README.md

---
license: apache-2.0
---

# **Raptor-X5-UIGEN**

> [!NOTE]    
> Raptor-X5-UIGEN is based on the Qwen 2.5 14B modality architecture, designed to enhance reasoning capabilities in UI design, minimalist coding, and content-rich development. This model is optimized for structured reasoning, logical deduction, and multi-step computations. It has been fine-tuned using advanced chain-of-thought reasoning techniques and specialized datasets to improve comprehension, structured responses, and computational intelligence.

## **Key Improvements**
1. **Advanced UI Design Support**: Excels in 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 clean and efficient structure.
3. **Minimalist Coding Approach**: Supports multiple programming languages, focusing on simplicity, maintainability, and efficiency.
4. **Enhanced Instruction Following**: Improves understanding and execution of complex prompts, generating structured and coherent responses.
5. **Long-Context Support**: Handles up to 128K tokens for input and generates up to 8K tokens in output, suitable for detailed analysis and documentation.

## **Quickstart with transformers**

Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:

```python
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]
```

## **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.