Instructions to use jbochi/madlad400-7b-mt-bt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbochi/madlad400-7b-mt-bt 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="jbochi/madlad400-7b-mt-bt")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jbochi/madlad400-7b-mt-bt") model = AutoModelForSeq2SeqLM.from_pretrained("jbochi/madlad400-7b-mt-bt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c752f996e82109c7d42b1154c2e1cd3b805e40c3578c454a4a14a7b339c48dd2
- Size of remote file:
- 5 GB
- SHA256:
- 9f3df9ced4bad2dacab0d2eadf651fc2687d9c80ffde37de476944cee304920a
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