Developed a robust validation sampler for small datasets within the NVIDIA/Megatron-LM repository, addressing the challenge of reliable model evaluation when data is limited. The solution introduced a specialized sampling approach in Python, leveraging data processing and distributed computing techniques to ensure reproducible and stable validation results. By resolving the multivalidation issue, the work improved the correctness and efficiency of the validation pipeline, reducing evaluation variance and enabling faster experimentation cycles. This enhancement supports more trustworthy benchmarking and informed hyperparameter selection for Megatron-LM users working with small datasets, reflecting a focused application of machine learning and unit testing skills.