Instructions to use lysandre/test_dynamic_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/test_dynamic_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lysandre/test_dynamic_model", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lysandre/test_dynamic_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modeling.py from lysandre/test_dynamic_model: direct link, hf CLI and curl.
- Browser
- Download file 304 Bytes
-
https://huggingface.co/lysandre/test_dynamic_model/resolve/main/modeling.py
- Command line
-
hf download hf://lysandre/test_dynamic_model/modeling.py
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curl -L -o modeling.py https://huggingface.co/lysandre/test_dynamic_model/resolve/main/modeling.py
304 Bytes
| import torch | |
| from transformers import BertModel | |
| from .configuration import NewModelConfig | |
| class NewModel(BertModel): | |
| config_class = NewModelConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.last_layer = torch.nn.Linear(config.hidden_size, config.new_hidden_size) |