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
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UI-Oriented Abliterated Reasoning Controlled reasoning precision tailored for frontend development and code generation, with polished token distributions ensuring structured, maintainable output.
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Web UI Component Generation Excels at generating responsive components, semantic HTML, and Tailwind-based layouts with reasoning-aware structure and minimal boilerplate.
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Layout-Aware Structured Logic Understands UI state flows, component hierarchies, and responsive design patterns, producing logically consistent, production-ready UI code.
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Hybrid Reasoning for Code Combines symbolic reasoning with probabilistic inference to deliver optimized component logic, conditional rendering, and event-driven UI behavior.
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Structured Output Mastery Natively outputs in HTML, React, Markdown, JSON, and YAML, making it ideal for UI prototyping, design systems, and documentation generation.
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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