|
--- |
|
datasets: |
|
- EleutherAI/pile |
|
language: |
|
- en |
|
--- |
|
# Model Card |
|
|
|
This model is pretrained Attention (Llama architecture) model. The goal of this model is to provide a quality reference for the Based architecture. |
|
|
|
As a quality reference, we include a pretrained Mamba model provided here: https://huggingface.co/hazyresearch/mamba-1b, and a pretrained Based model provided here: https://huggingface.co/hazyresearch/based-1b |
|
|
|
All three checkpoints are pretrained on **10Bn tokens** of the Pile in the exact same data order using next token prediction. |
|
|
|
|
|
### Model Sources |
|
|
|
The model implementation and training code that produced the model are provided here: https://github.com/HazyResearch/based |
|
|
|
### Uses |
|
|
|
The purpose of this work is to evaluate the language modeling quality of a new efficient architecture, Based. |
|
|
|
We include a series of benchmarks that you can use to evaluate quality: |
|
- FDA: https://huggingface.co/datasets/hazyresearch/based-fda |
|
- SWDE: https://huggingface.co/datasets/hazyresearch/based-swde |
|
- SQUAD: https://huggingface.co/datasets/hazyresearch/based-squad |
|
|
|
|
|
## Citation |
|
|
|
Please consider citing this paper if you use our work: |
|
|
|
``` |
|
@article{arora2024simple, |
|
title={Simple linear attention language models balance the recall-throughput tradeoff}, |
|
author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas and Zinsley, Dylan and Zou, James and Rudra, Atri and Ré, Christopher}, |
|
journal={arXiv:2402.18668}, |
|
year={2024} |
|
} |
|
``` |
|
|
|
Please reach out to [email protected], [email protected], and [email protected] with questions. |
|
|
