Instructions to use ruotian/ConClick-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ruotian/ConClick-8B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") model = PeftModel.from_pretrained(base_model, "ruotian/ConClick-8B") - Notebooks
- Google Colab
- Kaggle
Add link to paper
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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library_name: peft
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base_model: Qwen/Qwen3-VL-8B-Instruct
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base_model_relation: adapter
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datasets:
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- ruotian/ConClick-Data
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pipeline_tag: image-text-to-text
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tags:
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- gui-grounding
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- qwen3-vl
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- lora
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- conclick
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---
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# ConClick-8B
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Model for **ConClick: Element-wise Contrastive Learning Improves GUI Grounding**. ConClick trains on target–distractor examples with coordinate supervision, listwise ranking, and pairwise margin losses.
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| Inference | ScreenSpot-Pro | MMBench-GUI L2 | OSWorld-G |
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|---|---:|---:|---:|
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| Direct | 66.03 | 86.28 | 70.20 |
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---
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base_model: Qwen/Qwen3-VL-8B-Instruct
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datasets:
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- ruotian/ConClick-Data
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library_name: peft
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license: apache-2.0
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pipeline_tag: image-text-to-text
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tags:
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- gui-grounding
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- qwen3-vl
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- lora
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- conclick
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base_model_relation: adapter
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---
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# ConClick-8B
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Model for **ConClick: Element-wise Contrastive Learning Improves GUI Grounding** ([paper](https://huggingface.co/papers/2610.13268)). ConClick trains on target–distractor examples with coordinate supervision, listwise ranking, and pairwise margin losses.
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| Inference | ScreenSpot-Pro | MMBench-GUI L2 | OSWorld-G |
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|---|---:|---:|---:|
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| Direct | 66.03 | 86.28 | 70.20 |
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