Update README.md

README.md

@@ -43,6 +43,8 @@ You can use the raw model for text generation or fine-tune it to a downstream ta
 
 Here is how to use the ONNX models of gpt2 to get the features of a given text:
 
+Example using transformers.pipelines:
+
 ```python
 from transformers import AutoTokenizer, pipeline
 from optimum.onnxruntime import ORTModelForCausalLM
@@ -54,3 +56,19 @@ onnx_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
 text = "My name is Philipp and I live in Germany."
 gen = onnx_gen(text)
 ```
+
+Example of text generation:
+
+```python
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("optimum/gpt2")
+model = ORTModelForCausalLM.from_pretrained("optimum/gpt2")
+
+inputs = tokenizer("My name is Arthur and I live in", return_tensors="pt")
+
+gen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=20,max_length=20)
+tokenizer.batch_decode(gen_tokens)
+```