Instructions to use ChatterjeeLab/FusOn-pLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/FusOn-pLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/FusOn-pLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/FusOn-pLM") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/FusOn-pLM") - Notebooks
- Google Colab
- Kaggle
Commit History
Rename model/tokenizer_config.json to tokenizer_config.json 3557447 verified
Rename model/special_tokens_map.json to special_tokens_map.json fc0e096 verified
Rename model/pytorch_model.bin to pytorch_model.bin aad6179 verified
Rename model/config.json to config.json ba795e4 verified
uploaded files required for AutoModel b584770 verified
“uploading snp_2000_finetune_11layers_esm_b8_lr5e-5_mask0.15-05-17-2024-16:01:29/checkpoint_epoch_14 c700342
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