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title: README |
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## Panda Villa Tech Limited |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/PandaVT/DataTager/main/assert/PandaVilla_logo.jpg" width="650" style="margin-bottom: 0.2;"/> |
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<p> |
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<h5 align="center"> Grow Together ⭐ </h5> |
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<h4 align="center"> [<a href="https://github.com/PandaVT/DataTager">GitHub</a> | <a href="https://datatager.com/">DataTager</a>]</h4> |
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**Long-term Focus:** |
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- Our company is dedicated to long-term specialization in **synthetic data**, **metaphysics**, and **psychology LLM**, exploring how these fields can intersect with AI. |
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**Product:** DataTager |
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**Website:** [DataTager.com](https://DataTager.com/) |
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- **Description:** DataTager is a tool designed to evaluate and generate the training data needed for large language models. We believe it's more important for individuals and enterprises to fine-tune large models easily and create models tailored to their specific business needs, rather than just choosing models with the highest benchmarks. |
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**Philosophy:** |
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- We published a paper titled "AnyTaskTune," advocating that **Task Fine-Tuning** based on real-world scenarios is crucial. This approach is more significant than using universally high-scoring models. |
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**Resources:** |
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- We have open-sourced various subtask datasets across multiple domains to support the community. These resources are available on our website for anyone interested in specific task fine-tuning. |
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Explore more on how to fine-tune your tasks efficiently with our resources at [DataTager.com](https://DataTager.com/). |
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