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:
- ce0cfe6b95baa9157d7471385b821e287ecdda6126a71fa9cd259c26b2845a1b
- Size of remote file:
- 12.3 kB
- SHA256:
- e8a1b78cfec66b03dd7ff100d319febeb222c008be58f9350f8bab1169b623a2
路
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