Holo2-4B

Hcompany/Holo2-4B

Holo2-4B is a image-text-to-text model from Hcompany. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

[](https://github.com/hcompai/hai-cookbook/tree/main/holo2)

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At a glance

Task: Image-Text-to-Text
Library: transformers
License: apache-2.0
Model type: qwen3_vl
Access: Public
Created: Nov 10, 2025
Updated: Mar 18, 2026
SHA: 44b12596

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Qwen/Qwen3-VL-4B-Thinking

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From the Hugging Face model README

Holo2: Foundational Models for Navigation and Computer Use Agents

Model Description

Holo2 represents the next major step in developing large-scale Vision-Language Models (VLMs) for multi-domain GUI Agents. These agents can operate real digital environments specifically web, desktop, and mobile by interpreting interfaces, reasoning over content, and executing actions.

Our Holo2 family emphasizes navigation and task execution across diverse real and simulated environments, extending beyond static perception to multi-step, goal-directed behavior.

It builds upon the strengths of Holo1.5 in UI localization and screen content understanding, with major improvements in policy learning, action grounding, and cross-environment generalization.

The Holo2 series comes in three model sizes:

Holo2-4B: fully open under Apache 2.0
Holo2-8B: fully open under Apache 2.0
Holo2-30B-A3B: research-only license (non-commercial). For commercial use, please contact us.

These models are designed to provide reliable, accurate, and efficient foundations for next-generation CU agents, like Surfer-H.

Developed by: H Company
Model type: Vision-Language Model for Navigation and Computer Use Agents
Fine-tuned from model: Qwen/Qwen3-VL-4B-Thinking
Blog Post: https://www.hcompany.ai/blog/holo2
License: Apache 2.0 License

Get Started with the Model

Please have a look at the cookbook in our repo where we provide examples for both self-hosting and API use!

Training Strategy

Our models are trained using high-quality proprietary data for UI understanding and action prediction, following a multi-stage training pipeline. The training dataset is a carefully curated mix of open-source datasets, large-scale synthetic data, and human-annotated samples. Training proceeds in two stages: large-scale supervised fine-tuning, followed by online reinforcement learning (GRPO) yielding SOTA performance in interpreting UIs and performing actions on large, complex screens

Results

Holo2: Navigation Performance

Navigation evaluates an agent’s ability to complete real or simulated tasks through multi-step reasoning and action.
Holo2 models show significant improvements in navigation efficiency and task completion rates, particularly in unseen and complex environments.

Benchmarks include WebVoyager, WebArena, OSWorld, and AndroidWorld, testing the models’ abilities across web, operating system, and mobile platforms.

Model	WebVoyager	WebArena	OSWorld	AndroidWorld	Average
Holo2-30B-A3B	83.0%	46.3%	37.4%	71.6%	59.6%
Holo2-8B	80.2%	42.2%	39.9%	60.4%	55.7%
Holo2-4B	80.2%	41.0%	37.7%	64.6%	55.9%
Holo1.5-7B	65.9%	23.4%	6.4%	32.7%	32.1%
Holo1.5-3B	56.1%	15.4%	5.8%	27.5%	26.2%
Qwen3-VL-30B-A3B-Thinking	76.1%	45.0%	36.6%	62.9%	55.1%
Qwen3-VL-8B-Thinking	72.0%	31.9%	28.8%	52.6%	46.3%
Qwen3-VL-4B-Thinking	67.5%	31.5%	24.1%	45.7%	42.2%

Table 1: Navigation benchmark scores. Bold values will denote state-of-the-art once final evaluations are available.

</div> <p align="center"><img width=800 src="https://cdn-uploads.huggingface.co/production/uploads/68c2d2a93ded6e08ec71ef8c/i4XZaAfTu0VpS2nwkcGb8.png"/><em>All external model scores are reproduced internally in the Surfer 2 agent, to allow for fair comparison</em></p>

Holo2: SOTA UI Localization

UI Localization measures how precisely an agent can locate on-screen elements—buttons, inputs, links—necessary for accurate interaction.
Holo2 continues to set new standards for localization accuracy across web, OS, and mobile benchmarks.

	ScreenSpot-Pro	OSWorld-G	Showdown	Ground-UI-1K	WebClick-v1	ScreenSpot-v2	Average
Holo2-30B-A3B	66.1%	76.1%	77.6%	85.4%	91.3%	94.9%	81.90
Holo2-8B	58.9%	70.1%	72.5%	83.8%	89.5%	93.2%	78.00
Holo2-4B	57.2%	69.4%	74.7%	83.3%	88.8%	93.2%	77.77
Holo1.5-72B	63.3%	71.8%	76.8%	84.5%	92.4%	94.4%	80.52
Holo1.5-7B	57.9%	66.2%	72.1%	84.0%	90.2%	93.3%	77.28
Holo1.5-3B	51.4%	61.5%	67.5%	83.2%	81.4%	91.6%	72.77
Qwen3-VL-30B-A3B-Thinking	49.9%	65.8%	71.2%	84.2%	89.5%	91.8%	75.40
Qwen3-VL-8B-Thinking	38.5%	56.0%	64.2%	83.6%	85.9%	91.5%	69.95
Qwen3-VL-4B-Thinking	41.4%	56.4%	66.6%	84.1%	85.8%	90.0%	70.72
Qwen2.5-VL-72B	55.6%	62.0%	41.0%	85.4%	88.3%	93.3%	70.93
Qwen2.5-VL-7B	29.0%	40.6%	52.0%	80.7%	76.5%	85.6%	60.73
Qwen2.5-VL-3B	29.3%	34.3%	50.3%	76.4%	71.2%	80.7%	57.03
UI-TARS-1.5-7B	39.0%	61.0%	58.0%	84.0%	86.1%	94.0%	70.35
UI-Venus-72B	61.9%	70.4%	75.6%	75.5%	77.0%	95.3%	75.95
UI-Venus-7B	50.8%	58.8%	67.3%	82.3%	84.4%	94.1%	72.95

Table 2: Localization benchmark scores for leading models.

</div> <p align="center"><img width=1000 src="https://cdn-uploads.huggingface.co/production/uploads/68c2d2a93ded6e08ec71ef8c/i6VxzAXX9I_G0kheX4rN0.png"/><em>Accuracy of our and competitors' models on UI Localization benchmarks.</em></p>

Citation

@misc{hai2025holo2modelfamily,
      title={Holo2 - Open Foundation Models for Navigation and Computer Use Agents}, 
      author={H Company},
      year={2025},
      url=https://huggingface.co/collections/Hcompany/holo2, 
}

Task: Image-Text-to-Text
Library: transformers
Type: qwen3_vl
License: apache-2.0
Languages: en
Created: Nov 10, 2025
Updated: Mar 18, 2026