diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index 7351abb1199..7bc43e5664e 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -1040,10 +1040,9 @@ class SequenceBiasLogitsProcessor(LogitsProcessor):
 
     <Tip>
 
-    In order to get the token ids of the sequences that you want to bias, make sure to set `add_prefix_space=True` when
-    initializing the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The
-    `add_prefix_space` argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours
-    come from `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
+    At a token-level, biasing a word is different from biasing a word with a space before it. If you want to bias
+    "foo" mid-sentence, you'll likely want to add a prefix space and bias " foo" instead. Check the tokenizer section
+    of our NLP course to find out why: https://huggingface.co/learn/nlp-course/chapter2/4?fw=pt
 
     </Tip>
 
@@ -1060,37 +1059,40 @@ class SequenceBiasLogitsProcessor(LogitsProcessor):
     ```python
     >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-    >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
-    >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+    >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
     >>> inputs = tokenizer(["The full name of Donald is Donald"], return_tensors="pt")
 
-    >>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4)
+    >>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4, do_sample=False)
     >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
-    The full name of Donald is Donald J. Trump Jr
-
-    >>> # Now let's control generation through a bias. Please note that the tokenizer is initialized differently!
-    >>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("openai-community/gpt2", add_prefix_space=True)
-
+    The full name of Donald is Donald John Trump Sr.
 
     >>> def get_tokens(word):
-    ...     return tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0]
+    ...     return tokenizer([word], add_special_tokens=False).input_ids[0]
 
-
-    >>> # If we add a negative bias without beam search, it may become "stuck" in a prefix without good continuations
-    >>> sequence_bias = [get_tokens("Trump"), -10.0]
-    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, sequence_bias=sequence_bias)
+    >>> # IMPORTANT: Remember our tip about adding spaces before words to bias them correctly.
+    >>> sequence_bias = [[get_tokens("Trump"), -10.0],]  # will fail to apply bias
+    >>> biased_ids = model.generate(
+    ...     inputs["input_ids"], max_new_tokens=4, do_sample=False, sequence_bias=sequence_bias
+    ... )
     >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
-    The full name of Donald is Donald J. Donald,
+    The full name of Donald is Donald John Trump Sr.
 
-    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
+    >>> sequence_bias = [[get_tokens(" Trump"), -10.0],]  # will work
+    >>> biased_ids = model.generate(
+    ...     inputs["input_ids"], max_new_tokens=4, do_sample=False, sequence_bias=sequence_bias
+    ... )
     >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
-    The full name of Donald is Donald Rumsfeld,
+    The full name of Donald is Donald John Harper. He
 
-    >>> # We can also add a positive bias to nudge the model towards specific tokens or continuations
-    >>> sequence_bias = [get_tokens("Donald Duck"), 10.0]
-    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
+    >>> # We can also add a positive bias to nudge the model towards specific tokens or continuations. This technique
+    >>> # is also more effective when paired up with beam search.
+    >>> sequence_bias = [[get_tokens(" Donald Duck"), 10.0],]
+    >>> biased_ids = model.generate(
+    ...     inputs["input_ids"], max_new_tokens=4, num_beams=4, do_sample=False, sequence_bias=sequence_bias
+    ... )
     >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
-    The full name of Donald is Donald Duck.
+    The full name of Donald is Donald Duck. He is
     ```
     """