vllm-sr/d3-nano
(2.2B, part of
Decision 3.0
by the vLLM Semantic Router team) converted to Core ML for Apple silicon: text, image and video requests on device. Same contract as upstream: one forward pass per question, the 255-way FP32 readout at the last prompt token, a probability for every option, no text generated.
Files
File
What
d3_E256
/
d3_E640
/
d3_E1536.mlpackage
text graph (fp16 compute, int8 weights) per prompt-length bucket; input = token embeddings with image/video tokens spliced in
Swift:
D3VisionManager
in
FluidUse
loads this folder.
Checked against upstream
Same weights as upstream v3.1.0. Answers that differ from the upstream PyTorch runtime (bf16, MPS), same requests:
flips
Text (typed-decisions TEST, 20 requests)
0 / 100
Images (receipt checks)
0 / 24
Video (4-second street clips, 2 fps)
1 / 28
Speed on an M5 Pro (24 GB),
CPU_AND_GPU
: ~65 ms per text question, 0.57 s per 4-second video look.
Python
from d3_coreml_text import D3CoreMLText # d3_coreml_image.D3CoreMLImage, d3_coreml_video.D3CoreMLVideo
m = D3CoreMLText("<vllm-sr/d3-nano snapshot>", "<this repo>")
m.system_one(state="...", questions={"route": {"type": "choice", "criteria": {...}}})
The Python host builds prompts with upstream's own runtime (
d3_runtime.py
in the upstream snapshot, run without weights), so prompts match upstream exactly.
License: Apache-2.0, as upstream.
Runs of FluidInference d3-nano-coreml on huggingface.co
62
Total runs
2
24-hour runs
4
3-day runs
10
7-day runs
10
30-day runs
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