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
File size: 610 Bytes
6af52a6 5c51c76 6af52a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"architectures": [
"RnaFmForMaskedLM"
],
"attention_heads": 20,
"auto_map": {
"AutoConfig": "configuration_rnafm.RnaFmConfig",
"AutoModel": "modeling_rnafm.RnaFmModel",
"AutoModelForMaskedLM": "modeling_rnafm.RnaFmForMaskedLM"
},
"cls_idx": 0,
"dtype": "float32",
"emb_layer_norm_before": true,
"embed_dim": 640,
"eos_idx": 2,
"ffn_embed_dim": 5120,
"mask_idx": 24,
"model_max_length": 1024,
"model_type": "rnafm",
"model_variant": "rna",
"num_layers": 12,
"padding_idx": 1,
"token_dropout": false,
"transformers_version": "4.57.6",
"vocab_size": 25
}
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