CoreAI Model Zoo

CoreAI-Model-Zoo is an open-source community repository that converts open-weight LLMs into Apple’s Core AI `.aimodel` format — the successor to CoreML — and makes the results downloadable, verified, and reproducible. The core workflow is: download a pre-converted model from the linked Hugging Face repos, drop it into an iOS 27 or macOS 27 project, and run inference through the included Swift runner. For teams who need to convert their own models, the `conversion/` directory contains the scripts and the documented gotchas that are otherwise scattered across Apple developer forums.

The differentiating feature is verification. The models are not just converted and uploaded — they are confirmed to run on actual hardware (iPhone 17 Pro GPU and ANE), with notes on what broke during conversion and what workaround was applied. That paper trail is the part Apple’s own documentation does not provide, and it is what separates this from a generic model dump.

The repository fits teams doing early-stage on-device AI development on Apple platforms who need a working baseline fast. It breaks — or rather, becomes irrelevant — the moment a project requires cross-platform deployment, a REST inference API, production monitoring, or any model family outside the zoo’s current coverage. There is no versioning policy, no SLA, and the model selection reflects one maintainer’s conversion priorities. Teams that outgrow the zoo’s model list will use the conversion code as a template but end up maintaining their own fork.

The `knowledge/` directory documents Core AI-specific behavior: quantization constraints, MoE handling, MLA architecture notes for GLM-4.7-Flash, and custom Metal kernel experiments for benchmarking ANE versus GPU throughput. The `apps/` directory provides reference Swift integrations. No CI pipeline for model validation is documented in the scraped content, so freshness of any given model file depends on maintainer cadence.