# Lucy (/desktop/jan-models/lucy)

## Overview [#overview]

Lucy is a 1.7B parameter model built on Qwen3-1.7B, optimized for web search through tool calling. The model has been trained to work effectively with search APIs like Serper, enabling web search capabilities in resource-constrained environments.

<a
  href="jan://models/huggingface/Menlo/Lucy-128k-gguf"
  style={{
    display: 'inline-flex',
    alignItems: 'center',
    gap: '8px',
    padding: '10px 20px',
    backgroundColor: '#000',
    color: '#fff',
    borderRadius: '8px',
    fontWeight: '600',
    fontSize: '15px',
    textDecoration: 'none',
    marginTop: '8px',
  }}
>
  Open in Jan
</a>

## Performance [#performance]

### SimpleQA Benchmark [#simpleqa-benchmark]

Lucy achieves competitive performance on SimpleQA despite its small size:

![Lucy SimpleQA Performance](../_assets/simpleqa_lucy.png)

The benchmark shows Lucy (1.7B) compared against models ranging from 4B to 600B+ parameters. While larger models generally perform better, Lucy demonstrates that effective web search integration can partially compensate for smaller model size.

## Requirements [#requirements]

- **Memory**:
  - Minimum: 4GB RAM (with Q4 quantization)
  - Recommended: 8GB RAM (with Q8 quantization)
- **Search API**: Serper API key required for web search functionality
- **Hardware**: Runs on CPU or GPU

<Callout type="info">
To use Lucy's web search capabilities, you'll need a Serper API key. Get one at [serper.dev](https://serper.dev).
</Callout>

## Using Lucy [#using-lucy]

### Quick Start [#quick-start]

1. Download Jan Desktop
2. Download Lucy from the Hub
3. Configure Serper MCP with your API key
4. Start using web search through natural language

### Demo [#demo]

![Lucy Demo](https://catalog.jan.ai/docs/lucy_demo.gif)

### Deployment Options [#deployment-options]

**Using vLLM:**
```bash
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
```

**Using llama.cpp:**
```bash
llama-server model.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --rope-scaling yarn \
    --rope-scale 3.2 \
    --yarn-orig-ctx 40960
```

### Recommended Parameters [#recommended-parameters]

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

## What Lucy Does Well [#what-lucy-does-well]

- **Web Search Integration**: Optimized to call search tools and process results
- **Small Footprint**: 1.7B parameters means lower memory requirements
- **Tool Calling**: Reliable function calling for search APIs

## Limitations [#limitations]

- **Requires Internet**: Web search functionality needs active connection
- **API Costs**: Serper API has usage limits and costs
- **Context Processing**: While supporting 128k context, performance may vary with very long inputs
- **General Knowledge**: Limited by 1.7B parameter size for tasks beyond search

## Models Available [#models-available]

- [Lucy on Hugging Face](https://huggingface.co/Menlo/Lucy-128k)
- [Lucy GGUF on Hugging Face](https://huggingface.co/Menlo/Lucy-128k-gguf)

## Citation [#citation]

```bibtex
@misc{dao2025lucyedgerunningagenticweb,
      title={Lucy: edgerunning agentic web search on mobile with machine generated task vectors},
      author={Alan Dao and Dinh Bach Vu and Alex Nguyen and Norapat Buppodom},
      year={2025},
      eprint={2508.00360},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.00360},
}
```