---
license: mit
language:
- en
base_model:
- Tesslate/UIGEN-T2-7B
pipeline_tag: text-generation
library_name: transformers
tags:
- PEFT
- Qwen2
- UI-Generation
- tailwind-css
- html
---
# Tesslate/UIGEN-T2-7B (GGUF Q4) - Sandlogic Lexicons

## Model Overview

**UIGEN-T2-7B** is the latest innovation in the UIGEN model series by Tesslate, engineered specifically for high-quality, design-aware user interface (UI) code generation. Built on the strong foundation of the **Qwen2.5-Coder-7B-Instruct** model and fine-tuned using **PEFT/LoRA** (Rank 128), UIGEN-T2 has been trained on a significantly larger dataset of **50,000 annotated UI samples**. This enables it to generate not only functional HTML and Tailwind CSS code but also code that aligns with usability, design structure, and modern layout principles.

## Model Highlights

- **Architecture**: Based on Qwen2.5-Coder-7B-Instruct (decoder-only transformer)
- **Quantization Format**: GGUF Q4
- **LoRA Fine-Tuning**: PEFT/LoRA (Rank 128), with checkpoints published at each training stage
- **Training Dataset**: 50,000 high-quality UI examples (up from 400 in the previous version)
- **Code Output**: Semantic HTML and utility-first Tailwind CSS
- **UI-Based Reasoning**: Incorporates guidance from a reasoning teacher model to ensure usability and aesthetics
- **Chat Interface**: Improved prompt interaction for seamless developer experience

## Intended Use Cases

### Recommended Applications

- **Rapid UI Prototyping**  
  Generate clean, production-ready HTML/Tailwind code from natural language descriptions or wireframes.

- **Component Generation**  
  Create both standard (buttons, forms, cards) and custom components based on user-defined design goals.

- **Frontend Development Assistant**  
  Accelerate development by producing baseline layouts and component structures for web interfaces.

- **Design-to-Code Exploration**  
  Bridge the gap between visual design and code implementation, especially helpful for non-developers or design teams.

## Model Integration

This quantized **Q4 GGUF** version of UIGEN-T2-7B is now part of the [Sandlogic Lexicons](https://huggingface.co/SandLogicTechnologies) model zoo. It is optimized for efficient inference on edge and constrained environments, without compromising on code quality or reasoning capability.