sonicc
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tabby-distilgpt2-diabetes

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- MOE
+- GPT-2
+- tabular
+- generative
+- causalLM
+pipeline_tag: tabular-regression
 ---
+# Tabby Model Card
+Tabby is a post-training architecture modification for Transformer-based large language models,
+enabling their use for **tabular dataset synthesis**. This specific demo checkpoint is based on [DistilGPT-2](https://huggingface.co/distilbert/distilgpt2)
+and fine-tuned on the [UCI Diabetes dataset](https://www.openml.org/search?type=data&sort=version&status=any&order=asc&exact_name=diabetes&id=37),
+using our novel Plain training method,
+as an example of Tabby’s tabular synthesis capabilities.
+Tabby enhances transformer-based LLMs by incorporating **Mixture of Experts (MoE) layers**,
+allowing for better modeling of structured data.
+For more details, check out our paper and GitHub repo!
+- **Developed by:** University of Wisconsin-Madison
+- **Shared by:** Sonia Cromp et al.
+- **Model type:** MoE-enhanced GPT-2-based causal language model for tabular data
+- **License:** MIT
+- **Finetuned from model:** [`distilgpt2`](https://huggingface.co/distilbert/distilgpt2)
 ## Uses
 ### Direct Use
+This Tabby checkpoint can be used for:
+- High-fidelity synthesis of diabetes patients based on the [UCI Diabetes dataset](https://www.openml.org/search?type=data&sort=version&status=any&order=asc&exact_name=diabetes&id=37).
+- Data augmentation for training machine learning models on the UCI Diabetes dataset.
+- Comparison with other tabular synthesis approaches.
+### Downstream Use
+- Further fine-tuning on other structured datasets (e.g., financial records, medical records, or survey data).
+- Generating synthetic tabular data for privacy-preserving machine learning.
 ## Bias, Risks, and Limitations
+This Tabby checkpoint inherits biases from the GPT-2 architecture and the UCI Diabetes dataset used for fine-tuning.
+Considerations include those common to all generative models, such as:
+- Bias in synthetic data feature distributions, particularly those that may reflect real-world disparities in the dataset.
+- Potential hallucinations that do not perfectly match real-world distributions.
+## Citation
+If you use Tabby, please cite:
+```bibtex
+@article{cromp2025tabby,
+  title={Tabby: Tabular Data Synthesis with Language Models},
+  author={Sonia Cromp, Satya Sai Srinath Namburi GNVV, Mohammed Alkhudhayri, Catherine Cao, Samuel Guo, Nicholas Roberts, Frederic Sala},
+  journal={arXiv preprint arXiv:2405.01147},
+  year={2025},
+  url={https://arxiv.org/abs/2405.01147}
+}
+```
 ## Model Card Contact
+For questions or collaborations, please reach out to [Sonia Cromp](https://socromp.github.io) at [[email protected]].