Instructions to use pgilliar/MNLP_M2_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pgilliar/MNLP_M2_rag_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pgilliar/MNLP_M2_rag_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3aea673b539a283321aa68e5795cb9f1cfd227cfd4c4f2919d43b40727fed24
- Size of remote file:
- 1.81 kB
- SHA256:
- b4f94cd80a5e718582c801bcf7fcb28017ae870180d2c7c2791c868ed6a10462
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