Instructions to use Synthyra/ESM2-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESM2-8M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/ESM2-8M", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESM2-8M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_fastesm.py with huggingface_hub
Browse files- modeling_fastesm.py +2 -0
modeling_fastesm.py
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@@ -847,6 +847,7 @@ class FastEsmForSequenceClassification(FastEsmPreTrainedModel):
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labels: Optional[torch.LongTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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) -> Union[Tuple, SequenceClassifierOutput]:
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outputs = self.esm(
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input_ids,
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@@ -907,6 +908,7 @@ class FastEsmForTokenClassification(FastEsmPreTrainedModel):
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labels: Optional[torch.LongTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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) -> Union[Tuple, TokenClassifierOutput]:
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outputs = self.esm(
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input_ids,
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labels: Optional[torch.LongTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None
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) -> Union[Tuple, SequenceClassifierOutput]:
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outputs = self.esm(
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input_ids,
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labels: Optional[torch.LongTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None
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) -> Union[Tuple, TokenClassifierOutput]:
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outputs = self.esm(
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input_ids,
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