🖋 Authors: Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin

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.

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}
}

• [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

./setup.sh

Please make sure that the cudatoolkit version in setup.sh aligns with your local cuda version.

We collect all the training annotations, raw data and prompt converted annotations in a single Google Drive folder. It can be downloaded by

cd data
python -c "import gdown; gdown.download_folder('https://drive.google.com/drive/folders/1ASFhOkhezgewVxR01dQg-8KUVR8IdBlY?usp=sharing', quiet=True)" 

We also provide generated annotations for planning and grounding modules in 🤗 Huggingface Datasets.

./train.sh [MODULE] [FORMULATION]

[MODULE] can be either plan or ground. [FORMULATION] can be either iterative or onetime.

You can adjust the fine-tuning hyperparameters and specific task you want to fine-tune in the training scripts such as finetune_llama2_plan_iterative.sh in scripts/train.

We also provide the fine-tuned planning and grounding module checkpoints in 🤗 Huggingface.

Evaluation scripts for different datasets are under scripts/eval. For example, you can evaluate Lumos on HotpotQA by running:

./scripts/eval/hotpotqa.sh

We provide the code for generating training annotations based on raw existing benchmarks from scratch.

Before generating annotations, we first need to download the existing benchmarks providing ground-truth intermediate reasoning steps. The raw data are can be downloaded via this Google Drive folder.

python -m data.prompt_convertion \
  --domain DOMAIN \
  --data_fn DATA_FN \
  --convert_all

domain covers maths, complex QA, web agent, multimodal. data_fn is the path where raw benchmarks are stored.

For multimodal task annotation generation, please download COCO 2017 train images in data/train/multimodal/raw_data and unzip it.

We greatly thank Tulu team for providing awesome code to finetune LLAMA-2. We also sincerely appreciate the contributors of zeno-build, Mind2Web, and WebShop for providing fast GPT prompting, HTML preprocessing and evaluation docker environment.