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
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
@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
- Repository & Demo: Coming soon!
Sponsored by vichar ai Huggingface Website