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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Tesslate/UIGEN-T3-4B-Preview
Input Modalities
Output Modalities
Supported Functionality
UI-Oriented Abliterated Reasoning Controlled reasoning precision tailored for frontend development and code generation, with polished token distributions ensuring structured, maintainable output.
Web UI Component Generation Excels at generating responsive components, semantic HTML, and Tailwind-based layouts with reasoning-aware structure and minimal boilerplate.
Layout-Aware Structured Logic Understands UI state flows, component hierarchies, and responsive design patterns, producing logically consistent, production-ready UI code.
Hybrid Reasoning for Code Combines symbolic reasoning with probabilistic inference to deliver optimized component logic, conditional rendering, and event-driven UI behavior.
Structured Output Mastery Natively outputs in HTML, React, Markdown, JSON, and YAML, making it ideal for UI prototyping, design systems, and documentation generation.
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.
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)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)