mlx-community/sapiens2-pose-0.4b-bf16
bf16 MLX conversion of facebook/sapiens2-pose-0.4b (Meta's
Sapiens2, ICLR 2026): 308-keypoint top-down pose heatmaps. Converted with mlx-vlm
0.7.0; the original float32 checkpoint is 2x this size.
What is in model.safetensors (0.85 GB):
- every parameter in bfloat16 (the reference runs inference in bf16 mixed precision);
- the q/k/v projections merged into one
wqkvtensor per block, as the mlx-vlm Sapiens2 model expects.
Refer to the original model card for the model description, intended use and license.
Use with mlx-vlm
pip install -U mlx-vlm
from mlx_vlm import load
from mlx_vlm.models.sapiens2.generate import Sapiens2Predictor, read_image
model, _ = load("mlx-community/sapiens2-pose-0.4b-bf16")
predictor = Sapiens2Predictor(model)
# boxes: (N, 4) xyxy person boxes from a detector; defaults to the full image.
output = predictor.infer(read_image("image.jpg"), boxes=boxes, flip_test=False)
keypoints, scores = output["keypoints"], output["scores"] # (N, 308, 2), (N, 308)
Outputs are numpy arrays at the input resolution (dense tasks) or in source-image pixel coordinates (pose). See the mlx-vlm Sapiens2 README for preprocessing details and the per-task output keys.
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Model size
0.4B params
Tensor type
BF16
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facebook/sapiens2-pose-0.4b