Instructions to use CXDuncan/madlad400-3b-mt-optimized-quantized-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-quantized-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-quantized-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CXDuncan/madlad400-3b-mt-optimized-quantized-onnx") model = AutoModelForSeq2SeqLM.from_pretrained("CXDuncan/madlad400-3b-mt-optimized-quantized-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model_quantized.onnx from CXDuncan/madlad400-3b-mt-optimized-quantized-onnx: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/CXDuncan/madlad400-3b-mt-optimized-quantized-onnx/resolve/main/encoder_model_quantized.onnx
- Command line
-
hf download hf://CXDuncan/madlad400-3b-mt-optimized-quantized-onnx/encoder_model_quantized.onnx
-
curl -L -o encoder_model_quantized.onnx https://huggingface.co/CXDuncan/madlad400-3b-mt-optimized-quantized-onnx/resolve/main/encoder_model_quantized.onnx
1.34 GB
- Xet hash:
- a4bc689083f4fe221cf239eed4e5294e1f38b3e40ab17a47b2d2e9accb4e5d3a
- Size of remote file:
- 1.34 GB
- SHA256:
- ec4762557be4a5db1d8d88fbe4437a6809bf4ad7a2322f45b7205fef58533e39
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