Worked on the jeejeelee/vllm repository to enhance the reliability of image input processing by addressing error handling for corrupt or truncated images. Focused on backend development and image processing using Python, the work involved modifying the API to return HTTP 400 responses for invalid image inputs instead of generic 500 errors. This adjustment aligned error responses with client expectations and improved the user experience. Automated tests were added to validate the new behavior, strengthening the robustness of the image loading workflow. The approach emphasized test-driven development and code quality, ensuring that error handling patterns were consistent and maintainable across the codebase.