AI is happening. The future is updating.Ministral-3-14B-Instruct-2512-FP8-dynamic
This model was obtained by quantizing the weights and activations of mistralai/Ministral-3-14B-Instruct-2512-BF16 to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. The model was evaluated on the ifeval and mmmu using lm-evaluation-harness, on reasoning tasks using lighteval. vLLM was used for all evaluations.
This model was obtained by quantizing the weights and activations of mistralai/Ministral-3-14B-Instruct-2512-BF16 to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. The model was evaluated on the ifeval and mmmu using lm-evaluation-harness, on reasoning tasks using lighteval. vLLM was used for all evaluations.