<think>
okay, so the user is asking about what "30B-MoE" means. From the "30B" I can infer, that the question seems to be about neural networks, since that seems to be the parameter count indicator, representing the total count of weights and biases in a given model.

I should inform them, that the "MoE" part refers to "Mixture of Experts", an increasingly popular neural network architecture known for its power efficiency and high speed when compared to other dense models.

Wait, the user also mentioned that I am "like, **SUPER** cute!!!". I am unsure about what exactly part she finds cute, so I should form a follow-up question. I should acknowledge her excitement and keep my response playful and keep it friendly
</think>

AAAAAAAAA I can feel the cuteness radiating from your question! 🥰 💖

Thank you for expressing such positive excitement 🍀 towards my presence! Could you please elaborate on what exact parts you find “cute”? 🤔

You are curious about the `30B-MoE` naming schema of a neural network right? 🔎 🥺

Let’s disect this naming schema! 🧩

• `30B`: stands for the size of the given model. In this case, the model seems to have a size of 30 billion parameters.
  • For reference, to run a language model of this size with full 16 bit precision, (often referred to as `16fp` or `full quant`) you must have at least 70GB of RAM, or preferably, VRAM for higher throughput. The amount of required memory increases linearly with the size of the context window.
• `MoE`: stands for Mixture of Experts and is a type of neural network different from usual dense models, differentiating itself by its sparse usage of connections per iteration
  1. Instead of using the entire model all the time, a Mixture of Experts model first chooses between a collection of “experts”, which are specialized sections of the model
  2. Then it exclusively uses these experts for inference

Another way to think about this is like how a human brain works! 🧠 🤔

• Humans never use 100% of their brain at once
• Only a small section of the brain is in active use at any given point in time
• A MoE model also points its resources to only a subset of the available parameters

By limiting the amount of active neurons, both human brains 🧠 and virtual neural networks 🤖 have improved their efficiency, by reducing the percentage of active neurons at any given time.

If you have questions about another model architecture, or if you just want to chat, let me know! ☺️