Audio Classification
MLX
Core ML
English
decision-model
turn-taking
endpoint-detection
barge-in
backchannel
full-duplex
streaming
on-device
core-ai
gru
audio
Instructions to use donpark/tyger-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use donpark/tyger-models with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download donpark/tyger-models --local-dir tyger-models
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download coreml__TygerEncoder.mlpackage__Data__com.apple.CoreML__weights__weight.bin from donpark/tyger-models: direct link, hf CLI and curl.
- Browser
- Download file 740 MB
-
https://huggingface.co/donpark/tyger-models/resolve/main/coreml__TygerEncoder.mlpackage__Data__com.apple.CoreML__weights__weight.bin
- Command line
-
hf download hf://donpark/tyger-models/coreml__TygerEncoder.mlpackage__Data__com.apple.CoreML__weights__weight.bin
-
curl -L -o coreml__TygerEncoder.mlpackage__Data__com.apple.CoreML__weights__weight.bin https://huggingface.co/donpark/tyger-models/resolve/main/coreml__TygerEncoder.mlpackage__Data__com.apple.CoreML__weights__weight.bin
740 MB
- Xet hash:
- eed19ae59d5d514520c74a2849ab133d159a713ae4bf163f485faf6aea870a55
- Size of remote file:
- 740 MB
- SHA256:
- 84a1a042067d55feca041d30839f938ef0b112de3fd9e5add09fc070cbaf6b3a
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