109 lines
3.9 KiB
Markdown
109 lines
3.9 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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- zh
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library_name: transformers
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tags:
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- trl
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- gpt_oss
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- code
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- ui
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- web
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- .tsx
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- .html
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- .css
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- abliterated
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- text-generation-inference
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- web-ui
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base_model:
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- Tesslate/UIGEN-T3-4B-Preview
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pipeline_tag: text-generation
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---
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# **Muscae-Qwen3-UI-Code-4B**
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> **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**.
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> 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.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF](https://huggingface.co/prithivMLmods/Muscae-Qwen3-UI-Code-4B-GGUF)
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## **Key Features**
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1. **UI-Oriented Abliterated Reasoning**
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Controlled reasoning precision tailored for frontend development and code generation, with polished token distributions ensuring structured, maintainable output.
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2. **Web UI Component Generation**
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Excels at generating **responsive components**, **semantic HTML**, and **Tailwind-based layouts** with reasoning-aware structure and minimal boilerplate.
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3. **Layout-Aware Structured Logic**
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Understands **UI state flows**, **component hierarchies**, and **responsive design patterns**, producing logically consistent, production-ready UI code.
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4. **Hybrid Reasoning for Code**
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Combines symbolic reasoning with probabilistic inference to deliver optimized component logic, conditional rendering, and event-driven UI behavior.
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5. **Structured Output Mastery**
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Natively outputs in **HTML**, **React**, **Markdown**, **JSON**, and **YAML**, making it ideal for UI prototyping, design systems, and documentation generation.
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6. **Optimized Lightweight Footprint**
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With a **4B parameter size**, it’s deployable on **mid-range GPUs**, **offline workstations**, or **edge devices** while retaining strong UI coding capabilities.
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Muscae-Qwen3-UI-Code-4B"
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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 responsive landing page hero section with Tailwind and semantic HTML."
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messages = [
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{"role": "system", "content": "You are a frontend coding assistant skilled in UI generation, semantic HTML, and component structuring."},
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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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print(response)
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```
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## **Intended Use**
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* Web UI coding and component generation
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* Responsive layout and frontend architecture prototyping
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* Semantic HTML, Tailwind, and React code generation
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* Research and experimental projects on structured code synthesis
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* Design-system-driven development workflows
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## **Limitations**
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* Experimental model – not optimized for production-critical deployments
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* Focused on **UI coding** – not suitable for general reasoning or creative writing
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* May produce inconsistent results with **very long prompts** or **cross-framework tasks**
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* Prioritizes structure and correctness over stylistic creativity or verbosity
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