Update README.md

README.md

@@ -9,17 +9,33 @@ license: mit
 
 # OPT : Open Pre-trained Transformer Language Models
 
-OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. 
-
 OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
 
 **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). 
 Content from **this** model card has been written by the Hugging Face team.
 
+## Intro
+
+To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
+
+> Large language models trained on massive text collections have shown surprising emergent
+> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
+> can interact with these models through paid APIs, full model access is currently limited to only a
+> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
+> why these large language models work, hindering progress on improving known challenges in areas
+> such as robustness, bias, and toxicity.
+
+> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
+> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match 
+> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
+> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
+> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
+> collective research community as a whole, which is only possible when models are available for study.
+
 ## Model description
 
-OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling 
-objective.
+OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
+OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
 
 For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read 
 the [official paper](https://arxiv.org/abs/2205.01068).