Instructions to use m-a-p/MERT-v1-95M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/MERT-v1-95M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="m-a-p/MERT-v1-95M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("m-a-p/MERT-v1-95M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from m-a-p/MERT-v1-95M: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/m-a-p/MERT-v1-95M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://m-a-p/MERT-v1-95M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/m-a-p/MERT-v1-95M/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 4d7525e000f949e6eeaa8017e5fd80a19edda41f88e8126c767d90515fed025a
- Size of remote file:
- 378 MB
- SHA256:
- a2b8b747f72c06e0595aeae41ae5473f4364938c6b39b2c58be38c48e6bd3fcd
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