Integrate memory tools into default examples

I found a diff like this useful for debugging memory problems:

diff --git a/examples/example_llama3.py b/examples/example_llama3.py
index e94f663..4c061d7 100644
--- a/examples/example_llama3.py
+++ b/examples/example_llama3.py
@@ -586,7 +586,7 @@ device = torch.device("cuda")

 def model_fn():
     model_args = TransformerModelArgs(
-        n_layers=8, vocab_size=vocab_size, max_seq_len=seqlen
+        n_layers=2, vocab_size=vocab_size, max_seq_len=seqlen
     )
     m = Transformer(model_args)
     return m
@@ -628,6 +628,8 @@ with AutoParallel(model, input_fn, mesh) as autop:
     parallel_mod = autop.apply_placement(sharding_placement)

 # run weight init on our sharded DTensor params
+torch.cuda.memory._record_memory_history(max_entries=100000)
+
 parallel_mod.to_empty(device="cuda")
 parallel_mod.init_weights()

@@ -643,3 +645,7 @@ x = (
 out = parallel_mod(*x)
 out.backward(torch.randn_like(out))
 print("All good!")
+
+torch.cuda.memory._dump_snapshot("mini_new3.pickle")
+
+print(torch.cuda.memory_summary())

Would be good to actually commit this to all of our examples in some way that's useful for other people.

Start with examples/example_llama3.py and compare the other example files to identify where the memory debugging tools fit consistently. Review the shown memory-history recording, snapshot dump, and memory summary calls, then verify that the examples remain runnable and provide useful memory diagnostics.