---
library_name: transformers
tags: []
---




```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("tomg-group-umd/step-00047360-recurrence_full_512_0", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("tomg-group-umd/step-00047360-recurrence_full_512_0")

device=torch.device("cuda:0")

input_ids = tokenizer.encode("The capital of Westphalia is", return_tensors="pt", add_special_tokens=True).to(device)[:, :-1]
model.eval()
model.to(device)

model(input_ids)

# or, more efficiently
amp_settings = {"device_type": "cuda", "enabled": True, "dtype": torch.bfloat16}
if not amp_settings["enabled"]:
    torch.backends.cuda.enable_math_sdp(True)



with torch.autocast(**amp_settings), torch.no_grad():
    model(input_ids=input_ids)

###### Caching:
# first step:
past_key_values = None
outputs = model(input_ids=input_ids, use_cache=True, past_key_values=past_key_values)
past_key_values = outputs.past_key_values
# next step
outputs = model(input_ids=input_ids, use_cache=True, past_key_values=past_key_values)

######## Generate!
with torch.autocast(**amp_settings), torch.no_grad():
    output_ids = model.generate(input_ids, max_new_tokens=20, use_cache=True, num_steps=32)

print(tokenizer.decode(output_ids[0]))


# with or without cache
with torch.autocast(**amp_settings), torch.no_grad():
    output_ids = model.generate(input_ids, max_new_tokens=20, use_cache=False, num_steps=32)

print(tokenizer.decode(output_ids[0]))

# Both are supposed to print:

# <|begin_text|>The capital of Westphalia is the city of Münster. The city is located in the north of the state and is
```