Instructions to use Taykhoom/RNA-FM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RNA-FM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RNA-FM", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RNA-FM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +5 -0
config.json
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"RnaFmForMaskedLM"
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],
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"attention_heads": 20,
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"cls_idx": 0,
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"dtype": "float32",
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"emb_layer_norm_before": true,
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"RnaFmForMaskedLM"
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],
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"attention_heads": 20,
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"auto_map": {
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"AutoConfig": "configuration_rnafm.RnaFmConfig",
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"AutoModel": "modeling_rnafm.RnaFmModel",
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"AutoModelForMaskedLM": "modeling_rnafm.RnaFmForMaskedLM"
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},
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"cls_idx": 0,
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"dtype": "float32",
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"emb_layer_norm_before": true,
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