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

Name: mistral-7b-v0.3-CoreAI-official
Availability: InStock

mistralai/Mistral-7B-Instruct-v0.3

finetune

apache-2.0

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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, 2026-06).

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

Use it

⚡ One line — run the kit's task op on this model (import CoreAIOps; no session, no model plumbing, downloads on first use):

let tldr = try await CoreAI.summarize(text, options: .model("mistral-7b-v0.3"))

Every op, one shape — Cookbook.

▶️ Run it (source) — the ChatDemo runner (GUI + CLI, one app for every chat model in the catalog):

bash

git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/ChatDemo/ChatDemo.xcodeproj
# → Run, then pick "Mistral 7B v0.3" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/ChatDemo
swift run chat-cli --model mistral-7b-v0.3 --prompt "What can you do, offline?"

💻 Build with it — complete; the glue is kit API, copy-paste runs:

swift

import CoreAIKit

let chat = try await ChatSession(catalog: "mistral-7b-v0.3")
let reply = try await chat.respond(to: prompt)
// reply: the answer, generated fully on-device

The take-home is Examples/ChatDemo/Sources/QuickStart.swift — this exact code as one typed function, no UI; the CLI is an argument shell over it, and the GUI drives the same ChatSession across turns for its transcript. Multi-turn? Hold the ChatSession and call respond(to:) per turn — it keeps the conversation history; streamResponse(to:) yields tokens as they decode.

Integration checklist

SPM: https://github.com/john-rocky/coreai-kit → product CoreAIKit
Info.plist: none needed
Entitlements: none needed (macOS)
First run downloads the model — 4.1 GB (Mac) — then it loads from the local cache (Application Support; progress via the downloadProgress callback)
Measure in Release — Debug is ~3× slower on per-token host work

Why pre-converted bundles?

The conversion needs a big-RAM Mac (the 20B export was done on 128 GB); running only needs enough RAM to mmap the artifact.
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

bash

# 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.

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

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.

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