Instructions to use tuandunghcmut/blip_multimodal_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuandunghcmut/blip_multimodal_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tuandunghcmut/blip_multimodal_encoder") model = AutoModel.from_pretrained("tuandunghcmut/blip_multimodal_encoder") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "bert-base-uncased", | |
| "add_cross_attention": true, | |
| "add_type_embeddings": false, | |
| "architectures": [ | |
| "BertCrossModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "encoder_width": 768, | |
| "fusion_layer": 6, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "token_type_embeddings": null, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |