README.md

---
license: apache-2.0
datasets:
- ai2lumos/lumos_maths_plan_onetime
language:
- en
tags:
- language-agent
- maths
- reasoning
- planning
---

# 🪄 Lumos: Language Agents with Unified Formats, Modular Design, and Open-Source LLMs
<p align="center">
  🌐<a href="https://allenai.github.io/lumos">[Website]</a> &nbsp;
  📝<a href="">[Paper]</a> &nbsp;
  🤗<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a> &nbsp;
  🤗<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a> &nbsp;
</p>

We introduce 🪄**Lumos**, Language Agents with **Unified** Formats, **Modular** Design, and **Open-Source** LLMs. **Lumos** unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents. 

**Lumos** has following features:
* 🧩 **Modular Architecture**:
  - **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B.
* 🌍 **Diverse Training Data**:
  - **Lumos** is trained with ~40K high-quality annotations from ground-truth reasoning steps in existing benchmarks with GPT-4. 
* 🚀 **Competitive Performance**:
  - 🚀 **Lumos** outperforms **GPT-4/3.5-based** agents on complex QA and web agent tasks, and **larger open agents** on maths tasks.
  - 🚀 **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **unmodularized** training.
  - 🚀 **Lumos** surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.

## Model Overview
`lumos_maths_plan_onetime` is a **planning** module checkpoint finetuned on **maths** task in **Lumos-Onetime (Lumos-O)** formulation. 

The training annotation is shown below:

| Training Data | Number |
|---|---|
|[`lumos_maths_plan_onetime`](https://huggingface.co/datasets/ai2lumos/lumos_maths_plan_onetime)|19778|


## Citation

If you find this work is relevant with your research, please feel free to cite our work!
```
@article{yin2023lumos,
  title={Lumos: Towards Language Agents that are Unified, Modular, and Open Source},
  author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
  year={2023}
}
```