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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# **Muscae-Qwen3-UI-Code-4B**
+
+> **Muscae-Qwen3-UI-Code-4B** is a reasoning-enhanced model fine-tuned on **Qwen** using the **GPT-OSS Web UI Coding dataset traces**, specializing in **web interface coding**, **structured generation**, and **polished token probabilities**.
+> It excels at generating **production-grade UI components**, **frontend layouts**, and **logic-driven interface code** with high precision and consistency.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF](https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF)
+
+
+## **Key Features**
+
+1. **UI-Focused Reasoning Engine**
+   Fine-tuned for precise **frontend development workflows**, generating optimized HTML, CSS, React, and Tailwind-based code with minimal refactoring needs.
+
+2. **Web Interface Generation Mastery**
+   Excels in building responsive layouts, interactive components, and dashboard UIs directly from natural language prompts or wireframe descriptions.
+
+3. **Polished Token Probabilities**
+   Trained for smoother generation curves and deterministic structure in code, minimizing syntax errors and enhancing readability.
+
+4. **Hybrid Logic-Coding Synthesis**
+   Combines structural reasoning with frontend logic understanding to generate UI code that’s both **functional** and **aesthetically consistent**.
+
+5. **Structured Output Formats**
+   Outputs code and structured data in **HTML**, **React (JSX/TSX)**, **Tailwind**, **JSON**, and **YAML**, supporting full-stack workflows and CI/CD pipelines.
+
+6. **Optimized Lightweight Footprint**
+   Compact **4B parameter size**, deployable on **mid-range GPUs**, **developer workstations**, and **edge build servers** while maintaining high-quality UI generation.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Muscae-Qwen3-UI-Code-4B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Generate a responsive React dashboard with a sidebar and top navigation bar using Tailwind CSS."
+
+messages = [
+    {"role": "system", "content": "You are a frontend coding assistant skilled in web UI generation and responsive design."},
+    {"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]
+print(response)
+```
+
+## **Intended Use**
+
+* Web UI component generation and layout scaffolding
+* Responsive dashboard, landing page, and frontend application coding
+* Educational and research tasks related to frontend development
+* Lightweight deployment in developer environments and CI/CD pipelines
+* Structured code generation and UI prototyping from natural language prompts
+
+## **Limitations**
+
+* Focused on **UI and frontend code generation** — not suited for deep backend logic or non-UI tasks
+* Might require minor manual adjustments for large-scale production apps
+* Prioritizes structured and readable code over creative design experimentation
+* Performance may vary with **extremely long code contexts** or multi-file full-stack generation tasks