🚀 EfficientAI

Efficient Inference for LLMs & MLLMs
An open-source research project from Alibaba Cloud dedicated to efficient large language model inference.

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📋 Table of Contents

• ✨ Key Features
• 🔥 Latest Updates
• 📦 Installation
• ⚡ Quick Start
• 🧪 Benchmarks
• 📚 Publications
• 🤝 Contributing
• 📄 License
• ✉️ Contact

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✨ Key Features

EfficientAI focuses on inference-time optimizations for LLMs and MLLMs:

Feature	Description	Status
🔹 Activation Sparsity	Dynamic sparsity methods for faster inference	✅ LaRoSa (ICML 2025)
🔹 Quantization	Post-training & quantization-aware techniques for MLLMs	✅ MASQuant (CVPR 2026)
🔹 Agentic Reasoning	Efficient tool-use and reasoning frameworks	✅ D-CORE
🔹 Reproducible Benchmarks	Standardized eval pipelines for research & production	🔄 In Progress

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🔥 Latest Updates

📰 Changelog (Click to expand)

• [2026-03] 🎉 MASQuant accepted to CVPR 2026
  → Multimodal LLM PTQ algorithm with SOTA accuracy-efficiency tradeoff
  📄 Paper | 💻 Code

• [2026-02] 🚀 D-CORE open-sourced
  → Efficient tool-use reasoning via dynamic computation routing
  📄 Paper | 💻 Code | 🎮 Demo

• [2026-01] 🏆 LaRoSa accepted to ICML 2025
  → Training-free activation sparsity for LLM acceleration
  📄 Paper | 💻 Code

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📦 Installation

# Clone the repository
git clone https://github.com/alibaba/EfficientAI.git
cd EfficientAI

# Install dependencies (recommended: use conda)
pip install -r requirements.txt

# Optional: Install with specific module support
# pip install -e ".[larosa]"   # for LaRoSa
# pip install -e ".[masquant]" # for MASQuant