Instructions to use vector-institute/atomformer-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vector-institute/atomformer-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vector-institute/atomformer-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers.configuration_utils import PretrainedConfig | |
| from typing import Any | |
| class AtomformerConfig(PretrainedConfig): # type: ignore | |
| r""" | |
| Configuration of a :class:`~transform:class:`~transformers.AtomformerModel`. | |
| It is used to instantiate an Atomformer model according to the specified arguments. | |
| """ | |
| model_type = "atomformer" | |
| def __init__( | |
| self, | |
| vocab_size: int = 123, | |
| dim: int = 768, | |
| num_heads: int = 32, | |
| depth: int = 12, | |
| mlp_ratio: int = 1, | |
| k: int = 128, | |
| dropout: float = 0.0, | |
| mask_token_id: int = 0, | |
| pad_token_id: int = 119, | |
| bos_token_id: int = 120, | |
| eos_token_id: int = 121, | |
| cls_token_id: int = 122, | |
| **kwargs: Any, | |
| ) -> None: | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.dim = dim | |
| self.num_heads = num_heads | |
| self.depth = depth | |
| self.mlp_ratio = mlp_ratio | |
| self.k = k | |
| self.dropout = dropout | |
| self.mask_token_id = mask_token_id | |
| self.pad_token_id = pad_token_id | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.cls_token_id = cls_token_id |