metadata
license: apache-2.0
datasets:
- dongsheng/DTA-Tool
base_model:
- meta-llama/Llama-2-7b
Model Description
DTA_llama2_7b is from the paper "Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation". It is a large language model capable of invoking tools and can parallel invoke multiple tools within a single round. The tool format it used is similar to OpenAI's Function Call.
Uses
The related code can be found in our GitHub repository.
Training Data
The training data comes from our specially constructed DTA-Tool, which is derived from ToolBench.
Evaluation
Testing Data
We evaluated the performance of DTA-Llama on StableToolBench.
Results
Citation
@misc{zhu2025dividethenaggregateefficienttoollearning,
title={Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation},
author={Dongsheng Zhu and Weixian Shi and Zhengliang Shi and Zhaochun Ren and Shuaiqiang Wang and Lingyong Yan and Dawei Yin},
year={2025},
eprint={2501.12432},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2501.12432},
}