CoreAIKit

VLChat — the vision-language runner (GUI + CLI, any vlm catalog id)

Pick a photo, ask about it, and a vision-language model running entirely on your device answers. No network, no cloud — the image never leaves the device.

The take-home is Sources/QuickStart.swift: one typed function (image + prompt → answer), no UI. The CLI is an argument shell over it; the GUI drives the same KitVisionModel(catalog:) gesture behind a FoundationModels LanguageModelSession:

let vlm = try await KitVisionModel(catalog: "qwen3-vl-2b")   // downloads decoder + vision tower
let session = LanguageModelSession(model: vlm)
let image = try ImageFile.load(imageURL)  // any image file → CGImage + EXIF orientation
let answer = try await session.respond(to: Prompt {
    "What is in this photo?"
    Attachment(image.cgImage, orientation: image.orientation)
})

Under the hood KitVisionModel/KitVisionExecutor/VLRuntime:

• create the decoder engine with the four static multimodal inputs the VL bundle declares (image_embeds, deepstack_embeds, rope_shift_start, rope_shift_amount) — the stock load path can’t, which is why a generic KitLanguageModel rejects a VL bundle;
• run the paired vision tower (a plain .aimodel) through CoreAIKit’s GraphModel and write the embeds into those buffers;
• splice the vision block into the prompt and rewrite the image-pad ids to the decoder’s extension id space, so the picked photo’s pixels actually reach the model.

Models

The picker lists every vlm entry in the catalog (ModelCatalog.builtin.available(.vlm)): Qwen3-VL 2B / 4B (iPhone + Mac) and 8B (Mac-only — its decoder exceeds the iPhone jetsam ceiling). Downloaded on first load and cached.

Build & run

open VLChat.xcodeproj   # run on My Mac, or on an iPhone (iOS 27)

# agents / headless (macOS):
swift run vlchat-cli --model qwen3-vl-2b --image sample.jpg --prompt "What is in this image?"

Run the GUI in Release — the engine’s per-token host work is ~3× slower unoptimized.

Notes

• One image per conversation (the latest photo you pick). Picking a new photo starts a fresh chat about it.
• The VL path streams the answer; Qwen3-VL is a thinking model, but the Instruct weights answer image questions directly.
• Tool calling / guided generation are not wired for the VL model — use the text KitLanguageModel for those.

This site is open source. Improve this page.