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:
- 5ff64136cb12559b41ebe33e8610f71ff3afa74f0452b71a94cba9fed76923b0
- Size of remote file:
- 4.97 GB
- SHA256:
- 52afab862449bf496da0d0ac7667d198feee45c930e547cfa7c05fcbfeef92c8
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