---
license: mit
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is a fully fine-tuned version of the [Llama-7B](https://huggingface.co./huggyllama/llama-7b) model on synthetically generated arithmetic tasks. It was introduced in [this](https://openreview.net/forum?id=8sKcAWOf2D) paper. It is very similar to [Goat-7B](https://github.com/liutiedong/goat), except it was trained without LoRA.

For inquiries about checkpoints during the fine-tuning process, kindly reach out to [Nikhil](mailto:prakash.nik@northeastern.edu) via email.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Nikhil Prakash](https://nix07.github.io/)
- **Model type:** Autoregressive Decoder-only Language Model
- **License:** MIT License
- **Finetuned from model:** [Llama-7B](https://huggingface.co./huggyllama/llama-7b)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [Link](https://github.com/Nix07/finetuning/)
- **Paper :** [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://arxiv.org/abs/2402.14811)

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoModel
model = AutoModel.from_pretrained("nikhil07prakash/float-7b")
```

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```python
@inproceedings{prakash2023fine,
  title={Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking},
  author={Prakash, Nikhil and Shaham, Tamar Rott and Haklay, Tal and Belinkov, Yonatan and Bau, David},
  booktitle={Proceedings of the 2024 International Conference on Learning Representations},
  note={arXiv:2402.14811},
  year={2024}
}
```