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---
license: apache-2.0
datasets:
- dongsheng/DTA-Tool
base_model:
- meta-llama/Llama-2-7b
---
## Model Description
<!-- Provide a longer summary of what this model is. -->
DTA_llama3_8b is from the paper "[Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation](https://arxiv.org/abs/2501.12432)".
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
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The related code can be found in our GitHub [repository](https://github.com/Zhudongsheng75/Divide-Then-Aggregate).
## Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The training data comes from our specially constructed [DTA-Tool](https://huggingface.co./datasets/dongsheng/DTA-Toolhttps://github.com/OpenBMB/ToolBench), which is derived from [ToolBench](https://github.com/OpenBMB/ToolBench).
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data
<!-- This should link to a Dataset Card if possible. -->
We evaluated the performance of DTA-Llama on [StableToolBench](https://github.com/THUNLP-MT/StableToolBench).
### Results
## Citation
<!-- If there is a paper or blog post introducing the model, the APA
 that should go in this section. -->
```bibtex
@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},
}
```
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