diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py
index 14e9712595..e7e7366908 100644
--- a/examples/text-generation/run_generation.py
+++ b/examples/text-generation/run_generation.py
@@ -232,6 +232,20 @@ def setup_parser(parser):
         action="store_true",
         help="Whether to use torch compiled model or not.",
     )
+    parser.add_argument(
+        "--trust_remote_code",
+        action="store_true",
+        help="Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+             "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
+             "execute code present on the Hub on your local machine.",
+    )
+    parser.add_argument(
+        "--default_transformers",
+        action="store_true",
+        help="Use Hugging Face transformers implementation"
+    )
+
+
     parser.add_argument("--temperature", default=1.0, type=float, help="Temperature value for text generation")
     parser.add_argument("--top_p", default=1.0, type=float, help="Top_p value for generating text via sampling")
 
@@ -309,14 +323,17 @@ def generate(size=None, reduce_recompile=False):
                     if torch.is_tensor(input_tokens[t]):
                         input_tokens[t] = input_tokens[t].to(args.device)
 
-            outputs = model.generate(
-                **input_tokens,
-                generation_config=generation_config,
-                lazy_mode=use_lazy_mode,
-                hpu_graphs=args.use_hpu_graphs,
-                profiling_steps=args.profiling_steps,
-                profiling_warmup_steps=args.profiling_warmup_steps,
-            ).cpu()
+            if args.default_transformers:
+                outputs = model.generate(**input_tokens).cpu()
+            else:
+                outputs = model.generate(
+                    **input_tokens,
+                    generation_config=generation_config,
+                    lazy_mode=use_lazy_mode,
+                    hpu_graphs=args.use_hpu_graphs,
+                    profiling_steps=args.profiling_steps,
+                    profiling_warmup_steps=args.profiling_warmup_steps,
+                ).cpu()
             return tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
         from optimum.habana.utils import HabanaProfile
@@ -489,14 +506,17 @@ def generate_dataset(batch):
                 if torch.is_tensor(batch[t]):
                     batch[t] = batch[t].to(args.device)
             # Generate new sequences
-            outputs = model.generate(
-                **batch,
-                generation_config=generation_config,
-                lazy_mode=use_lazy_mode,
-                hpu_graphs=args.use_hpu_graphs,
-                profiling_steps=args.profiling_steps,
-                profiling_warmup_steps=args.profiling_warmup_steps,
-            ).cpu()
+            if args.default_transformers:
+                outputs = model.generate(**batch).cpu()
+            else:
+                outputs = model.generate(
+                    **batch,
+                    generation_config=generation_config,
+                    lazy_mode=use_lazy_mode,
+                    hpu_graphs=args.use_hpu_graphs,
+                    profiling_steps=args.profiling_steps,
+                    profiling_warmup_steps=args.profiling_warmup_steps,
+                ).cpu()
             return prompt, outputs
 
         # warmup
diff --git a/examples/text-generation/utils.py b/examples/text-generation/utils.py
index 9b66de8128..f3ee52547e 100644
--- a/examples/text-generation/utils.py
+++ b/examples/text-generation/utils.py
@@ -125,10 +125,11 @@ def setup_env(args):
         os.environ.setdefault("PT_HPU_LAZY_ACC_PAR_MODE", "0")
         os.environ.setdefault("PT_HPU_ENABLE_LAZY_COLLECTIVES", "true")
 
-    # Tweak generation so that it runs faster on Gaudi
-    from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+    if not args.default_transformers:
+        # Tweak generation so that it runs faster on Gaudi
+        from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
 
-    adapt_transformers_to_gaudi()
+        adapt_transformers_to_gaudi()
 
 
 def setup_device(args):
@@ -164,7 +165,7 @@ def setup_model(args, model_dtype, model_kwargs, logger):
     if args.peft_model is not None:
         model = peft_model(args, model_dtype, logger, **model_kwargs)
     else:
-        model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path, torch_dtype=model_dtype, **model_kwargs)
+        model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path, torch_dtype=model_dtype, trust_remote_code=args.trust_remote_code, **model_kwargs)
     if args.quant_config:
         import habana_quantization_toolkit
 
@@ -187,7 +188,7 @@ def setup_distributed_model(args, model_dtype, model_kwargs, logger):
 
     logger.info("DeepSpeed is enabled.")
     deepspeed.init_distributed(dist_backend="hccl")
-    config = AutoConfig.from_pretrained(args.model_name_or_path, torch_dtype=model_dtype, **model_kwargs)
+    config = AutoConfig.from_pretrained(args.model_name_or_path, torch_dtype=model_dtype, trust_remote_code=args.trust_remote_code, **model_kwargs)
     load_to_meta = model_on_meta(config)
 
     if load_to_meta:
@@ -220,7 +221,7 @@ def setup_distributed_model(args, model_dtype, model_kwargs, logger):
                 model = peft_model(args, model_dtype, logger, **model_kwargs)
             else:
                 model = AutoModelForCausalLM.from_pretrained(
-                    args.model_name_or_path, torch_dtype=model_dtype, **model_kwargs
+                    args.model_name_or_path, torch_dtype=model_dtype, trust_remote_code=args.trust_remote_code, **model_kwargs
                 )
     model.eval()