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Helsinki-NLP/opus-mt-en-et, a Marian translation model from the Helsinki-NLP OPUS-MT project.optimum-cli export onnx --model Helsinki-NLP/opus-mt-en-et --task text2text-generation-with-past <out>onnxruntime.quantization.quantize_dynamic (QUInt8 weights)../ fp32 ONNX graphs (encoder_model.onnx, decoder_model.onnx, decoder_with_past_model.onnx) + tokenizer files
./int8/ int8 dynamic-quantized ONNX graphsMarianMTModel) vs ONNX fp32 (ORTModelForSeq2SeqLM) on 2 sentences, greedy and beam=4 (max_new_tokens=64). Overall: greedy PASS, beam4 PASS.1from optimum.onnxruntime import ORTModelForSeq2SeqLM
2from transformers import AutoTokenizer
3
4repo = "TigreGotico/opus-mt-en-et-onnx"
5tok = AutoTokenizer.from_pretrained(repo)
6model = ORTModelForSeq2SeqLM.from_pretrained(repo) # fp32; pass subfolder="int8" for the quantized graphs
7inputs = tok("This is a book.", return_tensors="pt")
8out = model.generate(**inputs, num_beams=4, max_new_tokens=64)
9print(tok.decode(out[0], skip_special_tokens=True))