Instructions to use yb1n/0409_deep_hw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yb1n/0409_deep_hw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yb1n/0409_deep_hw", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yb1n/0409_deep_hw", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "CustomTransformerEncoderModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_custom.CustomConfig", | |
| "AutoModel": "modeling_custom.CustomTransformerEncoderModel" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "relu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "custom-transformer-encoder", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "intermediate_size": 2048, | |
| "transformers_version": "4.0.0", | |
| "vocab_size": 10000 | |
| } | |