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sectionme
/
granite-clm-g2

Transformers
ONNX
agpl-3.0
embeddings
Model card Files Files and versions
xet
Community

Instructions to use sectionme/granite-clm-g2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sectionme/granite-clm-g2 with Transformers:

    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("sectionme/granite-clm-g2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
granite-clm-g2
418 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
sectionme's picture
sectionme
Fine-tuned granite-97m encoder (top 4 layers) + g2 heads
ad1cc41 verified about 1 month ago
  • .gitattributes
    1.57 kB
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • README.md
    10.3 kB
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • granite_encoder.json
    445 Bytes
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • heads.safetensors
    2.64 MB
    xet
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • manifest.json
    5.88 kB
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • model.onnx
    390 MB
    xet
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • tokenizer.json
    25.3 MB
    xet
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago
  • tokenizer_config.json
    608 Bytes
    Fine-tuned granite-97m encoder (top 4 layers) + g2 heads about 1 month ago