| from pathlib import Path | |
| import transformers as t | |
| from transformers import AutoTokenizer, pipeline | |
| from optimum.onnxruntime import ORTModelForSeq2SeqLM | |
| # print out the version of the transformers library | |
| print("transformers version:", t.__version__) | |
| models = [ | |
| #"google/flan-t5-small", | |
| #"google/flan-t5-base", | |
| #"google/flan-t5-large", | |
| "google/flan-t5-xl", | |
| "google/flan-t5-xxl", | |
| ] | |
| for model_id in models: | |
| model_name = model_id.split("/")[1] | |
| onnx_path = Path("onnx/" + model_name) | |
| # load vanilla transformers and convert to onnx | |
| model = ORTModelForSeq2SeqLM.from_pretrained(model_id, from_transformers=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # save onnx checkpoint and tokenizer | |
| model.save_pretrained(onnx_path) | |
| tokenizer.save_pretrained(onnx_path) | |