Instructions to use hamingsi/SpikingLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamingsi/SpikingLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hamingsi/SpikingLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hamingsi/SpikingLM") model = AutoModelForMaskedLM.from_pretrained("hamingsi/SpikingLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- temporal Spiking BERT with `T=4`
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- LIF nodes for projection, Q, K, V, attention output, and MLP blocks
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## Files
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- temporal Spiking BERT with `T=4`
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- LIF nodes for projection, Q, K, V, attention output, and MLP blocks
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- `self.learnmax(attention_scores)` for attention normalization
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## Files
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