Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- dongsheng/DTA-Tool
+base_model:
+- meta-llama/Llama-2-13b
+---
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+DTA_llama2_7b 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
+
+![result](result.png)
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630da0fae57da204209411d3/ViBSn34pV-4LWJkIpUvSr.png) 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}, 
+}
+```