README.md

---
license: apache-2.0
language:
- en
- zh
library_name: transformers
tags:
- trl
- gpt_oss
- code
- ui
- web
- .tsx
- .html
- .css
- abliterated
- text-generation-inference
- web-ui
base_model:
- Tesslate/UIGEN-T3-4B-Preview
pipeline_tag: text-generation
---

![2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/P-LIpWdt5ypMGIfE5kJiK.png)


# **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](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**

```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 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