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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e2438435d8908e0b5a22b3b433714cf4345b88a72d75091d4f9a641dd10eb22
- Size of remote file:
- 4.43 MB
- SHA256:
- ef11ac9a22c7503492f56d48dce53be20e339b63605983e9f27d2cd0e0f3922c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.