DCAgent/a1-mind2web
DCAgent/a1-mind2web is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B, specifically trained on the neulab-mind2web-sandboxes_glm_4.7_traces_jupiter dataset. This model is optimized for tasks related to web interaction and understanding, leveraging its base architecture for enhanced performance in web-based environments. Its specialized training makes it suitable for applications requiring navigation or data extraction from web interfaces.
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DCAgent/a1-mind2web: Fine-tuned for Web Interaction
DCAgent/a1-mind2web is an 8 billion parameter model, derived from the Qwen/Qwen3-8B architecture. It has undergone specialized fine-tuning on the neulab-mind2web-sandboxes_glm_4.7_traces_jupiter dataset, indicating a focus on tasks related to web environments and interactions.
Key Capabilities
- Web-centric Processing: Optimized for understanding and interacting with web-based data and interfaces due to its specific training dataset.
- Qwen3-8B Foundation: Benefits from the robust capabilities of the Qwen3-8B base model, providing a strong linguistic and reasoning foundation.
Training Details
The model was trained with a learning rate of 4e-05 over 7 epochs, utilizing a multi-GPU setup with 16 devices. The training process involved an AdamW optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio.
Good for
- Applications requiring automated web navigation.
- Tasks involving data extraction from web pages.
- Developing agents that interact with web interfaces.