LLM Fine Tuning

Industries are changing due to artificial intelligence (AI), which makes possible advancements that were previously only found in science fiction. At the core of AI’s growing impact are large language models (LLMs), which make everything possible, from customer support to the production of original content. Pre-training and fine-tuning are two essential procedures for these models. […]

Natural language processing (NLP) has seen tremendous advancements in recent years, largely due to the success of pre-trained language models like GPT, BERT, and T5. These models are trained on massive datasets, allowing them to learn complex language patterns, grammar, facts, and contextual relationships.  While these models are already powerful out of the box, fine-tuning

Artificial Intelligence (AI) is no longer a concept confined to science fiction; it has become a powerful tool transforming industries, revolutionizing the way we live, work, and communicate. From autonomous vehicles to predictive healthcare analytics, AI is reshaping the world around us.  However, with great power comes great responsibility, and this is where the concept

Generative AI, also known as Generative Artificial Intelligence (GAI), is a field of AI focused on creating new content such as text, images, videos, and other forms of data. These models analyze large datasets to understand patterns and generate outputs based on prompts, typically in natural language. Why Prompt Engineering Matters Precision- It ensures that

Prompt engineering involves designing clear and structured instructions (prompts) to guide generative AI models to deliver specific and desired outcomes. Purpose of a Prompt- A prompt acts as a connection between what a user wants and how the AI executes it.  Examples of Prompts For A Text-Based AI- “Explain the process of photosynthesis.” For An

Artificial intelligence (AI) has made industries better, changed the global economy, and changed normal life in many ways. Neural networks are a computational model that works as human brains and modern AI is created using the quality of the human brain as an inspiration.  Neural networks work in various ways such as virtual assistants, self-driving

During the past few years, our perspective on artificial intelligence (AI) for performing different tasks has been changed by deep learning models. However, these models are very bulky and complicated, making it difficult to use them on devices with limited resources, like smartphones. Knowledge distillation is a technique that addresses this issue and is used

Artificial intelligence (AI) is continually growing, and one of its most transformational techniques is transfer learning. Traditionally, machine learning models are built from scratch, necessitating massive datasets and tremendous computer capacity.  Transfer learning, on the other hand, has arisen as a paradigm change, providing a more efficient and effective method for developing intelligent systems. Transfer

The field of artificial intelligence (AI) is always developing and pushing the limits of what robots are capable of. The creation of large language models (LLMs) in recent years has drastically changed how computers comprehend, produce, and react to human language.  These models, such as GPT and similar advances, have demonstrated remarkable proficiency in understanding

Large Language Models (LLMs), including OpenAI’s GPT and Google’s Bard, have transformed how machines comprehend and produce text resembling human communication. As these models increase in size and complexity, it becomes essential to evaluate their performance effectively. LLM evaluation entails systematically examining these models to gauge their efficacy, dependability, and constraints across different tasks. This