readme: ```
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill")
  
  model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill")
  ```
  
  ```
  Demo:
  https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
  ```
  
  ```
  prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
  input_ids = tokenizer.encode(prompt, return_tensors='pt')
  outputs = model.generate(input_ids=input_ids,
                               max_length=10 + len(prompt),
                               temperature=1.0,
                               top_k=50,
                               top_p=0.95,
                               do_sample=True,
                               num_return_sequences=5,
                               early_stopping=True)
  for i in range(5):
    print(tokenizer.decode(outputs[i]))
  ```
  Most likely outputs (Disclaimer: I highly recommend using this over just generating):
  ```
  prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
  text = tokenizer.encode(prompt)
  myinput, past_key_values = torch.tensor([text]), None
  myinput = myinput
  myinput= myinput.to(device)
  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
  logits = logits[0,-1]
  probabilities = torch.nn.functional.softmax(logits)
  best_logits, best_indices = logits.topk(250)
  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
  text.append(best_indices[0].item())
  best_probabilities = probabilities[best_indices].tolist()
  words = []   
  print(best_words)
  ```
  
  Infill / Infilling / Masking / Phrase Masking
  
  ```
  when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
  
  ***
  ```