Instructions to use haiFrHust/VNJPTranslate_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haiFrHust/VNJPTranslate_base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" 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("question-answering", model="haiFrHust/VNJPTranslate_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("haiFrHust/VNJPTranslate_base") model = AutoModelForCausalLM.from_pretrained("haiFrHust/VNJPTranslate_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Improve language tag (#1)
Verified signature- Improve language tag (abe9f207fd865dfd4ecf33d718510c316a0f8a34) Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>4df293c
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README updated
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