Upload MHTabbyGPT2

README.md

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 ---
 library_name: transformers
-tags:
-- MOE
-- GPT-2
-- tabular
-- generative
-- causalLM
-pipeline_tag: tabular-regression
+tags: []
 ---
 
-# Tabby Model Card
+# Model Card for Model ID
 
-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!
+<!-- Provide a quick summary of what the model is/does. -->
 
 
-- **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)
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
+
 ### 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.
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-### Downstream Use
+[More Information Needed]
 
-- 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.
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## 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.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
 
-## Citation
+[More Information Needed]
 
-If you use Tabby, please cite:
+## Model Card Authors [optional]
 
-```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}
-}
-```
+[More Information Needed]
 
 ## Model Card Contact
 
-For questions or collaborations, please reach out to [Sonia Cromp](https://socromp.github.io) at [[email protected]].
+[More Information Needed]

model.safetensors

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