How to generate one token after the other with Scibert?

I would like to use Scibert for iterated token generation. Here is my code:

```
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda"
tokenizer = AutoTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')
model = AutoModelForCausalLM.from_pretrained('allenai/scibert_scivocab_uncased').to(device)

input_sequence = "Hello, I'm a language model,"

inputs = torch.as_tensor(tokenizer.encode(input_sequence)).unsqueeze(0).to(device)
attention_mask = torch.as_tensor(tokenizer(input_sequence).attention_mask).unsqueeze(0).to(device)
past_key_values = None

count = 0
complete_token = []
with torch.no_grad():
while count < 10:
count += 1
print("Iteration no.: " + str(count))
if count > 1:
inputs = input_token

print(inputs.to(device))
print(attention_mask)
print(past_key_values[0][0].shape if past_key_values else None)

model_out = model(input_ids=inputs.to(device), attention_mask=attention_mask, past_key_values=past_key_values)
logits = model_out.logits[:, -1, :]
past_key_values = model_out.past_key_values

topk_values, topk_indices = torch.topk(logits, 5)

log_probs = F.softmax(topk_values, dim=-1)
inputs_in_topk = torch.multinomial(log_probs, num_samples=1, replacement=True)
input_token = torch.gather(topk_indices, 1, inputs_in_topk)
attention_mask = torch.concat((attention_mask, torch.tensor([[1]]).to(attention_mask.device)), dim=1)
complete_token.append(input_token)
```

However, we have `past_key_values = Null` all the time. I tried this approach with other models and `past_key_values` is not null there. How can I make the iteration work here, such that we have the knowledge of the previous iteration?