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--- |
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base_model: |
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- Qwen/Qwen2.5-Coder-14B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- qwen2 |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- Tesslate/UIGEN-T1.5-Dataset |
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--- |
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*Landing page showcasing visual richness* |
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# **Model Card for UIGEN-T1.5** |
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--- |
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## **Model Overview** |
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UIGEN-T1.5 is an advanced transformer-based UI generation model fine-tuned from **Qwen2.5-Coder-14B-Instruct**, specifically enhanced to produce stunning, modern, and unique frontend user interfaces. Leveraging sophisticated reasoning and chain-of-thought methodologies, UIGEN-T1.5 excels at generating highly structured and visually compelling HTML and CSS code, ideal for sleek dashboards, engaging landing pages, and intuitive sign-up forms. |
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--- |
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## **Model Highlights** |
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- **Advanced UI Styles**: Produces sleek, modern, and unique designs. |
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- **Chain-of-Thought Reasoning**: Enhanced reasoning capabilities for accurate HTML/CSS layouts. |
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- **High Usability**: Generates responsive and production-ready frontend code. |
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--- |
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## **Visual Examples** |
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*See examples below showcasing UIGEN-T1.5-generated interfaces:* |
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*Dashboard UI generated by UIGEN-T1.5* |
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## **Use Cases** |
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### **Recommended Uses** |
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- **Dashboards**: Insightful and visually appealing data interfaces. |
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- **Landing Pages**: Captivating and high-conversion web pages. |
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- **Authentication Screens**: Elegant sign-up and login interfaces. |
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### **Limitations** |
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- **Limited Interactivity**: Minimal JavaScript functionality, focusing on HTML/CSS. |
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- **Prompt Engineering**: May require specific prompts (e.g., appending "answer"). |
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--- |
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## **How to Use** |
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### **Inference Example** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "smirki/UIGEN-T1.5" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") |
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prompt = """<|im_start|>user |
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Design a sleek, modern dashboard for monitoring solar panel efficiency.<|im_end|> |
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<|im_start|>assistant |
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<|im_start|>think |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=12012, do_sample=True, temperature=0.7) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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--- |
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## **Performance and Evaluation** |
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- **Strengths**: |
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- High-quality UI generation. |
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- Strong reasoning capabilities for structured layouts. |
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- **Weaknesses**: |
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- Occasional repetitive design patterns. |
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- Minor artifacting in complex designs. |
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--- |
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## **Technical Specifications** |
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- **Architecture**: Transformer-based LLM |
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- **Base Model**: Qwen2.5-Coder-7B-Instruct |
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- **Precision**: bf16 mixed precision, quantized to q8 |
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- **Hardware Requirements**: Recommended 12GB VRAM |
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- **Software Dependencies**: |
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- Hugging Face Transformers |
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- PyTorch |
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--- |
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## **Citation** |
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```bibtex |
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@misc{Tesslate_UIGEN-T1.5, |
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title={UIGEN-T1.5: Advanced Chain-of-Thought UI Generation Model}, |
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author={smirki}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/Tesslate/UIGEN-T1.5} |
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} |
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``` |
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--- |
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## **Contact & Community** |
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- **Creator:** [smirki](https://huggingface.co/Tesslate) |
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- **Repository & Demo**: Coming soon! |
