Instructions to use mb23/FullySpikingAutoEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mb23/FullySpikingAutoEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mb23/FullySpikingAutoEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mb23/FullySpikingAutoEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 461 Bytes
ba54498 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"Vth": 0.2,
"a": 0.25,
"aa": 0.5,
"architectures": [
"FSAEModel"
],
"auto_map": {
"AutoConfig": "configuration_fsae.FSAEConfig",
"AutoModel": "modeling_fsae.FSAEModel"
},
"dt": 5,
"hidden_dims": [
32,
64,
128,
256
],
"in_channels": 1,
"k": 20,
"latent_dim": 128,
"model_type": "fsae",
"n_steps": 16,
"scheduled": true,
"tau": 0.25,
"torch_dtype": "float32",
"transformers_version": "4.35.0"
}
|