Added inference examples using the .pt and .onnx models

examples/example-onnx-infer.py

@@ -0,0 +1,30 @@
+
+#Tested with the following Python package versions:
+#optimum                            1.2.3.dev0
+#transformers                       4.21.0.dev0
+#tokenizers                         0.11.6
+
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForCausalLM
+from optimum.pipelines import pipeline
+
+
+def main():
+    model_name="Norod78/distilgpt2-base-pretrained-he"
+
+    prompt_text = "שלום, קוראים לי"
+    generated_max_length = 192
+
+    print("Loading model...")
+    model =  ORTModelForCausalLM.from_pretrained(model_name)
+    print('Loading Tokenizer...')
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
+
+    print("Generating text...")
+    result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, temperature = 1, repetition_penalty=5.0, max_length = generated_max_length)
+
+    print("result = " + str(result))
+
+if __name__ == '__main__':
+    main()

examples/example-pt-infer.py

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+
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+def main():
+    model_name="Norod78/distilgpt2-base-pretrained-he"
+
+    prompt_text = "שלום, קוראים לי"
+    generated_max_length = 192
+
+    print("Loading model...")
+    model =  AutoModelForCausalLM.from_pretrained(model_name)
+    print('Loading Tokenizer...')
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
+
+    print("Generating text...")
+    result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, temperature = 1, repetition_penalty=5.0, max_length = generated_max_length)
+
+    print("result = " + str(result))
+
+if __name__ == '__main__':
+    main()