yasserrmd/AgentUX-4B

🧠 AgentUX‑4B

AgentUX‑4B is a compact, agentic reasoning model designed for UI layout generation, component reasoning, and lightweight code structuring tasks. It’s a 4B-parameter model merged using SLERP (Spherical Linear Interpolation) via MergeKit, combining:

• 🔷 60% Tesslate/UIGEN-X-4B-0729 — excellent at UI understanding and structured generation
• 🔹 40% Menlo/Jan-nano — strong generalist with compact tool-use and agentic reasoning

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✨ Highlights

• 📐 UI reasoning & layout structure understanding
• 🧩 Component-to-code generation (HTML, JSX, CSS fragments)
• 🧠 Compact agentic planning and multi-step reasoning
• ⚡ Lightweight & merge-optimized for local inference and real-time apps
• 🧬 Merged using SLERP to preserve semantic smoothness between sources

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🧪 Example Use Cases

Prompt	Task
"Generate a signup form layout using HTML and CSS"	Frontend layout generation
"Explain the role of flex-wrap in UI design"	UI reasoning
"Plan 3 steps to build a sidebar menu using React"	Agentic decomposition

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🔧 Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "yasserrmd/AgentUX-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Create a responsive layout with sidebar and header using Flexbox."
response = pipe(prompt, max_new_tokens=512)[0]["generated_text"]
print(response)

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🛠 Merge Details

• 🔗 MergeKit method: slerp
• 🔍 Focused on reasoning alignment between structured generation (UIGEN) and agent-style planning (Jan-nano)
• 🤖 No additional fine-tuning post-merge

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📘 License & Credit

• Model licensed under Apache 2.0

• All credit to the original base models:

  • Tesslate/UIGEN-X-4B-0729
  • Menlo/Jan-nano

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