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Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs"
| Date | Stars |
|---|---|
| 2026-07-24 | 478 |
| 2026-07-25 | 477 |
| 2026-07-28 | 477 |
| 2026-07-30 | 477 |
| 2026-08-06 | 477 |
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# 🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
<p align="center">
<a href="https://allenai.github.io/lumos/">
<img src="https://img.shields.io/badge/🌐-Website-red">
</a>
<a href="https://arxiv.org/abs/2311.05657">
<img src="https://img.shields.io/badge/📝-Paper-blue">
</a>
<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">
<img src="https://img.shields.io/badge/🤗-Data-orange">
</a>
<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">
<img src="https://img.shields.io/badge/🤗-Model-green">
</a>
<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">
<img src="https://img.shields.io/badge/🤗-Demo-yellow">
</a>
</p>
🖋 **Authors:** [Da Yin](https://wadeyin9712.github.io/), [Faeze Brahman](https://fabrahman.github.io/), [Abhilasha Ravichander](https://www.cs.cmu.edu/~aravicha/), [Khyathi Chandu](https://www.cs.cmu.edu/~kchandu/), [Kai-Wei Chang](http://web.cs.ucla.edu/~kwchang/), [Yejin Choi](https://homes.cs.washington.edu/~yejin/), [Bill Yuchen Lin](https://yuchenlin.xyz/)
We introduce 🪄**Lumos**, Language Agents with **Unified** Data 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/13B and off-the-shelf APIs.
- 🤗 **Lumos** utilizes a unified data format that encompasses multiple task types, thereby enabling the developed agent framework to conveniently support a range of interactive tasks.
* 🌍 **Diverse Training Data**:
- 🌍 **Lumos** is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- ⚒️ **Lumos** data can be instrumental for future research in developing open-source agents for complex interactive tasks.
* 🚀 **Competitive Performance**:
- 🚀 **Lumos** is comparable or even beats **GPT-series** agents on web/complex QA tasks Mind2Web and HotpotQA, and **larger open agents** on math and multimodal tasks.
- 🚀 **Lumos** exceeds contemporaneous agents that have been **fine-tuned** with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as **FiReAct**, **AgentLM**, and **AutoAct**.
- 🚀 **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **integrated** training.
- 🚀 **Lumos** surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.
## 🤩 Citation
If you find this work is relevant with your research, please feel free to cite our work!
```
@article{yin2023lumos,
title={{Agent Lumos: Unified and Modular Training for Open-Source Language Agents}},
author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
journal={arXiv preprint arXiv:2311.05657},
year={2023}
}
```
## 🔥 News
- **[2024, Mar 18]** We release the latest **Lumos** version:
- 📑 **Lumos** paper that covers new **multimodal** tasks and 13B-scale model experiments
- 🤗 **Lumos** demo that illustrates **Lumos** planning and grounding processes
- **[2023, Nov 8]** We release the important items for training and evaluating **Lumos**:
- 💻 **Lumos** code for annotation generation, training and evaluation
- 🤗 **Lumos** checkpoints with 7B model size
- 🤗 **Lumos** training annotations and their raw data
## 🧩 Architecture
<p align="center">
<img src=assets/lumos.png width=850/>
</p>
## 🛠️ Setup
```
./setup.sh
```
Please make sure that the cudatoolkit version in `setup.sh` aligns with your local cuda version.
## Training
### 📈 Training Data Download
We collect all the training annotations, raw data and prompt converted annotations in a single [Google Drive Excerpt of 9,957 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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