Instructions to use mohammadmahdinouri/moa-vanilla-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadmahdinouri/moa-vanilla-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mohammadmahdinouri/moa-vanilla-checkpoints")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("mohammadmahdinouri/moa-vanilla-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "adapter_reduction": 16, | |
| "architectures": [ | |
| "ModernALBERTForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "embedding_size": 128, | |
| "group_depth": 4, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2624, | |
| "layer_norm_eps": 1e-12, | |
| "load_balancing_loss_coef": 0.01, | |
| "max_position_embeddings": 8192, | |
| "moa_reduction_factor": 4, | |
| "model_type": "ModernALBERT", | |
| "num_attention_heads": 16, | |
| "num_experts": 4, | |
| "num_hidden_layers": 16, | |
| "pad_token_id": 0, | |
| "router_jitter_noise": 0.01, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.55.2", | |
| "use_adapter": false, | |
| "use_cache": true, | |
| "use_moa": false, | |
| "vocab_size": 50368 | |
| } | |