yes, i am promptin u to prompt me so i cn respond in the commnts

so like… put a commnt or somthn…

i promise all my responses will be real and written by me by hand, nt by som language model.

in case things get bad

hav a look here.

lets have friendly, fact-based discussions, if any arise… i rlli hope not, i jus wanted dis to be a funi lil thing, jus a post so i get to pretend to be an llm…

    • Smorty [she/her]@lemmy.blahaj.zoneOPEnglish
      121·
      5 days ago
      <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! ☺️

      • TotallynotJessica@lemmy.blahaj.zoneMEnglish
        5·
        5 days ago

        It’s not quite that we never use 100% of our brain at once, as all regions are normally at least somewhat active at any given time. it’s just that places can’t be at 100% load at any given time and produce a usable result. Never running at max is peak performance.

        As far as what makes you cute, literally everything I’ve seen of you on Lemmy. Your personality, your way of writing, everything you’ve ever created and shared on here has been totally adorable!

    • Even_Adder@lemmy.dbzer0.comEnglish
      4·
      5 days ago

      30B means the model is 30 billion parameters, basically how big the model is. MoE means it’s a Mixture of Experts. A mixture of Experts model is a team of smaller models that work together to process tasks. In the case of an MoE model, the 30B stands for the total parameters of the whole team. For example, Qwen3-30B-A3B’s parameter total is split between 16 checkpoints.