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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-VL-8B-Instruct
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tags:
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- coreai
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- apple
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- macos
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- on-device
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- vision-language
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- vlm
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- qwen3-vl
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---
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# Qwen3-VL 8B — Core AI (`.aimodel`)
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`Qwen/Qwen3-VL-8B-Instruct` converted to Apple **Core AI** (`.aimodel`,
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macOS 27): image+text → text fully on the GPU via Apple's `coreai-pipelined`
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engine, zero custom kernels. The 8B sibling of the
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[Qwen3-VL 2B](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI) port —
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**same recipe**, with one one-line loader change for its **untied** LM head.
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> **Mac-only.** The 8.7 GB int8hu decoder exceeds the iPhone increased-memory
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> jetsam ceiling (~6.4 GB class). For on-device iPhone use, see the
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> [4B](https://huggingface.co/mlboydaisuke/Qwen3-VL-4B-CoreAI) or
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> [2B](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI) ports.
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Part of the [CoreAI-Model-Zoo](https://github.com/john-rocky/coreai-model-zoo);
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full card with the conversion design:
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[zoo/qwen3-vl.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/qwen3-vl.md).
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## Measured
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| platform | prefill tok/s | decode tok/s | numerics |
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|---|---:|---:|---|
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| M4 Max (macOS 27 beta) | **54.4** | **54.3** | torch ladder vs fp32-HF incl. untied head + depth-27 ViT (vision cos 1.0001, 36/36 layers cos 1.000, decode 16/16) + engine ≡ python 24/24 on the 211-tok multimodal prompt |
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Decode is bandwidth-bound: the 8.7 GB int8hu decoder reads ~8.7 GB/token.
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Vision encode runs once per image. Cold GPU specialization ~16.5 s, warm load
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a few seconds.
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## Files
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| path | what | size |
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|---|---|---:|
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| `gpu-pipelined/qwen3_vl_8b_instruct_decode_int8hu_s1/` | text decoder LanguageBundle (SHIP: int8 per-block-32 body + untied absmax int8 head; tokenizer + metadata included) | 8.7 GB |
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| `gpu-pipelined/qwen3_vl_8b_instruct_vision/` | fixed-grid vision encoder (448×448 → 196 tokens + DeepStack), fp16 | 1.1 GB |
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## How it works (short version)
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The text-only pipelined engine carries the VLM through an id-space trick —
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no engine code changes beyond the published
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[static-inputs patch](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps):
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- the vision encoder runs once per image; its embeddings ride **4 static
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graph inputs** (rewritable owned `MTLBuffer`s),
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- the prompt's `<|image_pad|>` ids become **extension ids `vocab + slot`**;
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the graph selects text-table vs image-embed rows per token and applies the
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three DeepStack adds the same way,
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- **interleaved M-RoPE is derived in-graph from (ids, position) alone** —
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image tokens self-locate, text tokens use a host-set shift; with zero
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embeds the same bundle is a plain Qwen3 text LLM.
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The 8B differs from 2B/4B only in configuration: its LM head is **untied**
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(a separate `lm_head.weight`, quantized int8 absmax like the body) and its
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ViT is larger (depth 27, hidden 1152) — both absorbed by the config-driven
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overlay. Numerics are gated the zoo way: fp32-HF oracle → torch ladder
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(36/36 layers) → engine ≡ python 24/24.
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## Run it
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Conversion is reproducible from the zoo:
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`conversion/export_qwen3_vl_pipelined.py int8hu --hf-id Qwen/Qwen3-VL-8B-Instruct`.
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For the run contract (S=1 prefill, `COREAI_CHUNK_THRESHOLD=1`), see
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[knowledge/pipelined-engine.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).
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## License
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Apache-2.0 (inherited from Qwen3-VL-8B-Instruct). Conversion code BSD-3-Clause
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(zoo repo).
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