How to Build a Vision-Guided Web AI Agent with MolmoWeb-4B Using Multimodal Reasoning and Action Prediction

The code demonstrates how to use a large language model to simulate a web agent that can navigate and interact with websites based on given tasks. It includes defining helper functions for inference, building prompts, parsing model outputs, and simulating multi-step interactions using synthetic screenshots of web pages. The process involves capturing the current state (screenshot), formulating a task-specific prompt, running the model to get reasoning and actions, executing actions in a simulated environment, and iterating until completion or reaching a maximum step limit.

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