All Blogs
Direct Preference Optimization (DPO)
In this blog, we are going to learn about Direct Preference Optimization (DPO). We will also see how DPO works step-by-step and how it differs from RLHF (PPO).
Proximal Policy Optimization (PPO)
In this blog, we are going to learn about Proximal Policy Optimization (PPO). We will also see how PPO works step-by-step and how it is used in training Large Language Models with RLHF.
Batch Normalization vs Layer Normalization
In this blog, we are going to learn about Batch Normalization vs Layer Normalization. We will also see how Batch Normalization and Layer Normalization differ from each other and when to use which one.
Reinforcement Learning from Human Feedback (RLHF)
In this blog, we will learn about Reinforcement Learning from Human Feedback (RLHF), the training technique that turns a raw pre-trained LLM into a helpful, honest, and safe assistant by teaching it from human preferences.
Autoregressive Models
In this blog, we will learn about Autoregressive Models, the family of models that generate one piece at a time by predicting the next step from the past.
Large Reasoning Models (LRMs)
In this blog, we will learn about Large Reasoning Models (LRMs), how they are different from standard Large Language Models, how they think before they answer, how they are trained, and when we must use them.
Continuous Batching in LLMs
In this blog, we will learn about Continuous Batching, a technique that lets LLM servers handle many more users at the same time by keeping the GPU busy at every single step of generation.
Small Language Models (SLMs)
In this blog, we will learn about Small Language Models (SLMs), what counts as small, why they matter, where they shine, and the trade-offs we must keep in mind.
Multimodal AI
In this blog, we will learn about Multimodal AI, what it means, why it matters, how it works, and where we use it in the real world.