coreml

Features and purpose

Use when deploying custom ML models on-device, converting PyTorch models, compressing models, implementing LLM inference, or optimizing CoreML performance. Covers model conversion, compression, stateful models, KV-cache, multi-function models, MLTensor.

Quick start

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.