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Fine tuning in LLM In this article, we're going to delve into the techniques used to adapt a generic pre-trained model for a specific task or application. Now, pre-trained language models can do really impressive stuff just immediately off the shelf, including things like text generation, summarization, and even providing levels of code. But LLMs are not one-size-fits-all as you can imagine and maybe I want to specialize it even further after the model has been trained. I'd like to make it really good at answering questions based on certain topics such as medicine or travel or code or something. This is what the idea of fine-tuning comes in.  Methods used in Fine tuning: Fine-tuning a large language model refers to the process of taking a pre-trained language model or a model that's already been trained on a large dataset and further training it on a smaller task-specific dataset. Now this process will allow the model to adapt and specialize its capability. And if we take t...

Data on LLMs In this article, I'm going to explore the impact of data size and quality and how it can affect a large language model. So, we understand that these large language models are really good at text generation and they're great at being just deep learning models, they're transformers, right? At their very core, they're just like any other AI or machine learning model and they just happen to be good at natural language generation. Now these models, since they're able to learn the complexity of human language, their potential is really immense. You can almost tell that these applications are going to be developed for all sorts of things.  Understanding Data Quality: Now, even depending on let's say if we're trying to do something like text generation or text image generation or image captioning or all sorts of sectors from finance to telecom and healthcare, they're all going to be using these tools. Then the question is, is, well what data should ...

Architecture of LLMs In this article, we're going to discover the key components in general architecture that make up typical large language models.  Introduction to LLM Architecture: Now, large language models will have things like the transformer neural networks at their very underlying pieces of it. Remember, these transformers are just a large number of parameters are attached to this transformer model and these LLMs are able to then understand and generate accurate responses because it's using this type of neural network at its core. In fact, what you're going to see is that these LLMs are referred to as foundation models and it's all it means is a foundation model is so large and impactful that it serves as its own class of artificial intelligence so that we can start experimenting with these types. Transformer Model Structure: It's just a way for us to classify these models that are being used today. Now, what exactly is a transformer model? We'll get to ...

Large Language Models In this article, we're going to discuss large language models and explain how they are used in the world of AI.  What is LLM and how does it work? So, let's start with a brief definition of what large language models are. So, a large language model is just a type of artificial intelligence algorithm that uses deep learning techniques and massively large data sets to understand, summarize, and even predict new content. Now what you'll see here with the generative AI is that it's closely connected with things like LLMs. Now all that you can think of is an LLM is just a type of generative AI that's specifically architected to help generate text content for us. As a result, these LLMs use a deep learning algorithm that can perform a variety of these natural language processing tasks. This can include things like, well, words, grammar, and semantics are what the LLM is very good at understanding. It's able to look at the training set and identif...

benefits and limitations of ChatGPT Benefits: ChatGPT offers significant advantages across various business functions, for example to enhance productivity, automate tasks, deliver personalized experiences, or drive innovation. Let’s now explore some of these benefits of leveraging ChatGPT’s abilities to businesses, customers, employees, and society. Enhancing Business Efficiency and Cost Savings for organizations: Organizations can use ChatGPT’s ability to perform routine tasks quickly and accurately to streamline their business processes. This potentially creates significant savings in time and money for businesses, and frees up employees to focus on tasks that do require a human touch. For example, ChatGPT chatbots can quickly and accurately respond to commonly asked customer inquiries about products, services, billing, and account management; freeing up customer service representatives to focus on more important or complicated customer issues. Improving Customer Experience and Insig...

applications and ethical considerations of using ChatGPT In this article, we'll take a closer look at a few specific applications of this technology in real-world sectors and scenarios. In Financial Industry: In the finance industry, ChatGPT can be used to automate various tasks such as data entry, report generation, data analysis, and predictive modeling. By providing organizations with accurate analysis of real time information, they’re able to make data-driven decisions and respond rapidly to market changes. By analyzing financial data, ChatGPT can identify patterns that may not be immediately apparent to human analysts. Overall, ChatGPT has the potential to streamline operations in the financial industry, reduce errors, and improve the accuracy of predictions. In retail industry: In the retail industry, ChatGPT can be used to provide quick and accurate responses to customer inquiries. With its ability to provide relevant and helpful responses to customer queries, ChatGPT reduce...