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--- |
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license: apache-2.0 |
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datasets: |
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- smirki/UI_REASONING_v1.01 |
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language: |
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- en |
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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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- code |
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- ui |
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- generation |
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- uigen |
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library_name: transformers |
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--- |
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# **Model Card for UIGEN-T1.1** |
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New and Improved reasoning traces. Better ui generation. Smarter decisions. Better code generation! Trained on a 700+ dataset. |
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USE BUDGET FORCING (putting the word answer or think at the end of the assistant generation to keep generationg more thinking and use 'answer' to write code.) |
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SFT on a 4090 for 4 hours. |
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## **Model Summary** |
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UIGEN-T1.1 is a **14-billion parameter transformer model** fine-tuned on **Qwen2.5-Coder-14B-Instruct**. It is designed for **reasoning-based UI generation**, leveraging a complex chain-of-thought approach to produce **robust HTML and CSS-based UI components**. Currently, it is limited to **basic applications such as dashboards, landing pages, and sign-up forms**. |
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## **Model Details** |
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### **Model Description** |
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UIGEN-T1.1 generates **HTML and CSS-based UI layouts** by reasoning through design principles. While it has a strong **chain-of-thought reasoning process**, it is currently **limited to text-based UI elements and simpler frontend applications**. The model **excels at dashboards, landing pages, and sign-up forms**, but **lacks advanced interactivity** (e.g., JavaScript-heavy functionalities). |
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- **Developed by:** [smirki](https://huggingface.co/smirki) |
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- **Shared by:** [smirki](https://huggingface.co/smirki) |
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- **Model type:** Transformer-based |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** Qwen2.5-Coder-14B-Instruct |
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### **Model Sources** |
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- **Repository:** (Will be uploaded to GitHub soon) |
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- **Hosted on:** [Hugging Face](https://huggingface.co/smirki) |
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- **Demo:** Coming soon |
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## **Uses** |
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### **Direct Use** |
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- Generates HTML and CSS code for **basic UI elements** |
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- Best suited for **dashboards, landing pages, and sign-up forms** |
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- Requires **manual post-processing** to refine UI outputs |
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- **May require using the word "answer" at the end of the input prompt** to get better inference |
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### **Downstream Use (optional)** |
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- Can be fine-tuned further for **specific frontend frameworks (React, Vue, etc.)** |
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- May be integrated into **no-code/low-code UI generation tools** |
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### **Out-of-Scope Use** |
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- Not suitable for **complex frontend applications** involving JavaScript-heavy interactions |
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- May not generate **fully production-ready** UI code |
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- **Limited design variety** – biased towards **basic frontend layouts** |
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## **Bias, Risks, and Limitations** |
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### **Biases** |
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- **Strong bias towards basic frontend design patterns** (may not generate creative or advanced UI layouts) |
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- **May produce repetitive designs** due to limited training scope |
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### **Limitations** |
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- **Artifacting issues**: Some outputs may contain formatting artifacts |
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- **Limited generalization**: Performs best in **HTML + CSS UI generation**, but **not robust for complex app logic** |
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- **May require prompt engineering** (e.g., adding "answer" to input for better results) |
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## **How to Get Started with the Model** |
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### **Example Model Template** |
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```plaintext |
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<|im_start|>user |
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{question}<|im_end|> |
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<|im_start|>assistant |
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<|im_start|>think |
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{reasoning}<|im_end|> |
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<|im_start|>answer |
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``` |
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### **Basic Inference Code** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "smirki/UIGEN-T1.1-14B" |
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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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Make a dark-themed dashboard for an oil rig.<|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) #max tokens has to be greater than 12k |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## **Training Details** |
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### **Training Data** |
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- **Based on:** Qwen2.5-Coder-14B-Instruct |
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- **Fine-tuned on:** UI-related datasets with reasoning-based HTML/CSS examples |
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### **Training Procedure** |
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- **Preprocessing:** Standard text-tokenization using Hugging Face transformers |
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- **Training Precision:** **bf16 mixed precision** quantized to q8 |
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## **Evaluation** |
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### **Testing Data, Factors & Metrics** |
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- **Testing Data:** Internal UI design-related datasets |
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- **Evaluation Factors:** Bias towards basic UI components, robustness in reasoning, output quality |
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- **Metrics:** Subjective evaluation based on UI structure, correctness, and usability |
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### **Results** |
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- **Strengths:** |
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- **Good at reasoning-based UI layouts** |
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- **Generates structured and valid HTML/CSS** |
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- **Weaknesses:** |
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- **Limited design diversity** |
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- **Artifacting in outputs** |
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## **Technical Specifications** |
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### **Model Architecture and Objective** |
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- **Architecture:** Transformer-based LLM fine-tuned for UI reasoning |
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- **Objective:** Generate **robust frontend UI layouts with chain-of-thought reasoning** |
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### **Compute Infrastructure** |
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- **Hardware Requirements:** 12GB VRAM reccomended |
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- **Software Requirements:** |
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- Transformers library (Hugging Face) |
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- PyTorch |
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## **Citation** |
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If using this model, please cite: |
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**BibTeX:** |
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```bibtex |
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@misc{smirki_UIGEN-T1.1, |
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title={UIGEN-T1.1.1: 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/smirki/UIGEN-T1.11} |
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} |
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``` |
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## **More Information** |
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- **GitHub Repository:** (Coming soon) |
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- **Web Demo:** (Coming soon) |
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## **Model Card Authors** |
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- **Author:** smirki |
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## **Model Card Contact** |
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- **Contact:** [smirki on Hugging Face](https://huggingface.co/smirki) |
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--- |
