Muscae-Qwen3-UI-Code-4B

Muscae-Qwen3-UI-Code-4B

Muscae-Qwen3-UI-Code-4B is a web-UI-focused model fine-tuned on UIGEN-T3-4B-Preview (built upon Qwen3-4B) for controlled Abliterated Reasoning and polished token probabilities, designed exclusively for experimental use. It excels at modern web UI coding tasks, structured component generation, and layout-aware reasoning, making it ideal for frontend developers, UI engineers, and research prototypes exploring structured code generation.

[!note] GGUF: https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF

Key Features

1. UI-Oriented Abliterated Reasoning Controlled reasoning precision tailored for frontend development and code generation, with polished token distributions ensuring structured, maintainable output.

2. Web UI Component Generation Excels at generating responsive components, semantic HTML, and Tailwind-based layouts with reasoning-aware structure and minimal boilerplate.

3. Layout-Aware Structured Logic Understands UI state flows, component hierarchies, and responsive design patterns, producing logically consistent, production-ready UI code.

4. Hybrid Reasoning for Code Combines symbolic reasoning with probabilistic inference to deliver optimized component logic, conditional rendering, and event-driven UI behavior.

5. Structured Output Mastery Natively outputs in HTML, React, Markdown, JSON, and YAML, making it ideal for UI prototyping, design systems, and documentation generation.

6. Optimized Lightweight Footprint With a 4B parameter size, it’s deployable on mid-range GPUs, offline workstations, or edge devices while retaining strong UI coding capabilities.

Quickstart with Transformers

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 landing page hero section with Tailwind and semantic HTML."

messages = [
    {"role": "system", "content": "You are a frontend coding assistant skilled in UI generation, semantic HTML, and component structuring."},
    {"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 coding and component generation
• Responsive layout and frontend architecture prototyping
• Semantic HTML, Tailwind, and React code generation
• Research and experimental projects on structured code synthesis
• Design-system-driven development workflows

Limitations

• Experimental model – not optimized for production-critical deployments
• Focused on UI coding – not suitable for general reasoning or creative writing
• May produce inconsistent results with very long prompts or cross-framework tasks
• Prioritizes structure and correctness over stylistic creativity or verbosity