Platypus: Quick, Cheap, and Powerful LLM

In recent years, model parameters have exploded to a huge number of parameters (<a href="https://ai.google/discover/palm2/" rel="noopener ugc nofollow" target="_blank">540 B with PaLM</a>). The question that has been asked is whether this number of parameters is necessary. <a href="https://openai.com/research/scaling-laws-for-neural-language-models" rel="noopener ugc nofollow" target="_blank">According to OpenAI</a>, as models grow, there is an increase in performance. In addition, there is the appearance of emergent properties (properties that cannot be observed except at a certain scale). This view has been challenged by the fact that actually more data, and thus scaling is limited by the number of tokens needed<a href="https://arxiv.org/abs/2203.15556" rel="noopener ugc nofollow" target="_blank"> to train a model optimally</a>. Moreover, even these emergent properties may not even exist. Second, these proprietary models cannot be analyzed or used freely by the scientific community. Therefore, first with <a href="https://pub.towardsai.net/a-new-bloom-in-ai-why-the-bloom-model-can-be-a-gamechanger-380a15b1fba7" rel="noopener ugc nofollow" target="_blank">BLOOM</a> and then with <a href="https://medium.com/mlearning-ai/metas-llama-a-small-language-model-beating-giants-5065948e0b7f" rel="noopener">META’s LLaMA</a>, the community has moved toward using open-source models. <a href="https://medium.com/mlearning-ai/metas-llama-a-small-language-model-beating-giants-5065948e0b7f" rel="noopener">LLaMA</a> also showed that an increased focus on data allows smaller models to compete with larger models. <a href="https://levelup.gitconnected.com/platypus-quick-cheap-and-powerful-llm-404b86af8755">Click Here</a>