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UIGEN T1 Qwen 7b
Overview :
UIGEN-T1-Qwen-7b is a large language model specializing in UI inference and generation. Utilizing a sophisticated reasoning chain approach, it generates HTML and CSS-based UI components, offering front-end development teams a fast layout generation solution. Fine-tuned from Qwen2.5-Coder-7B-Instruct, the model excels at generating basic front-end applications like dashboards, login pages, and registration forms. Its key advantage lies in its ability to rapidly produce structured HTML/CSS code and, through reasoning, create UI layouts that adhere to design principles. The primary use cases include streamlining front-end development workflows, boosting development efficiency, and providing support for low-code/no-code tools.
Target Users :
This model empowers front-end developers, low-code/no-code tool developers, and teams seeking rapid UI layout generation. For front-end developers, it accelerates development by generating initial code frameworks. For low-code/no-code platforms, it serves as a robust back-end model, enabling instant UI creation for users.
Use Cases
Generate a dark-themed dashboard for an oil rig.
Create a login page for showcasing products.
Generate a user registration page with form validation.
Features
Generates HTML and CSS code for creating basic UI elements.
Supports the creation of simple front-end applications, such as dashboards, login pages, and registration forms.
Produces structured and valid HTML/CSS layouts through a reasoning chain.
Can be further fine-tuned to adapt to specific front-end frameworks (e.g., React, Vue).
Supports manual post-processing for optimizing UI output.
How to Use
1. **Set up your development environment:** Install Python and necessary libraries, such as transformers and PyTorch.
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2. **Load the model:** Use the Hugging Face transformers library to load the UIGEN-T1-Qwen-7b model.
3. **Craft your input prompt:** Write a concise prompt describing your desired UI, e.g., 'Make a dark-themed dashboard for an oil rig.'
4. **Generate the code:** Pass the input prompt to the model and configure generation parameters such as maximum length and sampling temperature.
5. **Post-process the output:** Manually refine the generated HTML/CSS code to meet specific requirements.
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