Upload Qwen-Click-DiT model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,82 @@
+---
+license: mit
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+datasets:
+- Salesforce/grounding_dataset
+tags:
+- vision-language
+- click-prediction
+- gui-grounding
+- diffusion-transformer
+- flow-matching
+pipeline_tag: image-to-text
+---
+
+# Qwen-Click-DiT
+
+Vision-Language Model with Diffusion Transformer for GUI Click Prediction.
+
+## Model Description
+
+This model predicts click coordinates given a screenshot and natural language instruction. It uses:
+- **Qwen2.5-VL-3B** as a frozen vision-language backbone
+- **DiT (Diffusion Transformer)** action head using flow matching
+
+## Usage
+
+```python
+from transformers import AutoProcessor, AutoConfig
+from src.model import Qwen2_5_VLForClickPrediction
+
+# Load model
+config = AutoConfig.from_pretrained("TESS-Computer/qwen-click-dit")
+model = Qwen2_5_VLForClickPrediction.from_pretrained("TESS-Computer/qwen-click-dit")
+processor = AutoProcessor.from_pretrained("TESS-Computer/qwen-click-dit")
+
+# Predict
+from PIL import Image
+image = Image.open("screenshot.png")
+# ... process and predict
+```
+
+See [GitHub repo](https://github.com/TESS-Computer/qwen-click-dit) for full inference code.
+
+## Metrics
+
+| Metric | Value |
+|--------|-------|
+| Inside-box Accuracy | 0.0% |
+| Mean Distance | 0.4397 |
+| Acc @ 5% | 0.0% |
+| Acc @ 10% | 0.0% |
+
+## Training
+
+- **Dataset**: [Salesforce/grounding_dataset](https://huggingface.co/datasets/Salesforce/grounding_dataset)
+- **Samples**: 20,000
+- **Epochs**: 3
+- **Base Model**: [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
+
+## Architecture
+
+| Component | Value |
+|-----------|-------|
+| DiT Hidden Size | 512 |
+| DiT Layers | 6 |
+| DiT Heads | 8 |
+| Inference Steps | 16 |
+
+## Citation
+
+```bibtex
+@misc{lezzaik2025qwenclickdit,
+  title = {Qwen-Click-DiT: Vision-Language Model with Diffusion Transformer for GUI Click Prediction},
+  author = {Lezzaik, Hussein},
+  year = {2025},
+  howpublished = {\url{https://github.com/HusseinLezzaik/qwen-click-dit}},
+}
+```
+
+## License
+
+MIT

added_tokens.json

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chat_template.jinja

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+{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

config.json

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