Question Answering
Transformers
Safetensors
rag
retrieval-augmented-generation
multilingual
faiss
llama
mistral
Instructions to use hamzi275/multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzi275/multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hamzi275/multilingual")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hamzi275/multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
路
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