diff --git a/docs/source/en/tasks/idefics.md b/docs/source/en/tasks/idefics.md
index 0e81efca12..376ec8b308 100644
--- a/docs/source/en/tasks/idefics.md
+++ b/docs/source/en/tasks/idefics.md
@@ -276,7 +276,7 @@ We can instruct the model to classify the image into one of the categories that
 >>> inputs = processor(prompt, return_tensors="pt").to("cuda")
 >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 
->>> generated_ids = model.generate(**inputs, max_new_tokens=4, bad_words_ids=bad_words_ids)
+>>> generated_ids = model.generate(**inputs, max_new_tokens=6, bad_words_ids=bad_words_ids)
 >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
 >>> print(generated_text[0])
 Instruction: Classify the following image into a single category from the following list: ['animals', 'vegetables', 'city landscape', 'cars', 'office'].
@@ -357,7 +357,7 @@ for a batch of examples by passing a list of prompts:
 ...     ],
 ... ]
 
->>> inputs = processor(prompts, return_tensors="pt")
+>>> inputs = processor(prompts, return_tensors="pt").to("cuda")
 >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
 
 >>> generated_ids = model.generate(**inputs, max_new_tokens=10, bad_words_ids=bad_words_ids)