LLM Fine Tuning

The rapid advancement of natural language processing (NLP) has revolutionized the way robots interact with human language, enabling applications from chatbots to content production. Fine-tuning pre-trained language models to tailor general-purpose models, like GPT or BERT, to particular applications is the foundation of many of these advancements. However, the quality of the data used for […]

In the world of artificial intelligence, large language models (LLMs) have revolutionized how we interact with technology. From chatbots to personalized assistants, these models demonstrate remarkable versatility.  However, to make them truly effective for specific tasks or domains, fine-tuning becomes essential. Fine-tuning is the process of taking a pre-trained model and adapting it to perform

Large Language Model (LLM) has brought remarkable advancements to the field of natural language processing (NLP), enabling impressive feats like text generation, language translation, and summarization. To fully leverage these models’ capabilities, two main techniques are employed: fine-tuning and prompt engineering.  Every method has its own unique benefits and is suitable for different applications, making

Recent developments in the domain of LLMs (large language models) have brought a paradigm shift in the industry of AI, offering different facilities across a vast range of tasks, from natural language processing to content generation. To fully make use of their potential in specialized applications, it is necessary to employ a fine-tuning process.  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