Transformers
PyTorch
bert
chemistry
smiles
molecular-property-prediction
masked-language-modeling
transfer-learning
model-scaling
Instructions to use sagawa/molscaletransfer-chemlm-86.24m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagawa/molscaletransfer-chemlm-86.24m with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sagawa/molscaletransfer-chemlm-86.24m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bf16": { | |
| "enabled": true | |
| }, | |
| "gradient_clipping": 0.0, | |
| "steps_per_print": 100, | |
| "train_batch_size": 4096, | |
| "train_micro_batch_size_per_gpu": 128, | |
| "wall_clock_breakdown": false, | |
| "zero_optimization": { | |
| "gather_16bit_weights_on_model_save": true, | |
| "stage": 0 | |
| } | |
| } | |