Quantized version of Mistral-Small-24B-Instruct-2501.

This model was obtained by quantizing the weights and activations to W8A8 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.

This model can be deployed efficiently using the vLLM backend.

This model was created with llm-compressor and the neuralmagic/LLM_compression_calibration dataset.