Instructions to use FluidInference/d3-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FluidInference/d3-coreml with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download FluidInference/d3-coreml --local-dir d3-coreml
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
|
Download README.md from FluidInference/d3-coreml: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/FluidInference/d3-coreml/resolve/main/README.md
- Command line
-
hf download hf://FluidInference/d3-coreml/README.md
-
curl -L -o README.md https://huggingface.co/FluidInference/d3-coreml/resolve/main/README.md
1.39 kB
metadata
license: apache-2.0
base_model:
- vllm-sr/d3-lite
- vllm-sr/d3-nano
- vllm-sr/d3-mini
- vllm-sr/d3-edge
library_name: coreml
pipeline_tag: zero-shot-classification
tags:
- coreml
- mlx
- apple-silicon
- decision-model
- vision
- video
Decision 3.0 — Core ML (and MLX)
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.
| Folder | Upstream | Size |
|---|---|---|
lite/ |
vllm-sr/d3-lite | 0.8B |
nano/ |
vllm-sr/d3-nano | 2B |
mini/ |
vllm-sr/d3-mini | 4B |
edge/ |
vllm-sr/d3-edge | 0.6B · also ships mlx/ weights and an MLX host (video looks ~4× faster than Core ML) |
Each folder is a full release (text graphs, vision tower, host tables, Python host) with its own README covering files, parity against upstream and speed. Swift: D3VisionManager in FluidUse loads any of them.
License: Apache-2.0, as upstream.