add OpenELM-270M

README.md

@@ -161,3 +161,29 @@ lm_eval --model hf \
 ## Bias, Risks, and Limitations
 
 The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
+
+## Citation
+
+If you find our work useful, please cite:
+
+```BibTex 
+@article{mehtaOpenELMEfficientLanguage2024,
+	title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}-source {Training} and {Inference} {Framework}},
+	shorttitle = {{OpenELM}},
+	url = {https://arxiv.org/abs/2404.14619v1},
+	language = {en},
+	urldate = {2024-04-24},
+	journal = {arXiv.org},
+	author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
+	month = apr,
+	year = {2024},
+}
+
+@inproceedings{mehta2022cvnets, 
+     author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, 
+     title = {CVNets: High Performance Library for Computer Vision}, 
+     year = {2022}, 
+     booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, 
+     series = {MM '22} 
+}
+```