README.md

---
base_model:
- huggyllama/llama-7b
language:
- en
library_name: transformers
license: other
pipeline_tag: text-generation
---

This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file).
You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.

# Model Card for Model ID

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



## 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:** Meta, Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** This model inherited Llama License (see `LICENSE`).
- **Finetuned from model:** [huggyllama/llama-7b](huggyllama/llama-7b)

### Model Sources

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

- **Repository:** https://github.com/changwoolee/BLAST
- **Paper:** Changwoo Lee, Soo Min Kwon, Qing Qu, and Hun-Seok Kim. "BLAST: Block Level Adaptive Structured Matrix for Efficient Deep Neural Network Inference." NeurIPS 2024


## How to Get Started with the Model

Use the code below to get started with the model.

[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:**
```
@inproceedings{
    lee2024blast,
    title={{BLAST}: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference},
    author={Lee, Changwoo and Kwon, Soo Min and Qu, Qing and Kim, Hun-Seok},
    booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
    year={2024},
}
```