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lemuralabs/Keye-VL-2.0-30B-A3B-uncensored-mlx-mxfp4
Keye-VL-2.0-30B-A3B-uncensored-mlx-mxfp4 is a image-text-to-text model from lemuralabs. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
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Downloads · 30 days
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How the weights are stored.
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From the Hugging Face model README
MXFP4 (≈4.43 bpw) MLX build of the abliterated
Kwai-Keye/Keye-VL-2.0-30B-A3B,
for Apple Silicon. Quantized from
lemuralabs/Keye-VL-2.0-30B-A3B-uncensored
with mlx-vlm. ~16 GB on disk; runs in ~17 GB; ≈107 tok/s on an M4 Max.
Yes — This MLX build runs Keye coherently on a Mac. The original model uses a CUDA-only sparse-attention indexer (
SALightningIndexer) that is unstable on MPS — so the stock model generates garbage via Transformers on Apple Silicon. This port runs the mathematically-equivalent dense attention, which is coherent and fast on MLX.
Keye is not yet in mainline mlx-vlm. This repo bundles the support module under
mlx_vlm_keye_support/keyevl2/. Install it:
pip install mlx-vlm
# copy the bundled module into your mlx-vlm install:
python - <<'PY'
import mlx_vlm, os, shutil
dst = os.path.join(os.path.dirname(mlx_vlm.__file__), "models", "keyevl2")
shutil.copytree("mlx_vlm_keye_support/keyevl2", dst, dirs_exist_ok=True)
print("installed keyevl2 ->", dst)
PY
Also register the prompt format (one line in mlx_vlm/prompt_utils.py): add
"keye_vl2": MessageFormat.LIST_WITH_IMAGE_FIRST, to the format map.
python -m mlx_vlm generate --model lemuralabs/Keye-VL-2.0-30B-A3B-uncensored-mlx-mxfp4 \
--prompt "Describe this image." --image path/to/img.jpg --trust-remote-code
mlp_AR projector are included (quantized). Text gen is
verified coherent; image understanding is functional but the packed-vision forward in
this port is a first cut — report issues.qwen3_vl_moe; the sparse sa_indexer is dropped.Abliteration removes safety alignment; you are responsible for use.
Lemura Labs)Keye-VL-2.0-30B-A3B-uncensored — abliterated bf16 — full precision (CUDA / vLLM / SGLang)Keye-VL-2.0-30B-A3B-uncensored-mlx-optiq-3.7bpw — MLX mixed ~3.87 bpw — Apple Silicon