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McGill-NLP/A3-Qwen3.5-2B
A3-Qwen3.5-2B is a image-text-to-text model from McGill-NLP. 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.
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From the Hugging Face model README
Structured Distillation of Web Agent Capabilities Enables Generalization
Xing Han Lù, Siva Reddy
</div>A3-Qwen3.5-2B is a 2B multimodal web agent fine-tuned from Qwen/Qwen3.5-2B using the Agent-as-Annotators (A3) framework. It is trained on A3-Synth, a dataset of high-quality synthetic trajectories generated through a structured teacher-student distillation process.
A3-Qwen3.5-2B is designed to navigate complex web environments by processing visual screenshots and text. By decomposing the synthetic data generation process into three modular roles—Task Designer, Annotator, and Supervisor—the A3 framework allows small, locally deployable models to achieve competitive performance on benchmarks like WebArena, even surpassing some larger closed-source models.
You can evaluate the model using the agent-as-annotators toolkit:
vllm serve --model McGill-NLP/A3-Qwen3.5-2B
a3-eval --benchmark webarena_test --model A3-qwen3.5-2b
If you find this model useful, please cite our work:
@misc{lu2025structured,
title={Structured Distillation of Web Agent Capabilities Enables Generalization},
author={Xing Han Lù and Siva Reddy},
year={2025},
eprint={2604.07776},
archivePrefix={arXiv},
primaryClass={cs.LG}
}