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  license: apache-2.0
 
 
 
 
 
 
 
 
 
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  license: apache-2.0
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+ datasets:
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+ - ai2lumos/lumos_multimodal_plan_iterative
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+ language:
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+ - en
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+ tags:
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+ - language-agent
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+ - visual-question-answering
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+ - reasoning
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+ - planning
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  ---
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+
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+ # πŸͺ„ Agent Lumos: Unified and Modular Training for Open-Source Language Agents
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+ <p align="center">
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+ 🌐<a href="https://allenai.github.io/lumos">[Website]</a> &nbsp;
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+ πŸ“<a href="https://arxiv.org/abs/2311.05657">[Paper]</a> &nbsp;
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+ πŸ€—<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a> &nbsp;
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+ πŸ€—<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a> &nbsp;
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+ πŸ€—<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">[Demo]</a> &nbsp;
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+ </p>
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+
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+ We introduce πŸͺ„**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.
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+
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+ **Lumos** has following features:
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+ * 🧩 **Modular Architecture**:
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+ - 🧩 **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B/13B and off-the-shelf APIs.
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+ - πŸ€— **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.
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+ * 🌍 **Diverse Training Data**:
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+ - 🌍 **Lumos** is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
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+ - βš’οΈ **Lumos** data can be instrumental for future research in developing open-source agents for complex interactive tasks.
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+ * πŸš€ **Competitive Performance**:
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+ - πŸš€ **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.
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+ - πŸš€ **Lumos** exceeds contemporaneous agents that have been **fine-tuned** with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as **FiReAct**, **AgentLM**, and **AutoAct**.
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+ - πŸš€ **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **integrated** training.
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+ - πŸš€ **Lumos** surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.
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+
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+ ## Model Overview
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+ `lumos_multimodal_plan_iterative-13B` is a **planning** module checkpoint finetuned on **multimodal** task in **Lumos-Iterative (Lumos-I)** formulation.
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+
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+ The training annotation is shown below:
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+
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+ | Training Data | Number |
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+ |---|---|
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+ |[`lumos_multimodal_plan_iterative`](https://huggingface.co/datasets/ai2lumos/lumos_multimodal_plan_iterative)|19541|
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+
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+
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+ ## Citation
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+
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+ If you find this work is relevant with your research, please feel free to cite our work!
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+ ```
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+ @article{yin2023lumos,
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+ title={Agent Lumos: Unified and Modular Training for Open-Source Language Agents},
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+ author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
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+ journal={arXiv preprint arXiv:2311.05657},
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+ year={2023}
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+ }
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+ ```