Instructions to use oudmaria/darija-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use oudmaria/darija-translator with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("C:\Users\maria/.cache/huggingface/hub/models--unsloth--llama-3.2-3b-instruct-unsloth-bnb-4bit/snapshots/19846d3f624f3eb96f3bdd275620c6bc7e21e1f8") model = PeftModel.from_pretrained(base_model, "oudmaria/darija-translator") - Transformers
How to use oudmaria/darija-translator with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="oudmaria/darija-translator")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("oudmaria/darija-translator", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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