The short answer A dense model runs every parameter on every token. A mixture of experts instead routes each token through a few chosen experts. Mixtral’s router selects 2 of 8 experts. So it touches only 13B parameters per token,…
The short answer RLHF and DPO both start from the same preference pairs, one answer marked over another. RLHF trains a separate reward model first, then runs reinforcement learning against it, usually PPO. So DPO skips that separate network and…
The short answer Self-attention and cross-attention run the same formula. Only the source of Q, K and V differs, though. In self-attention, one sequence supplies all three matrices, unlike cross-attention. The paper puts it plainly: queries, keys and values all…
The short answer BERT (Bidirectional Encoder Representations from Transformers) vs GPT (Generative Pre-trained Transformer) is not a rivalry between two general-purpose models. Both come from the same 2017 Transformer paper, which described an encoder and a decoder together. BERT kept…
The short answer MCP (Model Context Protocol) vs function calling is not a choice between two rivals. Function calling is a capability of the model’s application programming interface (API). The application sends tool definitions with the request. Then the model…
The short answer Temperature vs top-p differ in what they touch inside a model’s output. Temperature rescales every logit before softmax runs, so it reshapes the whole probability distribution. Below T = 1 it sharpens the leading token’s share. Above…
The short answer LoRA vs QLoRA vs full fine-tuning comes down to what gets frozen and what gets trained. Full fine-tuning updates every parameter, so memory has to hold the weights, the gradients, and the optimizer state together. For a…
The short answer Quantization vs distillation solves the same problem in different ways. Quantization keeps the same model and stores each weight in fewer bits. The parameter count never changes; only the bytes per weight do. Distillation, instead, trains a…
The short answer MCP and A2A solve two different problems. They are not the same problem in different clothes. MCP connects one agent to its own tools and data. That connection runs vertically, from the agent down to the tools.…
