Feature Extraction
Transformers
PyTorch
English
apex
music
audio
popularity-prediction
aesthetic-quality
multi-task-learning
mert
ai-generated-music
suno
udio
custom_code
Instructions to use amaai-lab/apex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaai-lab/apex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="amaai-lab/apex", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amaai-lab/apex", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 265c1186c5c7402d4b1cadcbf7e2ea0996549c95c65cb9900d0a3b03a4257ec7
- Size of remote file:
- 3.33 MB
- SHA256:
- 02aeef0b954270c3bb898f68a04b384c218b2bcf7721a07dba08698670295707
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.