diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index cf5e1f3aea24d2..f7cf2e89682a4d 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -133,14 +133,53 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
     r"""
     [`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
+    Note that for decoder-only models, such as Llama2, `min_length` will compute the length of `prompt + newly
+    generated tokens` whereas for other models it will behave as `min_new_tokens`, that is, taking only into account
+    the newly generated ones.
 
     Args:
         prompt_length_to_skip (`int`):
-            The input tokens length.
+            The input tokens length. Not a valid argument when used with `generate` as it will automatically assign the
+            input length.
         min_new_tokens (`int`):
             The minimum *new* tokens length below which the score of `eos_token_id` is set to `-float("Inf")`.
         eos_token_id (`Union[int, List[int]]`):
             The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+    >>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+    >>> model.config.pad_token_id = model.config.eos_token_id
+    >>> model.generation_config.pad_token_id = model.config.eos_token_id
+    >>> input_context = "Hugging Face Company is"
+    >>> input_ids = tokenizer.encode(input_context, return_tensors="pt")
+
+    >>> # Without `eos_token_id`, it will generate the default length, 20, ignoring `min_new_tokens`
+    >>> outputs = model.generate(input_ids=input_ids, min_new_tokens=30)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a company that has been working on a new product for the past year.
+
+    >>> # If `eos_token_id` is set to ` company` it will take into account how many `min_new_tokens` have been generated
+    >>> # before stopping. Note that ` Company` (5834) and ` company` (1664) are not actually the same token, and even
+    >>> # if they were ` Company` would be ignored by `min_new_tokens` as it excludes the prompt.
+    >>> outputs = model.generate(input_ids=input_ids, min_new_tokens=1, eos_token_id=1664)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a company
+
+    >>> # Increasing `min_new_tokens` will bury the first occurrence of ` company` generating a different sequence.
+    >>> outputs = model.generate(input_ids=input_ids, min_new_tokens=2, eos_token_id=1664)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a new company
+
+    >>> # If no more occurrences of the `eos_token` happen after `min_new_tokens` it returns to the 20 default tokens.
+    >>> outputs = model.generate(input_ids=input_ids, min_new_tokens=10, eos_token_id=1664)
+    >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    Hugging Face Company is a new and innovative brand of facial recognition technology that is designed to help you
+    ```
     """
 
     def __init__(self, prompt_length_to_skip: int, min_new_tokens: int, eos_token_id: Union[int, List[int]]):
@@ -194,7 +233,6 @@ class TemperatureLogitsWarper(LogitsWarper):
     >>> import torch
     >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-
     >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
     >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
     >>> model.config.pad_token_id = model.config.eos_token_id