ai2lumos/lumos_maths_plan_onetime

Name: ai2lumos/lumos_maths_plan_onetime
Creator: ai2lumos
Published: 2024-03-04T19:07:58+08:00

Hugging Face2024-03-18 更新2024-03-04 收录

语言代理

数学推理

数据链接：

https://hf-mirror.com/datasets/ai2lumos/lumos_maths_plan_onetime 数据链接链接失效反馈

官方服务：

资源简介：

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - language-agent - maths - reasoning size_categories: - 10K<n<100K --- # 🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents <p align="center"> 🌐<a href="https://allenai.github.io/lumos">[Website]</a>   📝<a href="https://arxiv.org/abs/2311.05657">[Paper]</a>   🤗<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a>   🤗<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a>   🤗<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">[Demo]</a>   </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. ## Data Overview `lumos_maths_plan_onetime` is the data for training **planning** module on **maths** task in **Lumos-Onetime (Lumos-O)** formulation. The source of the training annotation training data is shown below: | Task | Number | |---|---| |PRM800K|10000| |GSM8K|7473| |ASDiv|2305| ## Models Trained with the Data `lumos_maths_plan_onetime` is used to train the following models. |Model|Huggingface Repo| |---|---| |`lumos_maths_plan_onetime`| [🤗Huggingface Repo](https://huggingface.co/ai2lumos/lumos_maths_plan_onetime) | |`lumos_maths_plan_onetime-13B`| [🤗Huggingface Repo](https://huggingface.co/ai2lumos/lumos_maths_plan_onetime-13B) | ## 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} } ```

提供机构：

ai2lumos

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