Instructions to use gae-m/codesage-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gae-m/codesage-base-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import CodeSage model = CodeSage.from_pretrained("gae-m/codesage-base-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_codesage.py with huggingface_hub
Browse files- modeling_codesage.py +2 -1
modeling_codesage.py
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@@ -8,7 +8,8 @@ import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import Conv1D, PreTrainedModel
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from transformers.utils import logging
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from .config_codesage import CodeSageConfig
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from transformers.modeling_outputs import (
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from torch import nn
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from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
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from transformers.activations import ACT2FN
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# from transformers.modeling_utils import Conv1D, PreTrainedModel
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from transformers import Conv1D, PreTrainedModel
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from transformers.utils import logging
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from .config_codesage import CodeSageConfig
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from transformers.modeling_outputs import (
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