Instructions to use rrivera1849/LUAR-CRUD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rrivera1849/LUAR-CRUD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rrivera1849/LUAR-CRUD", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rrivera1849/LUAR-CRUD", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix transformers 5.x compat: add all_tied_weights_keys
Browse files
model.py
CHANGED
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@@ -137,7 +137,8 @@ class LUAR(PreTrainedModel):
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"""Defines the LUAR model.
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"""
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config_class = LUARConfig
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-
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def __init__(self, config):
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super().__init__(config)
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self._luar_backbone_cfg = dict(config.backbone_config)
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"""Defines the LUAR model.
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"""
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config_class = LUARConfig
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+
all_tied_weights_keys = {}
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+
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def __init__(self, config):
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super().__init__(config)
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self._luar_backbone_cfg = dict(config.backbone_config)
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