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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-VL-4B-Instruct
+tags:
+- coreai
+- apple
+- ios
+- macos
+- on-device
+- vision-language
+- vlm
+- qwen3-vl
+---
+
+# Qwen3-VL 4B — Core AI (`.aimodel`)
+
+`Qwen/Qwen3-VL-4B-Instruct` converted to Apple **Core AI** (`.aimodel`, iOS 27 /
+macOS 27): image+text → text fully on the GPU via Apple's `coreai-pipelined`
+engine, zero custom kernels. The 4B sibling of the
+[Qwen3-VL 2B](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI) port — it
+drops onto the **same recipe with zero code changes** (the model overlay and
+exporter are fully config-driven).
+
+Part of the [CoreAI-Model-Zoo](https://github.com/john-rocky/coreai-model-zoo);
+full card with the conversion design:
+[zoo/qwen3-vl.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/qwen3-vl.md).
+
+## Measured
+
+| platform | prefill tok/s | decode tok/s | numerics |
+|---|---:|---:|---|
+| M4 Max (macOS 27 beta) | **93.3** | **92.2** | torch ladder vs fp32-HF (positions exact, vision cos 1.000, 36/36 layers cos 1.000, decode 16/16) + engine ≡ python 24/24 on the 211-tok multimodal prompt |
+| iPhone 17 Pro (iOS 27 beta) | 10–15 | **14.0 cool → ~8.5 sustained** | nat 24/24 + multimodal oracle 24/24 × 3 runs, token-identical to Mac |
+
+Decode is bandwidth-bound: the 4.7 GB int8hu decoder reads ~4.7 GB/token, so
+it runs at roughly half the 2B's rate. On iPhone the read is heavy enough to
+**thermally throttle** — ~14 tok/s from a cool start, settling to ~8.5 under
+sustained decode. Device cold load 52.7 s (on-device GPU specialization, no
+AOT), warm 8–9 s; needs the increased-memory entitlement (4.7 GB class).
+
+## Files
+
+| path | what | size |
+|---|---|---:|
+| `gpu-pipelined/qwen3_vl_4b_instruct_decode_int8hu_s1/` | text decoder LanguageBundle (SHIP: int8 per-block-32 body + untied absmax int8 head; tokenizer + metadata included) | 4.7 GB |
+| `gpu-pipelined/qwen3_vl_4b_instruct_vision/` | fixed-grid vision encoder (448×448 → 196 tokens + DeepStack), fp16 | 0.79 GB |
+
+## How it works (short version)
+
+The text-only pipelined engine carries the VLM through an id-space trick —
+no engine code changes beyond the published
+[static-inputs patch](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps):
+
+- the vision encoder runs once per image; its embeddings ride **4 static
+  graph inputs** (rewritable owned `MTLBuffer`s),
+- the prompt's `<|image_pad|>` ids become **extension ids `vocab + slot`**;
+  the graph selects text-table vs image-embed rows per token and applies the
+  three DeepStack adds the same way,
+- **interleaved M-RoPE is derived in-graph from (ids, position) alone** —
+  image tokens self-locate, text tokens use a host-set shift; with zero
+  embeds the same bundle is a plain Qwen3 text LLM.
+
+Numerics are gated the zoo way: fp32-HF oracle → torch ladder (position
+formula exact vs `get_rope_index`, 36/36 layers) → `.aimodel` GPU → engine ≡
+python 24/24 → device 24/24.
+
+## Run it
+
+See the zoo's `apps/CoreAIChat` (iOS) Qwen3-VL mode and the run contract
+(S=1 prefill, `COREAI_CHUNK_THRESHOLD=1`, never `engine.warmup()`) in
+[knowledge/pipelined-engine.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).
+
+Conversion is reproducible from the zoo:
+`conversion/export_qwen3_vl_pipelined.py int8hu --hf-id Qwen/Qwen3-VL-4B-Instruct`.
+
+## License
+
+Apache-2.0 (inherited from Qwen3-VL-4B-Instruct). Conversion code BSD-3-Clause
+(zoo repo).