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@@ -200,6 +200,47 @@ SFTTrainer(
 ```
 This will cause no OOMs and make it somewhat faster with no upcasting to float32.
 
+### Early Stopping
+If you want to stop the finetuning / training run since the evaluation loss is not decreasing, then you can use early stopping which stops the training process. Use `EarlyStoppingCallback`.
+
+As usual, set up your trainer and your evaluation dataset and procedure:
+```python
+from trl import SFTConfig, SFTTrainer
+trainer = SFTTrainer(
+    args = SFTConfig(
+        fp16_full_eval = True,
+        per_device_eval_batch_size = 2,
+        eval_accumulation_steps = 4,
+        output_dir = "training_checkpoints", # location of saved checkpoints for early stopping
+        save_strategy = "steps",             # save model every N steps
+        save_steps = 10,                     # how many steps until we save the model
+        save_total_limit = 3,                # keep ony 3 saved checkpoints to save disk space
+        eval_strategy = "steps",             # evaluate every N steps
+        eval_steps = 10,                     # how many steps until we do evaluation
+        load_best_model_at_end = True,       # MUST USE for early stopping
+        metric_for_best_model = "eval_loss", # metric we want to early stop on
+        greater_is_better = False,           # the lower the eval loss, the better
+    ),
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = new_dataset["train"],
+    eval_dataset = new_dataset["test"],
+)
+```
+then we add a callback:
+```python
+from transformers import EarlyStoppingCallback
+early_stopping_callback = EarlyStoppingCallback(
+    early_stopping_patience = 3,     # How many steps we will wait if the eval loss doesn't decrease
+                                     # For example the loss might increase, but decrease after 3 steps
+    early_stopping_threshold = 0.0,  # Can set higher - sets how much loss should decrease by until
+                                     # we consider early stopping. For eg 0.01 means if loss was
+                                     # 0.02 then 0.01, we consider to early stop the run.
+)
+trainer.add_callback(early_stopping_callback)
+```
+Then train the model as usual: `trainer.train()`
+
 ### Chat Templates
 Assuming your dataset is a list of list of dictionaries like the below:
 ```python