Add device to example

README.md

@@ -263,32 +263,22 @@ model-index:
   - this param forces the model to use new vocabulary and create an abstractive summary, otherwise it may compile the best _extractive_ summary from the input provided.
 - create the pipeline object:
 
-```
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+```python
+import torch
 from transformers import pipeline
 
 hf_name = 'pszemraj/led-large-book-summary'
 
-_model = AutoModelForSeq2SeqLM.from_pretrained(
-                hf_name,
-                low_cpu_mem_usage=True,
-            )
-
-_tokenizer = AutoTokenizer.from_pretrained(
-                hf_name
-            )
-                                           
-
 summarizer = pipeline(
-                    "summarization", 
-                    model=_model, 
-                    tokenizer=_tokenizer
-                )
+    "summarization",
+    hf_name,
+    device=0 if torch.cuda.is_available() else -1,
+)
 ```
 
 - put words into the pipeline object:
 
-```
+```python
 wall_of_text = "your words here"
 
 result = summarizer(