allenporter/xlam:1b

Welcome to the xLAM model family! Large Action Models (LAMs) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.

License

xLAM-1b-fc-r is distributed under the CC-BY-NC-4.0 license, with additional terms specified in the Deepseek license

Citation

If you find this repo helpful, please cite our paper:

@article{liu2024apigen,
  title={APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets},
  author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and Lan, Tian and Kokane, Shirley and Tan, Juntao and Yao, Weiran and Liu, Zhiwei and Feng, Yihao and others},
  journal={arXiv preprint arXiv:2406.18518},
  year={2024}
}

<img width="300" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
<a href="https://apigen-pipeline.github.io/">[Homepage]</a>  |   <a href="https://arxiv.org/abs/2406.18518">[Paper]</a> |   <a href="https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k">[Dataset]</a> |   <a href="https://github.com/SalesforceAIResearch/xLAM">[Github]</a>
<hr>

Welcome to the xLAM model family! <a href="https://blog.salesforceairesearch.com/large-action-models/">Large Action Models (LAMs)</a> are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.

## License

`xLAM-1b-fc-r` is distributed under the CC-BY-NC-4.0 license, with additional terms specified in the <A href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL">Deepseek license</a>

## Citation

If you find this repo helpful, please cite our paper:
```bibtex
@article{liu2024apigen,
  title={APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets},
  author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and Lan, Tian and Kokane, Shirley and Tan, Juntao and Yao, Weiran and Liu, Zhiwei and Feng, Yihao and others},
  journal={arXiv preprint arXiv:2406.18518},
  year={2024}
}
```

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