Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- ai2lumos/lumos_multimodal_plan_iterative
+language:
+- en
+tags:
+- language-agent
+- visual-question-answering
+- reasoning
+- planning
 ---
+
+# 🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
+<p align="center">
+  🌐<a href="https://allenai.github.io/lumos">[Website]</a> &nbsp;
+  📝<a href="https://arxiv.org/abs/2311.05657">[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;
+  🤗<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">[Demo]</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/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.
+
+## Model Overview
+`lumos_multimodal_plan_iterative-13B` is a **planning** module checkpoint finetuned on **multimodal** task in **Lumos-Iterative (Lumos-I)** formulation. 
+
+The training annotation is shown below:
+
+| Training Data | Number |
+|---|---|
+|[`lumos_multimodal_plan_iterative`](https://huggingface.co/datasets/ai2lumos/lumos_multimodal_plan_iterative)|19541|
+
+
+## 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}
+}
+```