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| title: Adaptive-K Demo | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 4.31.0 | |
| python_version: 3.11 | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: Dynamic Expert Selection for MoE Models | |
| tags: | |
| - moe | |
| - mixture-of-experts | |
| - inference-optimization | |
| - llm | |
| - adaptive-routing | |
| # Adaptive-K: Dynamic Expert Selection for MoE | |
| This demo shows how **Adaptive-K** dynamically selects the number of experts based on input complexity, reducing inference costs by 30-50% while maintaining accuracy. | |
| ## How it works | |
| 1. **Input text** → Router computes routing probabilities | |
| 2. **Entropy calculation** → Measures router uncertainty | |
| 3. **K selection** → Low entropy = fewer experts, High entropy = more experts | |
| 4. **Cost savings** → Visualize the compute reduction | |
| ## Links | |
| - 📄 [Paper](https://github.com/Gabrobals/sbm-efficient/blob/master/Entropy_Guided_Dynamic_Expert_Selection_in_Mixture_of_Experts_Models.pdf) | |
| - 📖 [Whitepaper](https://adaptive-k.vertexdata.it/whitepaper.html) | |
| - 💻 [GitHub](https://github.com/Gabrobals/sbm-efficient) | |
| - 📦 [PyPI](https://pypi.org/project/adaptive-k-routing/) | |