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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mb23/FullySpikingAutoEncoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from mb23/FullySpikingAutoEncoder: direct link, hf CLI and curl.
- Browser
- Download file 4.23 MB
-
https://huggingface.co/mb23/FullySpikingAutoEncoder/resolve/main/model.safetensors
- Command line
-
hf download hf://mb23/FullySpikingAutoEncoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/mb23/FullySpikingAutoEncoder/resolve/main/model.safetensors
4.23 MB
- Xet hash:
- 5c54e3c42ddda25f825ccc6283f78c4926ed3fdcba7cf77fdc28ebd0bbb1d490
- Size of remote file:
- 4.23 MB
- SHA256:
- df404010e0e2a77cf66e905d26b0b356397d8cfe61db32fddcca723319cb260b
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