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).

MinerU2.5-Pro (1.2B) — Core AI whole-page document parsing

On-device whole-page document structuring on Core AI. A port of opendatalab/MinerU2.5-Pro-2605-1.2B (Apache-2.0, 1.2B) — a small, SOTA-quality document parser (OmniDocBench v1.6 95.69). Unlike a per-prompt recognizer, MinerU2.5 does the whole page in one model: a layout pass finds the blocks (title / text / table / formula / figure) and their reading order, then a recognition pass reads each region into plain text, HTML tables (<table>…), or LaTeX — assembled into structured Markdown / JSON. This is the piece the zoo's per-region OCRs (Unlimited-OCR, GLM-OCR) leave to a separate layout detector; MinerU folds it into the same weights.

MinerU2.5 is stock Qwen2-VL (Qwen2VLForConditionalGeneration): a Qwen2-VL ViT vision tower + a Qwen2-0.5B text decoder, no custom code. This port is the shipped Qwen3-VL / GLM-OCR vision idiom (the image_embeds + rope-shift static-input hook) with a Qwen2 text decode — no deepstack, no MoE, no MLA. The vision tower runs once; its image_embeds are injected at the image-placeholder positions (V + slot, row-major over the merged grid) and the text decodes on top. The host runs the model twice per page (layout prompt, then recognition prompts).

⬇️ Converted .aimodel bundles: mlboydaisuke/MinerU2.5-Pro-CoreAI — vision/ + decoder/ (768-token recognition grid, Qwen2-VL ViT fp16 + int8lin decoder, pf64 chunked prefill) + layout/ (a 1036² square recognition/detection grid for the 2-stage) + tokenizer/. Apache-2.0.

Architecture

• Vision (Qwen2-VL ViT): embed 1280 / 32 L / 16 heads (head 80) / patch 14 / temporal 2 / spatial-merge 2, out 896. LayerNorm blocks, fused qkv (bias), no q/k-norm, non-gated fc1 → quick_gelu → fc2 MLP, and the standard Qwen2-VL PatchMerger (ln_q → view(merge²) → Linear → GELU → Linear). No deepstack, no learned pos-embed — just baked 2D-rope constants. Exported as one fp16 .aimodel; N (visual tokens) is fixed by the export grid.
• Decoder (Qwen2-0.5B): hidden 896 / 24 L / GQA 14-2 / head_dim 64 / vocab 151936, tie_word_embeddings. Separate q/k/v with bias, no q/k-norm, standard 2-norm block, silu SwiGLU, sectioned M-RoPE [8,12,12] applied split-half (Qwen standard rotate_half — not GLM's interleave). Driven on the pipelined-engine S=1 contract: input_ids [1,1], position_ids, static image_embeds [N,896]
  • rope_shift_start + rope_shift_amount. Zero embeds + shift_start = 1<<30 → a plain Qwen2 text decoder.

Verified (M4 Max + iPhone 17 Pro, GPU, Core AI pipelined engine)

• iPhone 17 Pro, on-device, whole page read correctly — the h18p AOT bundles run through KitMineruReader in a real app (ReadDoc): a page photo → structured text (headings, paragraphs, tables) with nothing leaving the device. ~4 s/page (warm) via chunked prefill: the pf64 multifunction bundle feeds the 768 image tokens in static S=64 chunks (the engine auto-discovers the prefill function) then decodes S=1 — the S=1 prefill (~10 s) drops to ~2.9 s, no token cut.
• End-to-end real generation on the engine (shipped portrait 768 config): the sample page read verbatim — letterbox → CLIP-norm → non-square patchify → GPU vision .aimodel → image_embeds → AOT-compiled (h16c) int8lin S=1 decoder, autoregressive greedy: title + paragraph + the full table reconstructed row-by-row ("Quarterly Report / On-device inference shipped across all product lines this quarter… / Whisper 809M 0.18 s/token / …"), matching the fp32 reference. 211.7 tok/s decode (int8lin S=1, AOT h16c GPU). Portrait vision vs HF: per-token cos min 0.9975.
• Torch ladder vs HF fp32: text-only + vision + full-VLM argmax 706/706 exact, max logit diff 0.0001, the generation-driving token bit-identical.
• Engine gate (Mac GPU): vision image_embeds cos 1.0002; AOT int8lin decoder teacher-forced over 706 positions — text region 24/24 exact, generated token exact.
• int8lin vs fp16: 13 / 706 argmax flips, all at visual-token positions (0 in the text region) — the OCR text is preserved. Greedy generation byte-identical to fp16.

Run in app — KitMineruReader (Mac; iPhone single-pass)

Both grids ride the kit's VL rope-shift runtime (VLRuntime). Pages are CLIP-normalized and patchified (the VLImagePreprocessor non-square + .stretch/.aspectFitPad paths added for Qwen2-VL). Two entry points:

// Single-pass (768 grid): whole page → plain text (reading order). Runs on iPhone (chunked prefill).
let reader = try await KitMineruReader(catalog: "mineru2.5-pro")
let text = try await reader.read(imageAt: documentURL)

// 2-stage (structured Markdown, tables as <table> HTML). Needs the layout bundle too.
let reader = try await KitMineruReader(
    visionDir: rec.vision, decoderDir: rec.decoder,
    layoutVisionDir: layout.vision, layoutDecoderDir: layout.decoder)
let markdown = try await reader.readStructured(imageAt: documentURL)

2-stage (readStructured, mirrors mineru-vl-utils two_step_extract + json2md):

1. Layout on the page stretched into 1036² square (VLArchitecture.mineruLayout, 37×37 = 1369 tokens) → Layout Detection: emits <|box_start|>…<|ref_start|>type… blocks. A 768 portrait grid mis-detects here — the square high-res grid is required. Boxes are 0–1 of the square, so they map linearly onto the original page.
2. Recognition per region on the 768 grid (.mineru): each block is cropped from the original and read by type — Table Recognition: emits OTSL (<fcel>/<nl>), kept (not skipped) and converted to <table> HTML; Formula Recognition: → LaTeX; else plain text.
3. json2md joins the region contents in reading order.

Verified end-to-end in the ReadDoc Mac app: an 8-block page → title/paragraphs + a 5-row <table>, byte-identical to the reference. ~11 s load (both bundles) + ~11 s/page. The single-pass path also runs on iPhone; the 2-stage's 1036² layout grid is Mac-tier (two bundles, heavy).

Pipeline (host side)

page image → letterbox into 672×896 (aspect-fit + white pad) → CLIP-normalize
           → non-square patchify [3072, 1176] (block-major, 64×48 patches)
           → vision .aimodel → image_embeds [768, 896]
           → prompt: [ <|vision_start|>, <image>×768, <|vision_end|>, "Text Recognition:" ]
             (image ids → V+slot; shift_start = img_start+768, shift_amount = 768−32 = 736)
           → decoder S=1 pipelined greedy decode → tokens → detokenize → markdown / <table>HTML / LaTeX

The full 2-stage whole-page mode (layout boxes → per-region crop → recognition → json2md) follows opendatalab/mineru-vl-utils (MinerUClient.two_step_extract + post_process.json2md) — prompts by type: text → "Text Recognition:" · table → "Table Recognition:" · formula → "Formula Recognition:" · figure → "Image Analysis:".

Use / reproduce

• Convert: conversion/export_mineru_pipelined.py (fp16 / int8lin / int8hu; vision stays fp16).
• Run (Mac): drive the S=1 decoder bundle on the pipelined engine with three static inputs (image_embeds + rope_shift_start + rope_shift_amount) and COREAI_CHUNK_THRESHOLD=1; feed the prompt with the image placeholders rewritten to V+slot. Large decode graphs need AOT on macOS 27 (xcrun coreai-build compile … --architecture h16c --preferred-compute gpu --expect-frequent-reshapes); h18p bundles are prepared for iPhone.
• Knowledge: knowledge/mineru-port.md.

Notes

• Whole-page structuring is in the model (layout + per-region recognition, json2md reading-order assembly) — the value over per-region OCRs. The 2-stage orchestration is host-side (Python mineru-vl-utils is the source of truth; a Swift host is the app-integration step).
• License: base MinerU2.5-2509 is AGPL-3.0 — this port uses MinerU2.5-Pro-2605 (Apache-2.0).
• Appropriate input: single-page documents; layout runs on a downsampled page, recognition on native-res crops (pick a larger export grid for dense small text).
• int4 not shipped (weight-only int4 without QAT risks a quality cliff on a 1.2B model). iPhone (h18p) throughput pending device verification.
• Community port — not affiliated with Apple or OpenDataLab.

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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.