Instructions to use Taykhoom/RiNALMo-giga with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RiNALMo-giga with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RiNALMo-giga", trust_remote_code=True, device_map="auto")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RiNALMo-giga", trust_remote_code=True, dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RiNALMoForMaskedLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_rinalmo.RiNALMoConfig", | |
| "AutoModel": "modeling_rinalmo.RiNALMoModel", | |
| "AutoModelForMaskedLM": "modeling_rinalmo.RiNALMoForMaskedLM" | |
| }, | |
| "attention_dropout": 0.1, | |
| "cls_idx": 0, | |
| "dtype": "float32", | |
| "embed_dim": 1280, | |
| "eos_idx": 2, | |
| "mask_idx": 4, | |
| "mask_ratio": 0.15, | |
| "mask_tkn_prob": 0.8, | |
| "model_max_length": 8192, | |
| "model_type": "rinalmo", | |
| "num_heads": 20, | |
| "num_layers": 33, | |
| "padding_idx": 1, | |
| "residual_dropout": 0.1, | |
| "rope_base": 10000, | |
| "token_dropout_active": true, | |
| "transformers_version": "4.57.6", | |
| "transition_dropout": 0.0, | |
| "transition_factor": 4, | |
| "unk_idx": 3, | |
| "use_rot_emb": true, | |
| "vocab_size": 22 | |
| } | |