Another blog post starts with you beautiful people👦. I hope you have explored my last blog post about 2x faster fine-tuning of Mistral 7b model on a custom dataset👈. In this blog post, we are going to learn an essential technique in Generative AI: Retrieval Augmented Generation (RAG). What is RAG? Retrieval Augmented Generation (RAG) is an innovative approach that melds generative models, like transformers, with a retrieval mechanism. By tapping into existing knowledge, RAG retrieves pertinent information from expansive external datasets or knowledge bases to enhance the generation process, thereby elevating the model's content relevance and factual accuracy💪. This versatility renders RAG particularly beneficial for tasks demanding the assimilation of external knowledge, such as question answering or content creation. Upon receiving input, RAG actively searches for relevant documents from specified sources (e.g., Wikipedia, company knowledge base, etc.). It then seamlessly a
Another blog post starts with you beautiful people💥. I hope you have started your Generative AI learning from my last post and if you have not, then I recommend you read that one before proceeding to this blog. In this post, we will delve into the transformative power of generative AI, specifically exploring the cutting-edge Mistral 7B model fine-tuning🚀. This revolutionary technology has not only redefined the boundaries of artificial intelligence but also sparked a paradigm shift in how we approach data generation and creativity. Join me on a journey through the fascinating intersection of machine learning and creativity, where Mistral 7B stands as a beacon of innovation, pushing the boundaries of what's possible in the realm of generative AI👈. Fine-tuning a large language model(LLM) refers to the process of training the model on a specific, smaller dataset to adapt it to a particular task or domain. Large language models, like ChatGPT, are pre-trained on massive dataset