Instructions to use Synthyra/FastESM2_650 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/FastESM2_650 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/FastESM2_650", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/FastESM2_650", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "modeling_fastesm.FastEsmConfig",
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"AutoModel": "modeling_fastesm.FastEsmModel"
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"AutoModelForMaskedLM": "modeling_fastesm.FastEsmForMaskedLM",
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"AutoModelForSequenceClassification": "modeling_fastesm.FastEsmForSequenceClassification",
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"AutoModelForTokenClassification": "modeling_fastesm.FastEsmForTokenClassification"
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"attention_probs_dropout_prob": 0.0,
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"auto_map": {
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"AutoConfig": "modeling_fastesm.FastEsmConfig",
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"AutoModel": "modeling_fastesm.FastEsmModel",
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"AutoModelForMaskedLM": "modeling_fastesm.FastEsmForMaskedLM",
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"AutoModelForSequenceClassification": "modeling_fastesm.FastEsmForSequenceClassification",
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"AutoModelForTokenClassification": "modeling_fastesm.FastEsmForTokenClassification"
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