100 lines
4.3 KiB
Markdown
100 lines
4.3 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- Tesslate/UIGEN-T1.5-Dataset
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- Tesslate/Tessa-T1-Dataset
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- KingstarOMEGA/HTML-CSS-UI
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- Juliankrg/HTML_CSS_CodeDataSet_100k
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language:
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- en
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base_model:
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- prithivMLmods/Viper-Coder-v1.7-Vsm6
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- X5
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- GEN
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- UI
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- Coder
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---
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# **Raptor-X5-UIGEN**
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> [!NOTE]
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> 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.
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## **Key Improvements**
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1. **Advanced UI Design Support**: Excels in generating modern, clean, and minimalistic UI designs with structured components.
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2. **Content-Rich Coding**: Provides optimized code for front-end and back-end development, ensuring clean and efficient structure.
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3. **Minimalist Coding Approach**: Supports multiple programming languages, focusing on simplicity, maintainability, and efficiency.
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4. **Enhanced Instruction Following**: Improves understanding and execution of complex prompts, generating structured and coherent responses.
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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.
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## **Quickstart with transformers**
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Raptor-X5-UIGEN"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Generate a minimalistic UI layout for a dashboard."
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messages = [
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{"role": "system", "content": "You are an expert in UI design, minimalist coding, and structured programming."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## **Intended Use**
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1. **UI/UX Design Assistance**:
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Ideal for generating UI layouts, component structures, and front-end frameworks.
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2. **Minimalist and Content-Rich Coding**:
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Generates clean, optimized, and maintainable code for front-end and back-end applications.
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3. **Programming Assistance**:
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Supports multiple languages with a focus on structured, reusable code.
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4. **Educational and Informational Assistance**:
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Suitable for developers, designers, and technical writers needing structured insights.
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5. **Conversational AI for Technical Queries**:
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Builds intelligent bots that answer coding, UI/UX, and design-related questions.
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6. **Long-Form Technical Content Generation**:
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Produces structured technical documentation, UI/UX design guides, and best practices.
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## **Limitations**
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1. **Hardware Requirements**:
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Requires high-memory GPUs or TPUs due to its large parameter size and long-context processing.
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2. **Potential Bias in Responses**:
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While trained for neutrality, responses may still reflect biases present in the training data.
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3. **Variable Output in Open-Ended Tasks**:
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May generate inconsistent outputs in highly subjective or creative tasks.
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4. **Limited Real-World Awareness**:
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Lacks access to real-time events beyond its training cutoff.
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5. **Error Propagation in Extended Outputs**:
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Minor errors in early responses may affect overall coherence in long-form explanations.
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6. **Prompt Sensitivity**:
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Response quality depends on well-structured input prompts. |