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Generative AI: Retrieval Augmented Generation(RAG)

  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 th...
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Generative AI: Faster fine-tuning of a Mistral Model with less memory

  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 ...
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Generative AI with LangChain: Basics

  Wishing everyone a Happy New Year '24😇 I trust that you've found valuable insights in my previous blog posts. Embarking on a new learning adventure with this latest post, we'll delve into the realm of Generative AI applications using LangChain💪. This article will initially cover the basics of Language Models and LangChain. Subsequent posts will guide you through hands-on experiences with various Generative AI use cases using LangChain. Let's kick off by exploring the essential fundamentals💁 What is a Large Language Model (LLM)? A large language model denotes a category of artificial intelligence (AI) models that undergo extensive training with extensive textual data to comprehend and produce language resembling human expression🙇. Such a large language model constitutes a scaled-up transformer model, often too extensive for execution on a single computer. Consequently, it is commonly deployed as a service accessible through an API or web interface. These models are...
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How to train a custom dataset with YOLOv7 for instance segmentation?

  Another post starts with you beautiful people! It is overwhelming for me to see massive interest in my last three posts about the YOLOv7 series💓. Your response keeps me motivated to share my learning with you all 💝. If you have not checked my previous posts about YOLOv7, then I am sharing here links to read those once and then proceed with this post- Train a custom dataset with YOLOv7 Export custom YOLOv7 model to ONNX Export custom YOLOv7 model to TensorRT Till now we have learned about object detection with YOLOv7. In this post, we are going to learn how can we train a custom dataset for instance segmentation task with YOLOv7 👌. For your information instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image. For our hands-on we need a dataset having images and their annotations in polygon format and of course in YOLO format. So I have found and downloaded the American Sign Language dataset in the required format...
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