Lucy 128k

🚀 Lucy: Edgerunning Agentic Web Search on Mobile with a 1.7B model.

Lucy is a 1.7B model designed for agentic web search and lightweight browsing. Built on Qwen3 - 1.7B, it combines the research capabilities of larger models and can run efficiently on mobile devices, even with just a CPU.

Authors: Alan Dao, Bach Vu Dinh, Alex Nguyen, Norapat Buppodom

🚀 Quick Start

Lucy can be deployed using various methods including vLLM, llama.cpp, or through local applications like Jan, LMStudio, and other compatible inference engines. The model supports integration with search APIs and web browsing tools through the MCP.

Deployment

Deploy using VLLM:

vllm serve Menlo/Lucy-128k \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --rope-scaling '{"rope_type":"yarn","factor":3.2,"original_max_position_embeddings":40960}' --max-model-len 131072

Or llama-server from llama.cpp:

llama-server ... --rope-scaling yarn --rope-scale 3.2 --yarn-orig-ctx 40960

Recommended Sampling Parameters

Temperature: 0.7
Top-p: 0.9
Top-k: 20
Min-p: 0.0

✨ Features

• 🔍 Strong Agentic Search: Powered by MCP-enabled tools (e.g., Serper with Google Search)
• 🌐 Basic Browsing Capabilities: Through Crawl4AI (MCP server to be released), Serper,...
• 📱 Mobile-Optimized: Lightweight enough to run on CPU or mobile devices with decent speed
• 🎯 Focused Reasoning: Machine-generated task vectors optimize thinking processes for search tasks

📚 Documentation

Overview

Lucy is a compact but capable 1.7B model focused on agentic web search and lightweight browsing. Built on Qwen3-1.7B, Lucy inherits deep research capabilities from larger models while being optimized to run efficiently on mobile devices, even with CPU-only configurations.

We achieved this through machine-generated task vectors that optimize thinking processes, smooth reward functions across multiple categories, and pure reinforcement learning without any supervised fine-tuning.

Evaluation

Following the same MCP benchmark methodology used for Jan-Nano and Jan-Nano-128k, Lucy demonstrates impressive performance despite being only a 1.7B model, achieving higher accuracy than DeepSeek-v3 on SimpleQA.

🤝 Community & Support

• Discussions: HuggingFace Community

📄 License

This project is licensed under the Apache 2.0 license.

📄 Citation

Paper (coming soon): Lucy: edgerunning agentic web search on mobile with machine generated task vectors.