Instructions to use GleghornLab/CAMP_nat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/CAMP_nat with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import CAMP model = CAMP.from_pretrained("GleghornLab/CAMP_nat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-19000/config.json from GleghornLab/CAMP_nat: direct link, hf CLI and curl.
- Browser
- Download file 532 Bytes
-
https://huggingface.co/GleghornLab/CAMP_nat/resolve/main/checkpoint-19000/config.json
- Command line
-
hf download hf://GleghornLab/CAMP_nat/checkpoint-19000/config.json
-
curl -L -o config.json https://huggingface.co/GleghornLab/CAMP_nat/resolve/main/checkpoint-19000/config.json
532 Bytes
| { | |
| "annotation_transformer": false, | |
| "architectures": [ | |
| "CAMP" | |
| ], | |
| "diff": false, | |
| "dropout": 0.05, | |
| "hidden_dim": 640, | |
| "input_dim": 384, | |
| "intermediate_dim": 2560, | |
| "kernel_size": 11, | |
| "latent": false, | |
| "mlm": false, | |
| "mnr": false, | |
| "model_type": "CAMP", | |
| "nhead": 8, | |
| "nlp_path": "allenai/scibert_scivocab_uncased", | |
| "num_hidden_layers": 1, | |
| "out_dim": 512, | |
| "plm_path": "facebook/esm2_t33_650m_UR50D", | |
| "pooling": "avg", | |
| "token": null, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.41.2" | |
| } | |