The Unfiltered Naming Conventions of Open-Source AI
Description
A screenshot of a model repository, likely Hugging Face, displayed in a dark theme with a two-column layout. It lists numerous text generation AI models, most of which are from a user named 'TheDrummer'. The image highlights the absurd and often juvenile naming conventions prevalent in the open-source AI community. Some of the visible model names include 'TheDrummer/Smegmma-9B-v1-GGUF', 'TheDrummer/Smegmma-Deluxe-9B-v1', 'TheDrummer/Fook-Yi-34B-32K-v1-GGUF', 'TheDrummer/Moist-Miqu-70B-v1.1-GGUF', and 'TheDrummer/Cream-Phi-3-14B-v1-GGUF'. This meme satirizes the clash between the highly sophisticated, cutting-edge technology of large language models (LLMs) and the chaotic, meme-driven, and sometimes immature culture that names them. For experienced developers, the humor lies in recognizing the legitimate technical indicators (like '9B' for 9 billion parameters or 'GGUF' for a specific file format) juxtaposed with names you'd expect from a teenager's online gaming handle
Comments
7Comment deleted
We've democratized AI to the point where a model capable of passing the bar exam is named like it was conceived in a 4chan thread. This is either the peak or the pit of open-source, and I'm not sure there's a difference anymore
At this point the hardest part of shipping an open LLM isn’t quantization - it’s convincing the compliance team that “Smegma-9B” can coexist with the corporate brand guidelines
When your PR review comment says "needs better naming conventions" but the model with 22.3k downloads is literally called Fook-Yi-34B and somehow still outperforms your carefully architected enterprise solution
When your model repository reads like a teenager's Xbox Live gamertag history, you know the AI community has truly democratized. Nothing says 'production-ready enterprise solution' quite like deploying Moist-Miqu-70B to your Fortune 500 client's infrastructure - though explaining the naming convention to the compliance team might require more compute than training the model itself
The registry supports 32k context - enough for every v1, v1.1, and GGUF fork - but still not enough context to explain these model names to compliance or the SBOM
MLOps 2025: merge three checkpoints, slap a spicy name, convert to GGUF, call it v2 - because versioning is cheaper than provenance and the metric everyone tracks is downloads
In ML, version control isn't about code - it's hoarding model checkpoints no one dares merge