Update python/samples/concepts/README.md

Co-authored-by: Tao Chen <taochen@microsoft.com>

python/samples/concepts/README.md

@@ -10,7 +10,7 @@ This section contains code snippets that demonstrate the usage of Semantic Kerne
 | Filtering | Creating and using Filters |
 | Functions | Invoking [`Method`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_method.py) or [`Prompt`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_prompt.py) functions with [`Kernel`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/kernel.py) |
 | Grounding | An example of how to perform LLM grounding |
-| Local Models        | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) and [`OnnxGenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py) to talk to models hosted locally in Ollama,OnnxGenAI and LM Studio |
+| Local Models | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) and [`OnnxGenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py) to talk to models hosted locally in Ollama, OnnxGenAI and LM Studio |
 | Logging | Showing how to set up logging |
 | Memory | Using [`Memory`](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/src/SemanticKernel.Abstractions/Memory) AI concepts |
 | Model-as-a-Service | Using models deployed as [`serverless APIs on Azure AI Studio`](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio) to benchmark model performance against open-source datasets |