diff --git a/docs/training/callbacks.md b/docs/training/callbacks.md
index 575ff324d0..a445a6814d 100644
--- a/docs/training/callbacks.md
+++ b/docs/training/callbacks.md
@@ -9,8 +9,12 @@ Megatron Bridge provides a lightweight callback system for injecting custom logi
 Subclass {py:class}`bridge.training.callbacks.Callback` and override event methods:
 
 ```python
+import time
+
 from megatron.bridge.training.callbacks import Callback
+from megatron.bridge.training.gpt_step import forward_step
 from megatron.bridge.training.pretrain import pretrain
+from megatron.bridge.recipes.qwen import qwen25_500m_pretrain_config
 
 class MyCallback(Callback):
     def on_train_start(self, context):
@@ -25,8 +29,11 @@ class MyCallback(Callback):
         elapsed = time.time() - context.user_state['start_time']
         print(f"Training completed in {elapsed:.2f}s")
 
+# Create a config that fits on a single GPU
+config = qwen25_500m_pretrain_config()
+
 # Pass callbacks to pretrain
-pretrain(config, forward_step_func, callbacks=[MyCallback()])
+pretrain(config, forward_step, callbacks=[MyCallback()])
 ```
 
 ### Functional Callbacks
@@ -35,7 +42,9 @@ Register functions directly with {py:class}`bridge.training.callbacks.CallbackMa
 
 ```python
 from megatron.bridge.training.callbacks import CallbackManager
-from megatron.bridge.training import pretrain
+from megatron.bridge.training.gpt_step import forward_step
+from megatron.bridge.training.pretrain import pretrain
+from megatron.bridge.recipes.qwen import qwen25_500m_pretrain_config
 
 def log_step(context):
     step = context.state.train_state.step
@@ -45,7 +54,10 @@ def log_step(context):
 callback_manager = CallbackManager()
 callback_manager.register("on_train_step_end", log_step)
 
-pretrain(config, forward_step_func, callbacks=callback_manager)
+# Create a config that fits on a single GPU
+config = qwen25_500m_pretrain_config()
+
+pretrain(config, forward_step, callbacks=callback_manager)
 ```
 
 ### Mixing Both Patterns
@@ -53,12 +65,20 @@ pretrain(config, forward_step_func, callbacks=callback_manager)
 Both registration patterns can be combined:
 
 ```python
+from megatron.bridge.training.callbacks import CallbackManager
+from megatron.bridge.training.gpt_step import forward_step
+from megatron.bridge.training.pretrain import pretrain
+from megatron.bridge.recipes.qwen import qwen25_500m_pretrain_config
+
 manager = CallbackManager()
 manager.add(MyCallback())
 manager.add([TimingCallback(), MetricsCallback()])
 manager.register("on_eval_end", lambda ctx: print("Evaluation complete!"))
 
-pretrain(config, forward_step_func, callbacks=manager)
+# Create a config that fits on a single GPU
+config = qwen25_500m_pretrain_config()
+
+pretrain(config, forward_step, callbacks=manager)
 ```
 
 ## Available Events