Instructions to use Synthyra/ESMplusplus_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMplusplus_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/ESMplusplus_small", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESMplusplus_small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload ESMplusplusForMaskedLM
Browse files- config.json +0 -2
config.json
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"ESMplusplusForMaskedLM"
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],
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"attn_backend": "flex",
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"attn_compile": true,
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"auto_map": {
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"AutoConfig": "modeling_esm_plusplus.ESMplusplusConfig",
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"AutoModel": "modeling_esm_plusplus.ESMplusplusModel",
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"AutoModelForTokenClassification": "modeling_esm_plusplus.ESMplusplusForTokenClassification"
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},
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"dropout": 0.0,
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"flex_block_size": 128,
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"hidden_size": 960,
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"initializer_range": 0.02,
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"model_type": "ESMplusplus",
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"ESMplusplusForMaskedLM"
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],
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"attn_backend": "flex",
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"auto_map": {
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"AutoConfig": "modeling_esm_plusplus.ESMplusplusConfig",
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"AutoModel": "modeling_esm_plusplus.ESMplusplusModel",
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"AutoModelForTokenClassification": "modeling_esm_plusplus.ESMplusplusForTokenClassification"
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},
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"dropout": 0.0,
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"hidden_size": 960,
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"initializer_range": 0.02,
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"model_type": "ESMplusplus",
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