Instructions to use Lolalb/local_hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lolalb/local_hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Lolalb/local_hf")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("Lolalb/local_hf", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "NeoBERTLMHead" | |
| ], | |
| "classifier_init_range": 0.02, | |
| "decoder_init_range": 0.02, | |
| "dim_head": 64, | |
| "embedding_init_range": 0.02, | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "kwargs": { | |
| "classifier_init_range": 0.02, | |
| "pretrained_model_name_or_path": "google-bert/bert-base-uncased", | |
| "trust_remote_code": true | |
| }, | |
| "max_length": 4096, | |
| "model_type": "neobert", | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 28, | |
| "pad_token_id": 0, | |
| "pretrained_model_name_or_path": "google-bert/bert-base-uncased", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.3", | |
| "trust_remote_code": true, | |
| "vocab_size": 30522 | |
| } | |