diff --git a/tests/models/llama/test_modeling_llama.py b/tests/models/llama/test_modeling_llama.py
index b4e1e2b1c672..32186f524978 100644
--- a/tests/models/llama/test_modeling_llama.py
+++ b/tests/models/llama/test_modeling_llama.py
@@ -117,7 +117,7 @@ def test_model_7b_logits_bf16(self):
             ("xpu", 3): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536,  0.8127, -2.9811,  1.2918, -3.3848]]),
             ("cuda", 7): torch.tensor([[-6.5061, -4.1147, -4.9669, -3.2038, 0.8069, -2.9694, 1.2864, -3.3786]]),
             ("cuda", 8): torch.tensor([[-6.5208, -4.1218, -4.9377, -3.2536,  0.8127, -2.9811,  1.2918, -3.3848]]),
-            ("rocm", (9, 4)): torch.tensor([[-6.5094, -4.1329, -4.9754, -3.5042,  0.8082, -2.9443,  1.2830, -3.3539]]),
+            ("rocm", (9, 4)): torch.tensor([[-6.5067, -4.1154, -4.9819, -3.1408,  0.8117, -2.9435,  1.2883, -3.3221]]),
         })
 
         expected_mean = expected_means.get_expectation().to(torch_device)
diff --git a/tests/models/t5/test_modeling_t5.py b/tests/models/t5/test_modeling_t5.py
index ae5031e14dc5..29f99c367510 100644
--- a/tests/models/t5/test_modeling_t5.py
+++ b/tests/models/t5/test_modeling_t5.py
@@ -47,6 +47,7 @@
     from transformers import (
         AutoTokenizer,
         ByT5Tokenizer,
+        GenerationConfig,
         T5EncoderModel,
         T5ForConditionalGeneration,
         T5ForQuestionAnswering,
@@ -932,7 +933,17 @@ def is_pipeline_test_to_skip(
 
 
 def use_task_specific_params(model, task):
-    model.config.update(model.config.task_specific_params[task])
+    task_params = model.config.task_specific_params[task]
+
+    # Get all valid GenerationConfig attributes
+    temp_config = GenerationConfig()
+    generation_config_attrs = set(temp_config.to_dict().keys())
+
+    for key, value in task_params.items():
+        if key in generation_config_attrs:
+            setattr(model.generation_config, key, value)
+        else:
+            setattr(model.config, key, value)
 
 
 @require_torch
@@ -1032,14 +1043,11 @@ def test_torch_quant(self):
     @slow
     def test_small_generation(self):
         model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
-        model.config.max_length = 8
-        model.config.num_beams = 1
-        model.config.do_sample = False
         tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
 
         input_ids = tokenizer("summarize: Hello there", return_tensors="pt").input_ids.to(torch_device)
 
-        sequences = model.generate(input_ids)
+        sequences = model.generate(input_ids, max_length=8, num_beams=1, do_sample=False)
 
         output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
         self.assertTrue(output_str == "Hello there!")