I tried the following two things and find a significant difference between pipeline and model.generate to complete sequences.
model_pr = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer.decode(model_pr.generate(**input_tok)[0])
'My name is Merve and my favorite+ and my CR+ and my CR+ and my CR'
(2) Using pipeline to do that same thing
generator = pipeline('text-generation', model='gpt2')
generator(input)
[{'generated_text': 'My name is Merve and my favorite brand is Baskin-Robbins.\n\n"They bring a whole lot of stuff to the table and we have to come up with a new way of making a big deal," explains Jeff.'}]
I get a lot more sensible output for pipeline for some reason. My understanding was that both should have given similar responses.