Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11).

This model has no row on DeviceMark, the on-device LLM leaderboard.

Ornith-1.0-9B — Core AI (agentic coder, 48–59 tok/s on M4 Max)

Apple Core AI (.aimodel) conversion of deepreinforce-ai/Ornith-1.0-9B (text decoder): DeepReinforce's self-scaffolding agentic-coding model — trained to jointly solve coding tasks and construct the orchestration scaffold that guides the solution. Architecturally a Qwen3.5 hybrid decoder (model_type qwen3_5): 32 layers on a 3:1 interleave of GatedDeltaNet linear-attention mixers and gated full attention, untied 248320-vocab head. Runs fully on-device on Apple silicon via Apple's coreai-pipelined GPU engine.

Part of the community Core AI model zoo: https://github.com/john-rocky/coreai-model-zoo (full card: zoo/ornith-1.0-9b.md).

Bundles

Both are decode-only loop-free S=1 LanguageBundles (max_ctx 8192, tokenizer + chat template embedded), gated against a margin-validated fp32 HF oracle (min top-2 margin 0.205; the eager fp16 baseline is also 24/24, so int8/int4 add zero flips).

Recipe: linear int8/int4 per-block-32 body + absmax symmetric per-block-32 int8 on the untied big-vocab head (int8hu). Conversion is the zoo's stock Qwen3.5 script with nothing but an --hf-id swap — see conversion/README.md.

Run it (macOS)

Easiest: CoreAIChatMac (the zoo's Mac chat app — apps/CoreAIChatMac) downloads this repo in-app: pick Ornith-1.0-9B in the Downloads panel.

CLI (needs the zoo's coreai-models checkout + the pipelined extra-states patch):

COREAI_CHUNK_THRESHOLD=1 llm-benchmark \
    --model ornith_1_0_9b_decode_int8hu_block32_sym -p 128 -g 256 -n 3
COREAI_CHUNK_THRESHOLD=1 llm-runner \
    --model ornith_1_0_9b_decode_int8hu_block32_sym \
    --prompt "Write a rate limiter in Swift." --sampling-strategy greedy \
    --warmup exact --warmup-length 1

Notes: COREAI_CHUNK_THRESHOLD=1 before engine creation; never engine.warmup() on an S=1 bundle; Release builds only. Details + every trap: knowledge/pipelined-engine.md.

iPhone

Not this one (yet): int8 at 9.8 GB exceeds the entitled jetsam ceiling (~6.4 GB on an iPhone 17 Pro). The arithmetic route is int4 body + int8 head (≈6.5 GB) or an in-graph int8 embed table (≈5.5 GB) — tracked in the zoo card.

License

MIT — as declared by the upstream model card (deepreinforce-ai/Ornith-1.0-9B; the upstream repo ships no LICENSE file, so none is mirrored here). Conversion scripts and harness: see the zoo repo.

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More models in this format: Core AI Model Zoo — 75 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.