Introducing our Artifacts Hub and Adoption Dashboard
- Source
- interconnects.ai
- Author
- Nathan Lambert
- Date
It gives engineers a single place to see which open models are actually gaining real-world traction (downloads, derivatives, volume) rather than just scores, plus a rare geographic/organizational lens on the US-China open model adoption gap.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
- benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
“The Artifacts Hub right now covers 792 models released in the last two years, across the core text-focused language models and multimodal generative models.”
“At Interconnects we follow the data of every model on Hugging Face, analyze the core few thousand LLMs (this list is public on GitHub and regularly updated), and hand select these core few hundred for further explanation.”
“Right now, as the world figures out how to use open models productively — especially in cost-competitive ways to frontier models — providing more transparency on what is happening is the best way for us to figure out what is working.”
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