---
base_model:
- Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- text-generation-inference
- transformers
- qwen2
- trl
license: apache-2.0
language:
- en
datasets:
- Tesslate/UIGEN-T1.5-Dataset
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/bCWPiMgNitTrrONN3SVpw.png)
*Landing page showcasing visual richness*

# **Model Card for UIGEN-T1.5**

---

## **Model Overview**

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.

---

## **Model Highlights**

- **Advanced UI Styles**: Produces sleek, modern, and unique designs.
- **Chain-of-Thought Reasoning**: Enhanced reasoning capabilities for accurate HTML/CSS layouts.
- **High Usability**: Generates responsive and production-ready frontend code.

---

## **Visual Examples**

*See examples below showcasing UIGEN-T1.5-generated interfaces:*


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/T4nz0JBAvYdwPVpZ_sb3g.png)
*Dashboard UI generated by UIGEN-T1.5*


---

## **Use Cases**

### **Recommended Uses**
- **Dashboards**: Insightful and visually appealing data interfaces.
- **Landing Pages**: Captivating and high-conversion web pages.
- **Authentication Screens**: Elegant sign-up and login interfaces.

### **Limitations**
- **Limited Interactivity**: Minimal JavaScript functionality, focusing on HTML/CSS.
- **Prompt Engineering**: May require specific prompts (e.g., appending "answer").

---

## **How to Use**

### **Inference Example**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "smirki/UIGEN-T1.5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

prompt = """<|im_start|>user
Design a sleek, modern dashboard for monitoring solar panel efficiency.<|im_end|>
<|im_start|>assistant
<|im_start|>think
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=12012, do_sample=True, temperature=0.7)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## **Performance and Evaluation**

- **Strengths**:
  - High-quality UI generation.
  - Strong reasoning capabilities for structured layouts.

- **Weaknesses**:
  - Occasional repetitive design patterns.
  - Minor artifacting in complex designs.

---

## **Technical Specifications**

- **Architecture**: Transformer-based LLM
- **Base Model**: Qwen2.5-Coder-7B-Instruct
- **Precision**: bf16 mixed precision, quantized to q8
- **Hardware Requirements**: Recommended 12GB VRAM
- **Software Dependencies**:
  - Hugging Face Transformers
  - PyTorch

---

## **Citation**

```bibtex
@misc{Tesslate_UIGEN-T1.5,
  title={UIGEN-T1.5: Advanced Chain-of-Thought UI Generation Model},
  author={smirki},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Tesslate/UIGEN-T1.5}
}
```

---

## **Contact & Community**
- **Creator:** [smirki](https://huggingface.co/Tesslate)
- **Repository & Demo**: Coming soon!