diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py
index 2206aa641..7685cbd34 100644
--- a/llama_stack/providers/remote/inference/bedrock/bedrock.py
+++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py
@@ -87,9 +87,9 @@ def _to_inference_profile_id(model_id: str, region: str = None) -> str:
 
 class BedrockInferenceAdapter(
     ModelRegistryHelper,
-    Inference,
     OpenAIChatCompletionToLlamaStackMixin,
     OpenAICompletionToLlamaStackMixin,
+    Inference,
 ):
     def __init__(self, config: BedrockConfig) -> None:
         ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
@@ -155,7 +155,7 @@ class BedrockInferenceAdapter(
     async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
         params = await self._get_params_for_chat_completion(request)
         res = self.client.invoke_model(**params)
-        chunk = next(res["body"])
+        chunk = res["body"].read()
         result = json.loads(chunk.decode("utf-8"))
 
         choice = OpenAICompatCompletionChoice(
@@ -172,14 +172,16 @@ class BedrockInferenceAdapter(
         event_stream = res["body"]
 
         async def _generate_and_convert_to_openai_compat():
-            for chunk in event_stream:
-                chunk = chunk["chunk"]["bytes"]
-                result = json.loads(chunk.decode("utf-8"))
-                choice = OpenAICompatCompletionChoice(
-                    finish_reason=result["stop_reason"],
-                    text=result["generation"],
-                )
-                yield OpenAICompatCompletionResponse(choices=[choice])
+            for event in event_stream:
+                if "chunk" in event:
+                    chunk_data = event["chunk"]["bytes"]
+                    result = json.loads(chunk_data.decode("utf-8"))
+                    if "generation" in result:
+                        choice = OpenAICompatCompletionChoice(
+                            finish_reason=result.get("stop_reason"),
+                            text=result["generation"],
+                        )
+                        yield OpenAICompatCompletionResponse(choices=[choice])
 
         stream = _generate_and_convert_to_openai_compat()
         async for chunk in process_chat_completion_stream_response(stream, request):
@@ -193,8 +195,9 @@ class BedrockInferenceAdapter(
 
         if sampling_params.max_tokens:
             options["max_gen_len"] = sampling_params.max_tokens
-        if sampling_params.repetition_penalty > 0:
-            options["repetition_penalty"] = sampling_params.repetition_penalty
+        # Note: repetition_penalty is not supported by AWS Bedrock Llama models
+        # if sampling_params.repetition_penalty > 0:
+        #     options["repetition_penalty"] = sampling_params.repetition_penalty
 
         prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))