Raptor X5 UIGEN

🚀 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

1. Advanced UI Design Support: It excels at generating modern, clean, and minimalistic UI designs with structured components.
2. Content - Rich Coding: Provides optimized code for front - end and back - end development, ensuring a clean and efficient structure.
3. Minimalist Coding Approach: Supports multiple programming languages, emphasizing simplicity, maintainability, and efficiency.
4. Enhanced Instruction Following: Improves the understanding and execution of complex prompts, generating structured and coherent responses.
5. 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.

🚀 Quick Start

📦 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:

💻 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]

📚 Documentation

Intended Use

1. UI/UX Design Assistance: Ideal for generating UI layouts, component structures, and front - end frameworks.
2. Minimalist and Content - Rich Coding: Generates clean, optimized, and maintainable code for front - end and back - end applications.
3. Programming Assistance: Supports multiple languages with a focus on structured, reusable code.
4. Educational and Informational Assistance: Suitable for developers, designers, and technical writers needing structured insights.
5. Conversational AI for Technical Queries: Builds intelligent bots that answer coding, UI/UX, and design - related questions.
6. Long - Form Technical Content Generation: Produces structured technical documentation, UI/UX design guides, and best practices.

Limitations

1. Hardware Requirements: Requires high - memory GPUs or TPUs due to its large parameter size and long - context processing.
2. Potential Bias in Responses: While trained for neutrality, responses may still reflect biases present in the training data.
3. Variable Output in Open - Ended Tasks: May generate inconsistent outputs in highly subjective or creative tasks.
4. Limited Real - World Awareness: Lacks access to real - time events beyond its training cutoff.
5. Error Propagation in Extended Outputs: Minor errors in early responses may affect overall coherence in long - form explanations.
6. Prompt Sensitivity: Response quality depends on well - structured input prompts.

📄 License

This project is licensed under the Apache - 2.0 license.

📦 Additional Information