Instructions to use RichXuOvO/GTMGC-Molecule3D-Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RichXuOvO/GTMGC-Molecule3D-Random with Transformers:
# Load model directly from transformers import GTMGCForConformerPrediction model = GTMGCForConformerPrediction.from_pretrained("RichXuOvO/GTMGC-Molecule3D-Random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "checkpoints/CP/GTMGC_Molecule3D_Random", | |
| "architectures": [ | |
| "GTMGCForConformerPrediction" | |
| ], | |
| "atom_vocab_size": 513, | |
| "attn_drop": 0.0, | |
| "d_embed": 256, | |
| "d_ffn": 1024, | |
| "d_k": 256, | |
| "d_model": 256, | |
| "d_q": 256, | |
| "d_v": 256, | |
| "decoder_use_A_in_attn": true, | |
| "decoder_use_D_in_attn": true, | |
| "embed_style": "atom_tokenized_ids", | |
| "encoder_use_A_in_attn": true, | |
| "encoder_use_D_in_attn": false, | |
| "ffn_drop": 0.0, | |
| "n_decode_layers": 6, | |
| "n_encode_layers": 6, | |
| "n_head": 8, | |
| "norm_drop": 0.0, | |
| "pre_ln": false, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.32.1" | |
| } | |