readme: ---
  inference:
    parameters:
      temperature: 0.5
  widget:
    text: "A courier received 50 packages yesterday and twice as many today.  All of these should be delivered tomorrow. How many packages should be delivered tomorrow?"
  ---
  
  This model was created using GPT-2 as a base, and fine-tuned upon a dataset of elementary school problems requiring logic and reasoning.
  Requires Pytorch
  
  How to use to infer text
  ```python
  
  from transformers import AutoTokenizer, AutoModelForCasualLM
  import torch
  
  type = "gpt2-large"
  tokenizer = AutoTokenizer.from_pretrained(type)
  model = AutoModelForCausalLM.from_pretrained(type)
  
  model_path = '../model.pt'
  
  model = torch.load(model_path)
  
  your_text = "A courier received 50 packages yesterday and twice as many today.  All of these should be delivered tomorrow. How many packages should be delivered tomorrow?"
  encoded_text = self.tokenizer.encode(your_text, return_tensors='pt')
  outputs = model.generate(encoded_text, max_length=64, do_sample=True, temperature=0.5, top_p=1)
  outputs = [tokenizer.decode(output) for output in outputs]
  ```