Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
best-rq-2
audioset
Instructions to use ltuncay/BEST-RQ-2.2-base-2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltuncay/BEST-RQ-2.2-base-2M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ltuncay/BEST-RQ-2.2-base-2M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ltuncay/BEST-RQ-2.2-base-2M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ltuncay/BEST-RQ-2.2-base-2M: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ltuncay/BEST-RQ-2.2-base-2M/resolve/main/model.safetensors
- Command line
-
hf download hf://ltuncay/BEST-RQ-2.2-base-2M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ltuncay/BEST-RQ-2.2-base-2M/resolve/main/model.safetensors
343 MB
- Xet hash:
- a8455fa79528b6e41069db77ec52ead39f0d6140a445cf0292cdf5033a4310fe
- Size of remote file:
- 343 MB
- SHA256:
- e0daf13449c77fe1d720df9990e6372e7bd98a64a9834785f13680f9182ccdb1
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