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:
- 6996fcd9a2e30cee3e6f88ea2cb68780156df3c6b5384e03604f42874101eddb
- Size of remote file:
- 9.82 kB
- SHA256:
- e3c4b3ad45d2a109a42cf40d262cdded35d4d1311cabbfc4a95746438d931352
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