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---
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
---
*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:*
*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! |