mlboydaisuke/mistral-7b-v0.3-CoreAI-official

Mistral 7B Instruct v0.3 — official Apple Core AI export

Pre-converted .aimodel bundles from Apple's official coreai-models export recipe — unmodified, with the exact environment, hashes, and measured performance published.

uv run coreai.llm.export mistral-7b-instruct-v0.3

Why pre-converted bundles?

1. The conversion needs a big-RAM Mac (the 20B export was done on 128 GB); running only needs enough RAM to mmap the artifact.
2. An .aimodel is a build artifact, not a pure function of the recipe — the same export command produced a 2.2× slower artifact across the macOS 26 → 27β boundary (forensics). Hosted artifacts + hashes are the reproducible ground truth; every bundle here is exactly the one measured in apple-silicon-llm-bench.

Bundles & integrity

Bundle	Contents	SHA-256 (main.mlirb)
macos/	macOS dynamic, int4	81c422124f0ccbf7e5e325a846e77656a4efeef0fc70bf0c5e1dfeb48de7581e

Measured (Apple's official llm-benchmark, greedy)

Bundle	Protocol	Decode tok/s	Prefill	Load (warm)	Peak RSS
macos	M4 Max, 512p/1024g	101.7	976	0.56 s	8.3 GB

Heads-up if exporting yourself: the source repo downloads 27 GB (it ships duplicate consolidated.safetensors); this bundle skips all that.

Export environment

• macOS 27.0 beta (build 26A5353q) · Xcode 27.0 (27A5194q)
• coreai-core 1.0.0b1 · coreai-torch 0.4.0 · coreai-opt 0.2.0 · torch 2.9.0
• apple/coreai-models @ b1cb71b (export code identical to upstream 0c1055f)

Run it

# CLI (from a coreai-models checkout)
swift run -c release llm-runner --model <downloaded-bundle-dir> --prompt "Hello"
swift run -c release llm-benchmark --model <downloaded-bundle-dir>

Or chat with it in CoreAIChatMac (point "Choose Models Folder…" at the download directory).

iOS static bundles must be AOT-compiled before device use: xcrun coreai-build compile <ir>.aimodel --platform iOS --preferred-compute neural-engine --architecture h18p (h18p = iPhone 17 Pro), then set metadata.json assets.main to the .aimodelc.

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Maintained alongside coreai-model-zoo (community models) and coreai-samples (apps).