pbhappliedsystems/quant_eval_efficiency_and_footprint

quant_eval — Efficiency and footprint One row per published run: stored weight artifact bytes before and after quantization, compression ratio, observed evaluation wall-time ratio with an explicit direction label, the accelerator used on each lane, and token throughput. Part of the quant_eval public corpus: a per-case behavioral evaluation of full-weight and quantized large language models across eight agent-relevant task families, with paired statistical testing. Cite this… See the full description on the dataset page: https://huggingface.co/datasets/pbhappliedsystems/quant_eval_efficiency_and_footprint.