Akomaragiri enhanced the NVIDIA/NeMo-Skills repository by implementing an inference configuration feature that introduces a min_p parameter to the InferenceConfig within generate.py. This addition allows explicit control over the minimum probability mass for token selection during inference, improving the balance between output quality and diversity in natural language generation tasks. The work focused on inference optimization and machine learning, leveraging Python to modify the model’s sampling behavior for more predictable and tunable results in deployment scenarios. The change was aligned with project traceability requirements, demonstrating a focused and technically sound approach to configurable natural language processing model inference.