Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic

모델 소개

- **Model Architecture:** Mistral3ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Activation quantization:** FP8 - **Weight quantization:** FP8 - **Intended Use Cases:** It is ideal for: - Fast-response conversational agents. - Low-latency function calling. - Subject matter experts via fine-tuning. - Local inference for hobbyists and organizations handling sensitive data. - Programming and math reasoning. - Long document understanding. - Visual understanding. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages not officially supported by the model. - **Release Date:** 04/15/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing activations and weights of Mistral-Small-3.1-24B-Instruct-2503 to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

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공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。