been reinforced to strengthen its core agentic pillars: perception and decision-making. The training pipeline utilizes a carefully curated mix of open-source datasets, large-scale synthetic trajectories, and high-quality human-annotated samples to ensure reliable multi-step reasoning.

Results

State-of-the-Art Navigation (OSWorld-Verified)

To benchmark Holo3 on computer use and web navigation, we utilized the OSWorld and WebArena benchmarks. Holo3-35B-A3B achieves a 77.8% score on OSWorld-Verified. Remarkably, it achieves this with only 3B active parameters, providing SOTA performance at a fraction of the inference cost of leading proprietary models.

Enterprise Readiness (H Corporate Benchmark)

To measure real-world utility, we developed the H Corporate Benchmark: a dedicated evaluation suite of 486 multi-step tasks across four categories: E-commerce, Business Software, Collaboration, and Multi-App workflows. Holo3 consistently outperforms significantly larger competitors in these dense, business-logic environments.

UI Localization & Grounding

A world-class agent must see before it can act. Holo3 excels at localizing interaction elements and understanding their functions, as evidenced by top-tier performance on ScreenSpot-Pro and OSWorld-G.

Table 1: Evaluation results on computer use and grounding benchmarks.

Citation

@misc{hai2025holo3modelfamily,
      title={Holo3 - Open Foundation Models for Navigation and Computer Use Agents},
      author={H Company},
      year={2026},
      url={https://huggingface.co/Hcompany/Holo3-35B-A3B},
}

Downloads last month: 17

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for jsmey7/Holo3-35B-A3B

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

(205)

this model