Allow nested transformer block names for quantization, lora targeting, quantizing

toolkit/util/quantize.py

@@ -297,7 +297,13 @@ def quantize_model(
         all_blocks: List[torch.nn.Module] = []
         transformer_block_names = base_model.get_transformer_block_names()
         for name in transformer_block_names:
-            block_list = getattr(model_to_quantize, name, None)
+            # name may be a dotted path for models that nest their blocks
+            # (e.g. hidream_o1's "model.language_model.layers").
+            block_list = model_to_quantize
+            for part in name.split('.'):
+                block_list = getattr(block_list, part, None)
+                if block_list is None:
+                    break
             if block_list is not None:
                 all_blocks += list(block_list)
         base_model.print_and_status_update(