fix minor spelling issues.

Author: raksiv

docs/guides/python/serverless-llama.mdx

@@ -49,7 +49,7 @@ We'll use a quantized version of the lightweight Llama 1B model, specifically [L
   accuracy of the model.
 </Note>
 
-The [LM Studio](https://lmstudio.ai) team provide several quantized versions of Llama 3.2 1B on [Hugging Face](https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF). Consider trying different versions to find one that best fits your needs (e.g. `Q5_K_M` which is slightly larger but higher quality).
+The [LM Studio](https://lmstudio.ai) team provides several quantized versions of Llama 3.2 1B on [Hugging Face](https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF). Consider trying different versions to find one that best fits your needs (e.g. `Q5_K_M` which is slightly larger but higher quality).
 
 Let's download the chosen model and save it in a `models` directory in your project.
 
@@ -291,4 +291,4 @@ At this point, we demonstrated how you can use a lightweight model like Llama 3.
 
 As you've seen in the code example, we've setup a fairly basic prompt structure, but you can expand on this to include more complex prompts. Including system prompts that help restrict/guide the model's responses, or even more complex interactions with the model. Also, in this example we expose the model directly as an API, but this limits the response time to 30 seconds on AWS with API Gateway.
 
-In future guides we'll show how you can go beyond simple one-time responses to more complex interactions, such as maintaining context between requests. We can also include Websockets and streamed responses to provider a better user experience for larger responses.
+In future guides we'll show how you can go beyond simple one-time responses to more complex interactions, such as maintaining context between requests. We can also include Websockets and streamed responses to provide a better user experience for larger responses.