diff --git a/src/axolotl/train.py b/src/axolotl/train.py
index 967179903..3a206d3da 100644
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -80,7 +80,7 @@ def setup_model_and_tokenizer(
 
     model_loader = ModelLoader(cfg, tokenizer, processor=processor)
     model, peft_config = model_loader.load()
-    if model.generation_config is not None:
+    if hasattr(model, "generation_config") and model.generation_config is not None:
         model.generation_config.do_sample = True
 
     # Apply freezing if specified
@@ -90,7 +90,10 @@ def setup_model_and_tokenizer(
             any(embed in param for embed in ["lm_head", "embed_tokens"])
             for param in cfg.unfrozen_parameters
         ):
-            model.enable_input_require_grads()
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+                LOG.warning("Model does not have enable_input_require_grads method, skipping")
 
     return model, tokenizer, peft_config, processor
 
@@ -246,9 +249,12 @@ def save_trained_model(
     LOG.info(f"Training completed! Saving trained model to {cfg.output_dir}.")
 
     # Post training module hooks
-    for name, module in model.named_modules():
-        if hasattr(module, "_post_training"):
-            module._post_training(model, name)  # pylint: disable=protected-access
+    if hasattr(model, "named_modules"):
+        for name, module in model.named_modules():
+            if hasattr(module, "_post_training"):
+                module._post_training(model, name)  # pylint: disable=protected-access
+    else:
+        LOG.warning("Model does not have named_modules attribute, skipping post training hooks")
 
     # handle QAT
     if cfg.qat:
@@ -308,11 +314,17 @@ def save_trained_model(
             model = BetterTransformer.reverse(model)
 
         if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
-            trainer.model.save_pretrained(
-                cfg.output_dir, safe_serialization=safe_serialization
-            )
+            if hasattr(trainer.model, "save_pretrained"):
+                trainer.model.save_pretrained(
+                    cfg.output_dir, safe_serialization=safe_serialization
+                )
+            else:
+                LOG.warning("Trainer model does not have save_pretrained method, skipping save")
 
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        if hasattr(model, "save_pretrained"):
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        else:
+            LOG.warning("Model does not have save_pretrained method, skipping save")
 
     if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
         # TODO: add integration support so this can be implemented completely within the plugin
@@ -398,7 +410,10 @@ def save_initial_configs(
     tokenizer.save_pretrained(str(output_dir))
     if hasattr(model, "config"):
         LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
-        model.config.save_pretrained(str(output_dir))
+        if hasattr(model.config, "save_pretrained"):
+            model.config.save_pretrained(str(output_dir))
+        else:
+            LOG.warning("Model config does not have save_pretrained method, skipping config save")
 
     if processor:
         LOG.info(f"Pre-saving processor to {cfg.output_dir}...")
@@ -461,9 +476,12 @@ def handle_untrained_tokens_fix(
     fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
 
     if cfg.local_rank == 0:
-        model.save_pretrained(
-            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
-        )
+        if hasattr(model, "save_pretrained"):
+            model.save_pretrained(
+                str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+            )
+        else:
+            LOG.warning("Model does not have save_pretrained method, skipping save")
 
 
 def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[