This release is a pre-release and may not be stable for production use.
mlx-beam-vision
The towers that let mlx-beam see. Install
it next to the engine (pip install mlx-beam[vision], or this package by
name) and a checkpoint that ships a vision tower serves images: an
image_url part with a data: URL in a chat request, the checkpoint's own
processor renders the template, the tower encodes the image, and the
engine's prefill takes the image's features in place of the placeholder
tokens - beside text-only requests in the same batch, with the prefix cache
keyed on the image's digest. Without this package the engine answers image
input with a 400 that names the vision extra.
Towers vendored from mlx-vlm (MIT,
see mlx_beam_vision/_vendor/VENDORED.md):
| Family | Models | Notes |
|---|---|---|
qwen3_vl |
Qwen3-VL, Qwen3.5, Qwen3.8 | DeepStack applied: the tower's intermediate features are added after the text model's first layers; the text model's multimodal positions (MRoPE: time, height, width per image token) are built from the processor's grids and applied through prefill, cache and decoding |
mistral3 |
Mistral Small 3.x (Pixtral tower) | the image's rows are separate spans ([IMG_BREAK] between them) |
gemma4 |
Gemma 4 E2B, E4B | image only; audio is not served yet. The 12B (gemma4_unified, encoder-free, its image tokens attending bidirectionally in the text model) is refused with that reason until the text trunk takes such a mask |
muse_glimmer |
Muse Glimmer | the projector's norm matches the text model's input norm |
granite4_vision |
Granite Vision 4.1 | AnyRes tiles, window Q-Former projectors; nothing at the embedding, every projector's features added ahead of its text layer |
Not yet: audio and video input, quantized towers (the towers load at model precision; a package that ships a quantized tower is refused with a message). An image above 32 megapixels is refused before it is decoded.
The package installs torch and torchvision: the processors transformers ships for these families (the template rendering and the image-to-patches step) are written on them. The towers themselves run on MLX; torch does no inference here.
Every family is exercised in the tests on a tiny random tower against a direct forward; the real towers and processors are validated on Apple silicon with the checkpoints themselves.
/health.vision reports the family, the tower, the processor and the
feature cache (encoder outputs by image digest, bounded in bytes).
1 maintainer
Metadata
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Release files for mlx-beam-vision 0.1.0a9
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