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hamingsi
/
SpikingLM

Fill-Mask
Transformers
Safetensors
PyTorch
bert
spiking-neural-network
masked-language-modeling
Model card Files Files and versions
xet
Community

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")
  • Notebooks
  • Google Colab
  • Kaggle
SpikingLM
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
hamingsi's picture
hamingsi
Upload SpikingLM checkpoint and code
a9b7c0d verified 25 days ago
  • scripts
    Upload SpikingLM checkpoint and code 25 days ago
  • spiking_bert
    Upload SpikingLM checkpoint and code 25 days ago
  • .gitattributes
    1.52 kB
    initial commit 25 days ago
  • README.md
    1.62 kB
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  • all_results.json
    33 Bytes
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  • config.json
    628 Bytes
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  • model.safetensors
    438 MB
    xet
    Upload SpikingLM checkpoint and code 25 days ago
  • requirements.txt
    152 Bytes
    Upload SpikingLM checkpoint and code 25 days ago
  • special_tokens_map.json
    125 Bytes
    Upload SpikingLM checkpoint and code 25 days ago
  • tokenizer.json
    711 kB
    Upload SpikingLM checkpoint and code 25 days ago
  • tokenizer_config.json
    1.22 kB
    Upload SpikingLM checkpoint and code 25 days ago
  • vocab.txt
    232 kB
    Upload SpikingLM checkpoint and code 25 days ago