update

generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "decoder_start_token_id": 0,
-  "eos_token_id": 1,
-  "pad_token_id": 0,
-  "transformers_version": "4.27.0.dev0"
-}

onnx/decoder_model_merged_quantized.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fe9751468ddf78017d5bbb317d1162049b57bc8b11321dcf3756907835245e42
-size 20201510

scripts/gen.py

@@ -0,0 +1,51 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
+import torch
+from transformers.onnx import OnnxConfig, export
+from pathlib import Path
+
+# Load the T5-efficient-tiny model and tokenizer
+model_name = "google/t5-efficient-tiny"
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+config = T5Config.from_pretrained(model_name)
+
+# Prepare a sample input
+text = "Translate English to French: The house is wonderful."
+inputs = tokenizer(text, return_tensors="pt")
+
+# Define the model configuration for ONNX
+class T5OnnxConfig(OnnxConfig):
+    @property
+    def inputs(self):
+        return {
+            "input_ids": {
+                "shape": [self.batch_size, self.sequence_length],
+                "dtype": torch.int64,
+            },
+            "attention_mask": {
+                "shape": [self.batch_size, self.sequence_length],
+                "dtype": torch.int64,
+            },
+        }
+
+    @property
+    def outputs(self):
+        return {
+            "logits": {
+                "shape": [self.batch_size, self.sequence_length, self.config.vocab_size],
+                "dtype": torch.float32,
+            },
+        }
+
+onnx_config = T5OnnxConfig(config, 1, 128)
+
+# Export the model to ONNX format
+output_path = Path("t5-efficient-tiny.onnx")
+export(
+    preprocessor=tokenizer,
+    model=model,
+    config=onnx_config,
+    output=output_path
+)
+
+print("Model has been successfully exported to ONNX format.")