Create README.md

README.md

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+---
+datasets:
+- EleutherAI/pile
+language:
+- en
+---
+# Model Card
+
+This model is pretrained as a reference baseline to the Based model provided here: https://huggingface.co/hazyresearch/based-1b-50b. 
+
+Both checkpoints are pretrained on **50Bn tokens** of the Pile in the exact same data order using next token prediction. 
+
+
+### Model Sources
+
+The model is a standard Transformer model, using the Llama architecture (Rotary encodings, SwiGLU, RMS Norm, etc.)
+
+The training code is provided here and can be used to reproduce training: https://github.com/HazyResearch/based
+
+The paper for the work is here, and the appendix includes additional experimental details/hyperparameters: https://arxiv.org/abs/2402.18668
+
+
+### 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 simarora@stanford.edu, eyuboglu@stanford.edu, and mzhang20@stanford.edu with questions.