126 lines
3.4 KiB
Markdown
126 lines
3.4 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-Coder-3B-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-3B-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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---
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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-3B-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!
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**Sponsored by vichar ai [Huggingface](https://huggingface.co/vicharai) [Website](https://vichar.io)** |