Instructions to use multimolecule/malinois with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/malinois with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/malinois") model = AutoModel.from_pretrained("multimolecule/malinois") - Notebooks
- Google Colab
- Kaggle
File size: 1,136 Bytes
3a5ca55 | 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | {
"architectures": [
"MalinoisForSequencePrediction"
],
"batch_norm_eps": 1e-05,
"batch_norm_momentum": 0.1,
"bos_token_id": 1,
"branched_act": "relu",
"branched_channels": 140,
"branched_dropout": 0.5757068086404574,
"conv_channels": [
300,
200,
200
],
"conv_kernel_sizes": [
19,
11,
7
],
"dtype": "float32",
"eos_token_id": 2,
"head": {
"act": null,
"bias": true,
"dropout": 0.0,
"hidden_size": 420,
"layer_norm_eps": 1e-12,
"loss_weight": null,
"num_labels": 3,
"output_name": null,
"problem_type": "regression",
"transform": null,
"transform_act": "gelu",
"type": null
},
"id2label": null,
"input_length": 600,
"label2id": null,
"linear_act": "relu",
"linear_channels": 1000,
"linear_dropout": 0.11625456877954289,
"mask_token_id": 4,
"model_type": "malinois",
"null_token_id": 5,
"num_branched_layers": 3,
"num_conv_layers": 3,
"num_labels": 3,
"num_linear_layers": 1,
"pad_token_id": 0,
"tie_word_embeddings": true,
"transformers_version": "5.7.0",
"unk_token_id": 3,
"vocab_size": 5
}
|