metadata
base_model:
- Tesslate/UIGEN-X-4B-0729
- Menlo/Jan-nano
library_name: transformers
tags:
- mergekit
- merge
🧠 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
✨ 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
🧪 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 |
🔧 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)
🛠 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
📘 License & Credit
Model licensed under Apache 2.0
All credit to the original base models: