diff --git a/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock.py b/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock.py
index 08b970bad..dda84fe14 100644
--- a/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock.py
+++ b/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock.py
@@ -48,7 +48,7 @@ class AmazonBedrockGenerator:
     from amazon_bedrock_haystack.generators.amazon_bedrock import AmazonBedrockGenerator
 
     generator = AmazonBedrockGenerator(
-        model_name="anthropic.claude-v2",
+        model="anthropic.claude-v2",
         max_length=99,
         aws_access_key_id="...",
         aws_secret_access_key="...",
@@ -71,7 +71,7 @@ class AmazonBedrockGenerator:
 
     def __init__(
         self,
-        model_name: str,
+        model: str,
         aws_access_key_id: Optional[str] = None,
         aws_secret_access_key: Optional[str] = None,
         aws_session_token: Optional[str] = None,
@@ -80,10 +80,10 @@ def __init__(
         max_length: Optional[int] = 100,
         **kwargs,
     ):
-        if not model_name:
-            msg = "model_name cannot be None or empty string"
+        if not model:
+            msg = "'model' cannot be None or empty string"
             raise ValueError(msg)
-        self.model_name = model_name
+        self.model = model
         self.max_length = max_length
 
         try:
@@ -112,14 +112,14 @@ def __init__(
         # It is hard to determine which tokenizer to use for the SageMaker model
         # so we use GPT2 tokenizer which will likely provide good token count approximation
         self.prompt_handler = DefaultPromptHandler(
-            model_name="gpt2",
+            model="gpt2",
             model_max_length=model_max_length,
             max_length=self.max_length or 100,
         )
 
-        model_adapter_cls = self.get_model_adapter(model_name=model_name)
+        model_adapter_cls = self.get_model_adapter(model=model)
         if not model_adapter_cls:
-            msg = f"AmazonBedrockGenerator doesn't support the model {model_name}."
+            msg = f"AmazonBedrockGenerator doesn't support the model {model}."
             raise AmazonBedrockConfigurationError(msg)
         self.model_adapter = model_adapter_cls(model_kwargs=model_input_kwargs, max_length=self.max_length)
 
@@ -146,8 +146,8 @@ def _ensure_token_limit(self, prompt: Union[str, List[Dict[str, str]]]) -> Union
         return str(resize_info["resized_prompt"])
 
     @classmethod
-    def supports(cls, model_name, **kwargs):
-        model_supported = cls.get_model_adapter(model_name) is not None
+    def supports(cls, model, **kwargs):
+        model_supported = cls.get_model_adapter(model) is not None
         if not model_supported or not cls.aws_configured(**kwargs):
             return False
 
@@ -170,19 +170,19 @@ def supports(cls, model_name, **kwargs):
             )
             raise AmazonBedrockConfigurationError(msg) from exception
 
-        model_available = model_name in available_model_ids
+        model_available = model in available_model_ids
         if not model_available:
             msg = (
-                f"The model {model_name} is not available in Amazon Bedrock. "
+                f"The model {model} is not available in Amazon Bedrock. "
                 f"Make sure the model you want to use is available in the configured AWS region and "
                 f"you have access."
             )
             raise AmazonBedrockConfigurationError(msg)
 
         stream: bool = kwargs.get("stream", False)
-        model_supports_streaming = model_name in model_ids_supporting_streaming
+        model_supports_streaming = model in model_ids_supporting_streaming
         if stream and not model_supports_streaming:
-            msg = f"The model {model_name} doesn't support streaming. Remove the `stream` parameter."
+            msg = f"The model {model} doesn't support streaming. Remove the `stream` parameter."
             raise AmazonBedrockConfigurationError(msg)
 
         return model_supported
@@ -194,7 +194,7 @@ def invoke(self, *args, **kwargs):
 
         if not prompt or not isinstance(prompt, (str, list)):
             msg = (
-                f"The model {self.model_name} requires a valid prompt, but currently, it has no prompt. "
+                f"The model {self.model} requires a valid prompt, but currently, it has no prompt. "
                 f"Make sure to provide a prompt in the format that the model expects."
             )
             raise ValueError(msg)
@@ -204,7 +204,7 @@ def invoke(self, *args, **kwargs):
             if stream:
                 response = self.client.invoke_model_with_response_stream(
                     body=json.dumps(body),
-                    modelId=self.model_name,
+                    modelId=self.model,
                     accept="application/json",
                     contentType="application/json",
                 )
@@ -217,7 +217,7 @@ def invoke(self, *args, **kwargs):
             else:
                 response = self.client.invoke_model(
                     body=json.dumps(body),
-                    modelId=self.model_name,
+                    modelId=self.model,
                     accept="application/json",
                     contentType="application/json",
                 )
@@ -225,7 +225,7 @@ def invoke(self, *args, **kwargs):
                 responses = self.model_adapter.get_responses(response_body=response_body)
         except ClientError as exception:
             msg = (
-                f"Could not connect to Amazon Bedrock model {self.model_name}. "
+                f"Could not connect to Amazon Bedrock model {self.model}. "
                 f"Make sure your AWS environment is configured correctly, "
                 f"the model is available in the configured AWS region, and you have access."
             )
@@ -238,9 +238,9 @@ def run(self, prompt: str, generation_kwargs: Optional[Dict[str, Any]] = None):
         return {"replies": self.invoke(prompt=prompt, **(generation_kwargs or {}))}
 
     @classmethod
-    def get_model_adapter(cls, model_name: str) -> Optional[Type[BedrockModelAdapter]]:
+    def get_model_adapter(cls, model: str) -> Optional[Type[BedrockModelAdapter]]:
         for pattern, adapter in cls.SUPPORTED_MODEL_PATTERNS.items():
-            if re.fullmatch(pattern, model_name):
+            if re.fullmatch(pattern, model):
                 return adapter
         return None
 
@@ -298,7 +298,7 @@ def to_dict(self) -> Dict[str, Any]:
         """
         return default_to_dict(
             self,
-            model_name=self.model_name,
+            model=self.model,
             max_length=self.max_length,
         )
 
diff --git a/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock_handlers.py b/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock_handlers.py
index ddfe686f8..56dcb24d3 100644
--- a/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock_handlers.py
+++ b/integrations/amazon_bedrock/src/amazon_bedrock_haystack/generators/amazon_bedrock_handlers.py
@@ -10,8 +10,8 @@ class DefaultPromptHandler:
     are within the model_max_length.
     """
 
-    def __init__(self, model_name: str, model_max_length: int, max_length: int = 100):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+    def __init__(self, model: str, model_max_length: int, max_length: int = 100):
+        self.tokenizer = AutoTokenizer.from_pretrained(model)
         self.tokenizer.model_max_length = model_max_length
         self.model_max_length = model_max_length
         self.max_length = max_length
diff --git a/integrations/amazon_bedrock/tests/test_amazon_bedrock.py b/integrations/amazon_bedrock/tests/test_amazon_bedrock.py
index a2a484340..a05c95ba3 100644
--- a/integrations/amazon_bedrock/tests/test_amazon_bedrock.py
+++ b/integrations/amazon_bedrock/tests/test_amazon_bedrock.py
@@ -45,7 +45,7 @@ def test_to_dict(mock_auto_tokenizer, mock_boto3_session):
     Test that the to_dict method returns the correct dictionary without aws credentials
     """
     generator = AmazonBedrockGenerator(
-        model_name="anthropic.claude-v2",
+        model="anthropic.claude-v2",
         max_length=99,
         aws_access_key_id="some_fake_id",
         aws_secret_access_key="some_fake_key",
@@ -57,7 +57,7 @@ def test_to_dict(mock_auto_tokenizer, mock_boto3_session):
     expected_dict = {
         "type": "amazon_bedrock_haystack.generators.amazon_bedrock.AmazonBedrockGenerator",
         "init_parameters": {
-            "model_name": "anthropic.claude-v2",
+            "model": "anthropic.claude-v2",
             "max_length": 99,
         },
     }
@@ -74,14 +74,14 @@ def test_from_dict(mock_auto_tokenizer, mock_boto3_session):
         {
             "type": "amazon_bedrock_haystack.generators.amazon_bedrock.AmazonBedrockGenerator",
             "init_parameters": {
-                "model_name": "anthropic.claude-v2",
+                "model": "anthropic.claude-v2",
                 "max_length": 99,
             },
         }
     )
 
     assert generator.max_length == 99
-    assert generator.model_name == "anthropic.claude-v2"
+    assert generator.model == "anthropic.claude-v2"
 
 
 @pytest.mark.unit
@@ -91,7 +91,7 @@ def test_default_constructor(mock_auto_tokenizer, mock_boto3_session):
     """
 
     layer = AmazonBedrockGenerator(
-        model_name="anthropic.claude-v2",
+        model="anthropic.claude-v2",
         max_length=99,
         aws_access_key_id="some_fake_id",
         aws_secret_access_key="some_fake_key",
@@ -101,7 +101,7 @@ def test_default_constructor(mock_auto_tokenizer, mock_boto3_session):
     )
 
     assert layer.max_length == 99
-    assert layer.model_name == "anthropic.claude-v2"
+    assert layer.model == "anthropic.claude-v2"
 
     assert layer.prompt_handler is not None
     assert layer.prompt_handler.model_max_length == 4096
@@ -124,7 +124,7 @@ def test_constructor_prompt_handler_initialized(mock_auto_tokenizer, mock_boto3_
     """
     Test that the constructor sets the prompt_handler correctly, with the correct model_max_length for llama-2
     """
-    layer = AmazonBedrockGenerator(model_name="anthropic.claude-v2", prompt_handler=mock_prompt_handler)
+    layer = AmazonBedrockGenerator(model="anthropic.claude-v2", prompt_handler=mock_prompt_handler)
     assert layer.prompt_handler is not None
     assert layer.prompt_handler.model_max_length == 4096
 
@@ -136,18 +136,18 @@ def test_constructor_with_model_kwargs(mock_auto_tokenizer, mock_boto3_session):
     """
     model_kwargs = {"temperature": 0.7}
 
-    layer = AmazonBedrockGenerator(model_name="anthropic.claude-v2", **model_kwargs)
+    layer = AmazonBedrockGenerator(model="anthropic.claude-v2", **model_kwargs)
     assert "temperature" in layer.model_adapter.model_kwargs
     assert layer.model_adapter.model_kwargs["temperature"] == 0.7
 
 
 @pytest.mark.unit
-def test_constructor_with_empty_model_name():
+def test_constructor_with_empty_model():
     """
-    Test that the constructor raises an error when the model_name is empty
+    Test that the constructor raises an error when the model is empty
     """
     with pytest.raises(ValueError, match="cannot be None or empty string"):
-        AmazonBedrockGenerator(model_name="")
+        AmazonBedrockGenerator(model="")
 
 
 @pytest.mark.unit
@@ -155,7 +155,7 @@ def test_invoke_with_no_kwargs(mock_auto_tokenizer, mock_boto3_session):
     """
     Test invoke raises an error if no prompt is provided
     """
-    layer = AmazonBedrockGenerator(model_name="anthropic.claude-v2")
+    layer = AmazonBedrockGenerator(model="anthropic.claude-v2")
     with pytest.raises(ValueError, match="The model anthropic.claude-v2 requires a valid prompt."):
         layer.invoke()
 
@@ -239,7 +239,7 @@ def test_supports_for_valid_aws_configuration():
         return_value=mock_session,
     ):
         supported = AmazonBedrockGenerator.supports(
-            model_name="anthropic.claude-v2",
+            model="anthropic.claude-v2",
             aws_profile_name="some_real_profile",
         )
     args, kwargs = mock_session.client("bedrock").list_foundation_models.call_args
@@ -254,7 +254,7 @@ def test_supports_raises_on_invalid_aws_profile_name():
         mock_boto3_session.side_effect = BotoCoreError()
         with pytest.raises(AmazonBedrockConfigurationError, match="Failed to initialize the session"):
             AmazonBedrockGenerator.supports(
-                model_name="anthropic.claude-v2",
+                model="anthropic.claude-v2",
                 aws_profile_name="some_fake_profile",
             )
 
@@ -270,7 +270,7 @@ def test_supports_for_invalid_bedrock_config():
         return_value=mock_session,
     ), pytest.raises(AmazonBedrockConfigurationError, match="Could not connect to Amazon Bedrock."):
         AmazonBedrockGenerator.supports(
-            model_name="anthropic.claude-v2",
+            model="anthropic.claude-v2",
             aws_profile_name="some_real_profile",
         )
 
@@ -286,21 +286,21 @@ def test_supports_for_invalid_bedrock_config_error_on_list_models():
         return_value=mock_session,
     ), pytest.raises(AmazonBedrockConfigurationError, match="Could not connect to Amazon Bedrock."):
         AmazonBedrockGenerator.supports(
-            model_name="anthropic.claude-v2",
+            model="anthropic.claude-v2",
             aws_profile_name="some_real_profile",
         )
 
 
 @pytest.mark.unit
 def test_supports_for_no_aws_params():
-    supported = AmazonBedrockGenerator.supports(model_name="anthropic.claude-v2")
+    supported = AmazonBedrockGenerator.supports(model="anthropic.claude-v2")
 
     assert supported is False
 
 
 @pytest.mark.unit
 def test_supports_for_unknown_model():
-    supported = AmazonBedrockGenerator.supports(model_name="unknown_model", aws_profile_name="some_real_profile")
+    supported = AmazonBedrockGenerator.supports(model="unknown_model", aws_profile_name="some_real_profile")
 
     assert supported is False
 
@@ -318,7 +318,7 @@ def test_supports_with_stream_true_for_model_that_supports_streaming():
         return_value=mock_session,
     ):
         supported = AmazonBedrockGenerator.supports(
-            model_name="anthropic.claude-v2",
+            model="anthropic.claude-v2",
             aws_profile_name="some_real_profile",
             stream=True,
         )
@@ -342,7 +342,7 @@ def test_supports_with_stream_true_for_model_that_does_not_support_streaming():
         match="The model ai21.j2-mid-v1 doesn't support streaming.",
     ):
         AmazonBedrockGenerator.supports(
-            model_name="ai21.j2-mid-v1",
+            model="ai21.j2-mid-v1",
             aws_profile_name="some_real_profile",
             stream=True,
         )
@@ -350,7 +350,7 @@ def test_supports_with_stream_true_for_model_that_does_not_support_streaming():
 
 @pytest.mark.unit
 @pytest.mark.parametrize(
-    "model_name, expected_model_adapter",
+    "model, expected_model_adapter",
     [
         ("anthropic.claude-v1", AnthropicClaudeAdapter),
         ("anthropic.claude-v2", AnthropicClaudeAdapter),
@@ -372,11 +372,11 @@ def test_supports_with_stream_true_for_model_that_does_not_support_streaming():
         ("unknown_model", None),
     ],
 )
-def test_get_model_adapter(model_name: str, expected_model_adapter: Optional[Type[BedrockModelAdapter]]):
+def test_get_model_adapter(model: str, expected_model_adapter: Optional[Type[BedrockModelAdapter]]):
     """
-    Test that the correct model adapter is returned for a given model_name
+    Test that the correct model adapter is returned for a given model
     """
-    model_adapter = AmazonBedrockGenerator.get_model_adapter(model_name=model_name)
+    model_adapter = AmazonBedrockGenerator.get_model_adapter(model=model)
     assert model_adapter == expected_model_adapter