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| README.md | 3.8 kB xet | a45bdd44 |
AgentInstruct Dataset
🤗 [Models] • 💻 [Github Repo] • 📌 [Project Page] • 📃 [Paper]
AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct.
- 🔍 CoT - Harness the power of ReAct, offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey.
- 🌍 Diversity - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35.
- 🎯 Precision - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality.
- ✅ Assurance - Rigorous checks to avoid data leakage, ensuring pristine dataset quality.
Task Overview
| Task | # Filt. Traj. | Avg # Filt. Traj. Turns |
|---|---|---|
| ALFWorld | 336 | 13.52 |
| WebShop | 351 | 3.68 |
| Mind2Web | 122 | 1.00 |
| Knowledge Graph | 324 | 6.04 |
| Operating System | 195 | 3.85 |
| Database | 538 | 2.06 |
| AgentInstruct | 1866 | 5.24 |
AgentInstruct includes 1,866 trajectories from 6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj." stands for filtered trajectories.
Models
AgentLM models are produced by mixed training on AgentInstruct dataset and ShareGPT dataset from Llama-2-chat models.
The models follow the conversation format of Llama-2-chat, with system prompt fixed as
You are a helpful, respectful and honest assistant.
7B, 13B, and 70B models are available on Huggingface model hub.
| Model | Huggingface Repo |
|---|---|
| AgentLM-7B | 🤗Huggingface Repo |
| AgentLM-13B | 🤗Huggingface Repo |
| AgentLM-70B | 🤗Huggingface Repo |
Check our [Github Repo] for details about AgentTuning.
Citation
If you find our work useful, please consider citing AgentTuning:
@misc{zeng2023agenttuning,
title={AgentTuning: Enabling Generalized Agent Abilities for LLMs},
author={Aohan Zeng and Mingdao Liu and Rui Lu and Bowen Wang and Xiao Liu and Yuxiao Dong and Jie Tang},
year={2023},
eprint={2310.12823},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
- Total size
- 1.26 MB
- Files
- 8
- Last updated
- Jun 16
- Pre-warmed CDN
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