Instructions to use CXDuncan/madlad400-3b-mt-optimized-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CXDuncan/madlad400-3b-mt-optimized-onnx 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="CXDuncan/madlad400-3b-mt-optimized-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CXDuncan/madlad400-3b-mt-optimized-onnx") model = AutoModelForSeq2SeqLM.from_pretrained("CXDuncan/madlad400-3b-mt-optimized-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0d177cdc1ba91be095ab03838c85709bc3a6c799f568503813e3563d9d5a610
- Size of remote file:
- 7.47 GB
- SHA256:
- a17f4569c47010fd9c6a5011637604ad3f583fa70d9a1978ca46176f33d93634
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