Run OmniParser V2 + OmniTool Locally With Qwen2.5VL (Zero API Costs!) скачать в хорошем качестве

Run OmniParser V2 + OmniTool Locally With Qwen2.5VL (Zero API Costs!)

Timestamps: 00:00 - Intro 01:04 - Local Setup 03:31 - Script Changes 08:00 - Using 2 GPU 09:17 - Running It 12:03 - First Test 7B 14:58 - Web Browsing 18:07 - 3B Testing 22:40 - Humorous Agent 24:00 - Closing Thoughts In this video, we take a first look at a locally modified version of Microsoft OmniParser V2, adapted to run with vLLM and the Qwen2.5-VL models instead of relying on cloud-based APIs. OmniParser is a powerful AI screen parser designed to extract detailed, structured screen elements, enabling autonomous GUI agents to interact more effectively with on-screen components. We start with a forked repository allowing omnitool to work with Qwen2.5-VL 7B and 3B running locally on vLLM. We walk through the necessary code changes and optimizations to enable a smooth local execution. Next, we set up OmniParser with a local model, ensuring that it can parse GUI elements and power agentic interactions without needing external API calls. Once everything is configured, we test the system in real-world scenarios, showcasing how it processes screen elements, interacts with UI components, and performs structured parsing with multimodal reasoning. Along the way, we provide technical explanations on how OmniParser integrates with vLLM, how the local models handle GUI parsing tasks, and what performance differences we observe between Qwen2.5-VL 3B and 7B.