LLM Fine Tuning

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

AI is becoming more intelligent every day, but there is a secret component that gives it the capacity to reason and think more like a human. That component? prompted by the Chain-OF-Thought (COT). Hold on tight if you’ve ever wondered how AI deconstructs difficult issues or generates sophisticated solutions. Without blowing a fuse in the

Large language models and generative AI systems have made great strides in recent years, but with their increasing usage, some of the ingrained issues also come to the forefront, such as the phenomenon known as AI hallucinations-a scenario in which the system displays incorrect, misleading, or nonsensical outputs even though it seems confident or believable. 

In the world of artificial intelligence, few technologies have captured as much attention as language models. These systems, which process and generate human language, range from compact algorithms running on basic devices to massive models powered by state-of-art supercomputers.  But what exactly sets a small language model (SLM) apart from a large language model (LLM)?

Natural language processing (NLP) and artificial intelligence (AI) have advanced quickly in recent years, resulting in the creation of language models of various sizes and levels of complexity. These include little language models, a class that has drawn interest due to its special qualities and usefulness.  However, how do little language models differ from their

Direct Preference Optimization (DPO) represents a methodology within the realms of machine learning and optimization that integrates user preferences or subjective evaluations directly into the optimization framework.  In contrast to conventional optimization techniques that depend exclusively on established objective functions, DPO prioritizes the alignment of outputs with human-like preferences, thereby improving relevance, usability, and overall

Machine learning (ML) is rapidly evolving, propelling advancements  across a wide range of industries. Diffusion models, one of the most recent advances in the field of generative models, have received a lot of attention.  These models represent a novel and sophisticated method to handle a wide range of machine learning challenges, particularly in image production,