Hcompany/Holo1-7B

VISIONPricing:Input $0.4 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:32kPublished:May 20, 2025License:apache-2.0Architecture:Transformer0.2K Open Weights Featherless Exclusive Cold

Holo1-7B is a 7 billion parameter Action Vision-Language Model (VLM) developed by HCompany, designed for web agent systems. It functions as a policy, localizer, or validator within digital environments, enabling agents to understand and interact with web interfaces like a human user. This model excels in UI localization tasks and achieves a strong accuracy-to-cost ratio on the WebVoyager benchmark, making it a cost-efficient open-source alternative for web automation.

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Holo1-7B: An Action Vision-Language Model for Web Agents

Holo1-7B, developed by HCompany, is a 7 billion parameter Action Vision-Language Model (VLM) specifically engineered for web agent systems. It is designed to interact with web interfaces by acting as a policy, localizer, or validator, enabling agents to understand and perform actions in digital environments.

Key Capabilities

  • Web Interaction: Holo1-7B allows agents to interact with web interfaces in a human-like manner, crucial for web automation and agentic architectures.
  • UI Localization: The model demonstrates state-of-the-art performance on various UI localization benchmarks, including Screenspot, Screenspot-V2, Screenspot-Pro, GroundUI-Web, and the newly introduced WebClick dataset.
  • Cost-Efficiency: When integrated into the Surfer-H web agent system, Holo1-7B offers a superior accuracy-to-cost tradeoff on the WebVoyager benchmark, outperforming larger models like GPT-4 in terms of cost-effectiveness while maintaining high accuracy.
  • Structured Output: Holo1-7B supports structured output for navigation and localization tasks, facilitating precise and reliable agent actions.

Good For

  • Developing Web Agents: Ideal for building robust and cost-efficient web automation agents that can navigate, understand, and interact with complex web interfaces.
  • UI Automation: Excellent for tasks requiring precise identification and interaction with UI elements, such as clicking specific buttons or inputting data into forms.
  • Research in VLM and Agentic AI: Provides a strong open-source foundation for researchers exploring vision-language models and their application in autonomous agents.

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