AI is happening. The future is updating.Mistral-Small-24B-Instruct-2501-FP8-dynamic
- **Model Architecture:** Mistral-Small-24B-Instruct-2501 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/1/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** Neural Magic Quantized version of Mistral-Small-24B-Instruct-2501. It achieves an average score of 78.88 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 79.45. This model was obtained by quantizing the weights and activations 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.
- **Model Architecture:** Mistral-Small-24B-Instruct-2501 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/1/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** Neural Magic Quantized version of Mistral-Small-24B-Instruct-2501. It achieves an average score of 78.88 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 79.45. This model was obtained by quantizing the weights and activations 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.