Instructions to use bezzam/xcodec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bezzam/xcodec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bezzam/xcodec2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bezzam/xcodec2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -0
config.json
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],
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"dtype": "float32",
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"encoder_hidden_size": 1024,
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"initializer_range": 0.02,
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"max_position_embeddings": 4096,
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"model_type": "xcodec2",
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],
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"dtype": "float32",
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"encoder_hidden_size": 1024,
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"head_dim": 64,
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"initializer_range": 0.02,
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"max_position_embeddings": 4096,
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"model_type": "xcodec2",
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