Invoking ChatBedrockConverse on us.meta.llama3-2-11b-instruct-v1:0 times out for some images

The issue you're experiencing with timeout errors when invoking ChatBedrockConverse on the Meta Llama 3.2 11B Instruct model for some images is not uncommon when working with large vision language models through Amazon Bedrock. Here are a few points to consider:

1. Model complexity: The Llama 3.2 11B Instruct model is a large, multimodal model capable of sophisticated reasoning tasks, including visual reasoning. Processing complex images or generating detailed responses can take significant time.

2. Network latency: The difference in response time between direct HuggingFace testing and Bedrock could be due to additional network latency when routing through AWS services.

3. Resource allocation: Bedrock may dynamically allocate resources, which could lead to varying processing times depending on current load and availability.

4. Timeout settings: The default timeout for AWS SDK operations might be too short for some complex image processing tasks.

To address this issue, you can try the following:

1. Increase timeout: Set a higher timeout value in your AWS SDK configuration. This is often recommended for vision LLMs used through Bedrock, especially for complex tasks or large images.

2. Use streaming: Consider using the ConverseStream API instead of the standard Converse API. Streaming can help you start receiving partial responses earlier, potentially avoiding timeout issues.

3. Optimize images: Ensure your images are optimized and not unnecessarily large, as this can impact processing time.

4. Monitor and retry: Implement a retry mechanism with exponential backoff for timeout errors, and continue monitoring CloudWatch metrics to understand resource usage patterns.

5. Check image compatibility: Ensure the images you're using are compatible with the model's expected input format and resolution.

If the issue persists after trying these solutions, it may be worth contacting AWS support for further assistance, as there could be specific service-related factors affecting your requests.
Sources
Supported foundation models in Amazon Bedrock - Amazon Bedrock
Introducing Llama 3.2 models from Meta in Amazon Bedrock: A new generation of multimodal vision and lightweight models | AWS News Blog
Invoke Meta Llama on Amazon Bedrock using Bedrock's Converse API with a response stream - Amazon Bedrock

It turns out the model was randomly returning large amount of repetitive data bloating the response output token size to 8k. Added the max_token limit and tweaked the prompt so the model doesn't repeat output text.