Instructions to use MusYW/MNLP_M2_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MusYW/MNLP_M2_rag_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MusYW/MNLP_M2_rag_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MusYW/MNLP_M2_rag_model") model = AutoModel.from_pretrained("MusYW/MNLP_M2_rag_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 835d3d87c630b542ac3351365c59f44d8a7365805405b6a657f80c087fc4bef0
- Size of remote file:
- 2.38 GB
- SHA256:
- 25e7389b57b466b5f7acff4573551641f42f48b3cf71125746c0c0cff718f971
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