Raptor-X5-UIGEN

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:

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.