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Model: smirki/UIGEN-T1.1-Qwen-14B Source: Original Platform
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README.md
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README.md
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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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