The Unfiltered Naming Conventions of Open-Source AI

Level 1: Serious Tech, Silly Names

Imagine you visit a zoo where each animal has a sign showing its name and some facts. You walk up to the mighty lion’s enclosure, expecting a sign that reads something like “Lion – 420 lbs, King of the Savannah.” Instead, the sign says “Mr. Fluffy Murdercat – 420 lbs”. Then you go see the elephants, and the sign on their pen says “Sir Trunky McBoogerton – 5,000 lbs”. It would feel pretty silly, right? The animals are big and impressive, but their names on the signs sound like jokes. You’d probably giggle and think, “Who on earth decided to call it that?”

This meme is showing a real-life version of that happening with AI models. These AI models are like the mighty lions and elephants of technology – really powerful and advanced. But the people who made them gave them names that are extremely goofy or gross, kind of like calling a lion “Murdercat” or an elephant “Boogerton.” For example, one very advanced text-generating AI model was named “Smegma-Deluxe” by its creator (that’s a pretty yucky word you wouldn’t normally say in polite company). Another big model got dubbed “Moist-Miqu,” which sounds just as odd and funny. Seeing names like that is so unexpected that it makes people laugh. It’s like if a genius inventor built a super-advanced robot and then decided to name it “Captain Booger.” You’d be amazed by the robot but also laughing at the ridiculously silly name.

In simple terms, the joke here is about serious stuff having very silly names. The tech community is marveling at how something as high-tech and important as a cutting-edge AI program can end up with a name that sounds like a playground prank. It’s both funny and a bit absurd, and that contrast is exactly why everyone who sees this can’t help but crack a smile.

Level 2: LLM Model Zoo

Let’s break down what’s happening in that meme for those not steeped in AI jargon. The screenshot shows a list of model names on a site called Hugging Face, which is basically a popular hub for sharing AI models (kind of like GitHub, but specifically for machine learning models and datasets). Each entry in the list follows the format UserName/ModelName. In this case, the user’s handle is TheDrummer, and all the model names start with that prefix. So think of TheDrummer like the account or organization, and things like Smegma-9B-v1 or Moist-Miqu-70B-v1.1 as the specific model names that user has published. There are 33 models listed, which is why the sidebar title says “Models 33”. That’s a lot of models from one person (or team)!

Now, let’s decode those funky model names. First off, LLM stands for Large Language Model. That’s a type of AI program designed to understand and generate text, basically by predicting words in a sentence (they learn from vast amounts of text data). Famous examples of LLMs are things like OpenAI’s GPT-4 or Meta’s LLaMA. One big characteristic of such models is their size, measured by the number of parameters (think of parameters as the millions or billions of adjustable knobs inside the neural network that get tuned during training). When you see “9B” or “70B” in a model’s name, that’s shorthand for “9 billion” or “70 billion” parameters. Generally, a 70B model is much larger and (potentially) more powerful than a 7B or 13B model, though it also needs more computing power to run. So, including the number is a bit of a brag and also informative — it tells users “this is a big model” (or a small one, if it said something like 3B).

The names also have version tags like v1, v1.1, v2 etc., which just mean the author has updated or altered the model and released newer versions. It’s similar to how software might have versions (1.0, 1.1, 2.0, etc.). So "Moist-Miqu-70B-v1.1" implies there was a v1.0 of Moist-Miqu-70B and this is a slightly improved or changed release.

You might also notice the suffix GGUF on some model names. This is essentially a format or file type for the model. Without getting too technical, GGUF is a specific way to package the model’s data so it can be loaded efficiently by certain tools (it’s related to a project called llama.cpp which lets you run these models on your own computer, often with compressed files). You can think of GGUF as analogous to a file extension like .zip or .pdf, but for AI models. In the list, some models have both a normal version and a "-GGUF" version, meaning TheDrummer provided the model in two formats: one standard and one optimized for that popular offline usage. If you’re new to this, don’t worry about the details — just know GGUF is a techie detail about how the model is stored, not part of the name’s “meaning.”

The real attention-grabber in those names is the choice of words like "Smegma", "Fook-Yi", "Moist-Miqu", "Cream-Phi", etc. These are very unconventional to say the least. In professional or academic settings, model names are usually neutral or descriptive (e.g. “ResNet50” for a type of image recognizer, or “EnglishTextGen-13B” for an English text generator with 13B parameters). But here, the naming has clearly gone off the rails into tongue-in-cheek territory. Some of those words are slang or have inappropriate connotations (for instance, "smegma" is something quite gross related to personal hygiene). Others are like puns or just random mashups. "Fook-Yi" sounds like a stylized way of writing a phrase that might not be polite. "Moist-Miqu" and "Moistral" feel like someone took the name of the Mistral model (which is a real open-source AI model) and gave it a silly twist (adding "o" to make "Moist", a word many people find a bit icky, as a joke). "Cream-Phi-3-14B" looks like a play on "cream pie" combined with the number 3.14 (pi – get it? phi instead of pie), so it’s mixing a crude joke with a nerdy math reference. In short, these names sound more like gamer tags or prank usernames than serious AI projects.

Why would someone name their models like this? Well, open source culture means there isn’t a boss or marketing department overseeing these releases. These are individuals (or informal groups) sharing their work for free, and they have the liberty to be irreverent or whimsical. Sometimes it’s done to be funny, sometimes to stand out, or maybe just because the person thought the name was cool in a meme-y way. It’s similar to how some indie game developers or app makers give their projects funny names just because they can. There’s also a bit of one-upmanship in the community — with so many models coming out, a wacky name might get your model a bit more attention on social media or forums. (After all, here we are talking about it!)

Now, from a newbie developer’s standpoint, this is a lighthearted lesson in the importance (and challenge) of naming conventions. In software development, having clear and sensible names for things (be it variables, functions, services, or models) is considered a best practice. When names get random or overly funky, it can confuse people. There’s a famous tongue-in-cheek saying: “The two hardest problems in programming are naming things and cache invalidation.” That’s just a nerdy way of saying, yes, even choosing good names is hard in coding! In big projects, teams often establish guidelines for naming (a.k.a conventions) so that things stay somewhat uniform and understandable. For example, a convention might be “microservice names should reflect what they do” or “variable names should be in camelCase and meaningful.” If you’ve just started coding, you might have already encountered advice like “use descriptive names” instead of calling everything x or temp in your code.

The meme exaggerates what happens when there’s no convention or oversight: people start using names as an outlet for humor or personal references. It’s funny, but it can also be chaotic. Imagine joining a project and seeing functions named after Pokémon or Star Wars characters — you’d probably be a bit lost on what’s what. That’s essentially what’s happening in the AI model scene here. There’s a ton of new models (thanks to the current AI boom), and without any rulebook every model creator is just doing their own thing. Some name their models professionally, and others, well, go for names that might make you chuckle or cringe.

Finally, why is this meme-worthy? Because it’s highlighting an absurd contrast. These AI models are not toys; many of them are serious technical achievements (some have tens of billions of parameters and can do really advanced text generation). Yet, seeing them presented with names like a bad joke book is unexpected. It’s the kind of thing that makes developers laugh and think, “Classic, we can build groundbreaking tech, but we still can’t agree on not using goofy names.” It also lightly pokes at the hype in the AI industry — there’s so much craze to create the next big model that things are getting a bit wild and unstructured (hence the “zoo”). For someone just learning about this field, the key takeaway is: the technology may be complex, but the people behind it are human. They get fixated on trends (AI hype), they compete to release new stuff, and sometimes, they give those projects ridiculously silly names just because. It’s a funny reminder that even in cutting-edge tech, we have inside jokes and moments where everyone just shakes their head and laughs.

Level 3: Naming Off the Rails

In the open-source LLM world, it looks like the rulebook for naming conventions got completely thrown out the window. Case in point: a Hugging Face model repository where a user (going by TheDrummer) has listed 33 different AI models, all with names that range from bizarre to borderline NSFW. Each model name is an absurd mash-up of juvenile humor and serious technical labels. You’ve got entries like TheDrummer/Smegma-9B-v1-GGUF and TheDrummer/Fook-Yi-34B-32K-v1-GGUF staring you in the face. At first glance, “Smegma-Deluxe-9B-v1” might sound like a limited-edition sandwich gone horribly wrong, but nope — that’s supposed to be an AI text generator with 9 billion parameters. The sheer volume of these names, combined with official-sounding version tags (like v1.1) and big model sizes (the 8B, 34B, 70B suffixes, denoting billions of parameters), creates a jarring contrast. It’s like reading a list of enterprise microservices where the devs lost a bet and had to use Cards Against Humanity words for names.

For any developer who’s survived microservice sprawl, this chaotic list of model names triggers a very specific déjà vu. In large systems, every new microservice needs a unique name. Initially teams start with sensible, even if dull, choices like auth-service or payment-processor. But as the count of services climbs into the dozens, people get creative (or desperate). Before you know it, you’re deploying containers named kraken-dev or unicorn-frappuccino that no one outside the original team understands. This meme’s scenario is the AI version of that. We have an open LLM zoo of models, each a slightly tweaked variant, and their creators are clearly entertaining themselves with naming. With no naming governance in this open-source Wild West, anything goes. One prolific contributor basically said, “I’m gonna fine-tune all these language models and I’ll call ’em whatever I darn well please.” The result? A model sprawl directory where highbrow tech meets lowbrow humor.

It’s equal parts hilarious and head-scratchingly frustrating. On one hand, you admire the rapid innovation – 70B-parameter models being updated on a whim and shared for free – that’s the current AI/ML gold rush for you. On the other hand, you might have to tell your team with a straight face, “Hey, we’re going to test Moist-Miqu-70B-v1.1-GGUF this week,” and watch coffee shoot out of someone’s nose. The naming free-for-all highlights the eternal struggle of naming things in software development. There’s that classic quip:

“There are only two hard things in Computer Science: cache invalidation and naming things.”
— Phil Karlton

Well, here we see why. When faced with the umpteenth variant of a model, coming up with a clear, dignified name is apparently so hard that people just give up and go full troll. If you can’t find a unique, meaningful name, you might as well pick one that makes people smirk or cringe. NamingConventions? More like naming anarchy.

Look at some of these gems: Llama-3S0ME-8B-v2 is obviously a play on Meta’s respectable LLaMA model name, cheekily inserting “3SOME” – yes, as in that kind of threesome. Real mature, guys. Moist-Miqu-70B sounds like a corrupted cousin of Mistral 7B (a well-regarded open model) with an extra letter that turns a cool wind into something… damp and uncomfortable. Then there’s Cream-Phi-3-14B – which reads like “Cream Pie 3.14B” – mixing a math pun (π -> 3.14) with a not-so-subtle innuendo. And the headliner of the bunch: the Smegma series (there are multiple entries with that name!). If you don’t know what “smegma” is, do yourself a favor and don’t google it on your work computer. Let’s just say it’s not something you’d usually name a cutting-edge AI model after. These ridiculous model names are what you get when contributors are unchecked and perhaps a bit too amused with themselves. It’s a spectacular naming_governance_fail in action.

For a newcomer or an outsider, stumbling onto this Hugging Face list is downright confusing. You’d be forgiven for thinking it’s a joke page or some edgy teenager’s portfolio. But no – these are legitimate text-generation checkpoints that people are actively downloading. (Some have thousands of downloads and dozens of likes, meaning folks out there are actually trying out “Smegma-Deluxe” in earnest.) This absurd naming trend is partly a side effect of the current AI hype cycle we’re in. Ever since models like GPT-3, ChatGPT, and Meta’s LLaMA showed the world what’s possible, there’s been an explosion of open-source replicas and fine-tuned offshoots. Everyone wants to stake a claim in the LLM gold rush. To stand out from the crowd, you either need superior performance or – apparently – a scandalous name. It’s a bit of a flex: not only do these model creators flaunt the parameter count in the name (that parameter_count_flex like “70B” is basically them saying “look how huge my model is”), but they also flaunt that they don’t have a boss or PR team telling them “No, you absolutely cannot call our flagship AI model ‘Moist-Miqu.’”

From an industry perspective, this is a fascinating (if absurd) study in open-source culture. With no corporate branding police around, naming models becomes almost like picking gamer tags or Reddit usernames – wild, woolly, and often wacky. It harks back to the early internet days when hobbyist programs or Linux builds had cheeky names (hello, Slackware, and the WTFPL license). The difference now is that these cheekily named projects aren’t just little scripts; they’re massive AI systems with serious capabilities. The IndustryTrends_Hype factor is that we’ve democratized building powerful models – but we sure haven’t democratized good taste in naming.

Ultimately, the humor of this meme hits a nerve in developer culture. There’s the surface-level laugh at seeing dirty words and silly phrases where they don’t belong. But deeper down, every engineer recognizes a pattern we’ve seen before: unchecked growth and the chaos that comes with it. Whether it was a company’s microservices or NPM packages, we’ve lived through that “Wild West” phase where names get out of control. The more things change, the more they stay the same. Now it’s the open-source AI community reliving that lesson in real time. Naming things is still hard, herd behavior still makes a mess, and we’re left with a list of models that’s as impressive as it is absurd. As a seasoned dev, you can’t help but chuckle (and maybe facepalm) at the fact that in 2024 we have world-class AI models named like bad teenage graffiti. The meme shines a light on this moment in tech – a snapshot of innovation colliding with irreverence – and it’s gloriously ridiculous.