đ Raptor-X5-UIGEN
Raptor-X5-UIGEN is based on the Qwen 2.5 14B modality architecture, aiming to enhance reasoning capabilities in UI design, minimalist coding, and content - rich development. It offers optimized performance in structured reasoning, logical deduction, and multi - step computations.

⨠Features
- Advanced UI Design Support: It excels at generating modern, clean, and minimalistic UI designs with structured components.
- Content - Rich Coding: Provides optimized code for front - end and back - end development, ensuring a clean and efficient structure.
- Minimalist Coding Approach: Supports multiple programming languages, emphasizing simplicity, maintainability, and efficiency.
- Enhanced Instruction Following: Improves the understanding and execution of complex prompts, generating structured and coherent responses.
- Long - Context Support: Can handle up to 128K tokens for input and generate up to 8K tokens in output, suitable for detailed analysis and documentation.
đ Quick Start
đĻ Installation
The model can be loaded using the transformers
library. Here is a code snippet with apply_chat_template
to show you how to load the tokenizer and model and generate content:
đģ Usage Examples
Basic Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Raptor-X5-UIGEN"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Generate a minimalistic UI layout for a dashboard."
messages = [
{"role": "system", "content": "You are an expert in UI design, minimalist coding, and structured programming."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
đ Documentation
Intended Use
- UI/UX Design Assistance: Ideal for generating UI layouts, component structures, and front - end frameworks.
- Minimalist and Content - Rich Coding: Generates clean, optimized, and maintainable code for front - end and back - end applications.
- Programming Assistance: Supports multiple languages with a focus on structured, reusable code.
- Educational and Informational Assistance: Suitable for developers, designers, and technical writers needing structured insights.
- Conversational AI for Technical Queries: Builds intelligent bots that answer coding, UI/UX, and design - related questions.
- Long - Form Technical Content Generation: Produces structured technical documentation, UI/UX design guides, and best practices.
Limitations
- Hardware Requirements: Requires high - memory GPUs or TPUs due to its large parameter size and long - context processing.
- Potential Bias in Responses: While trained for neutrality, responses may still reflect biases present in the training data.
- Variable Output in Open - Ended Tasks: May generate inconsistent outputs in highly subjective or creative tasks.
- Limited Real - World Awareness: Lacks access to real - time events beyond its training cutoff.
- Error Propagation in Extended Outputs: Minor errors in early responses may affect overall coherence in long - form explanations.
- Prompt Sensitivity: Response quality depends on well - structured input prompts.
đ License
This project is licensed under the Apache - 2.0 license.
đĻ Additional Information
Property |
Details |
Model Type |
Text Generation |
Training Datasets |
Tesslate/UIGEN - T1.5 - Dataset, Tesslate/Tessa - T1 - Dataset, KingstarOMEGA/HTML - CSS - UI, Juliankrg/HTML_CSS_CodeDataSet_100k |
Base Model |
prithivMLmods/Viper - Coder - v1.7 - Vsm6 |
Library Name |
transformers |
Tags |
text - generation - inference, X5, GEN, UI, Coder |