Update README.md

README.md

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----
-license: apache-2.0
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+---
+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.
+6. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+## **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.