Qwen2.5-7B-Instruct-FP8-dynamic

モデル紹介

- **Model Architecture:** Qwen2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Activation quantization:** FP8 - **Weight quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to Qwen2.5-7B, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). - **Release Date:** 11/27/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** apache-2.0 - **Model Developers:** Neural Magic This model was obtained by quantizing activations and weights of Qwen2.5-7B-Instruct 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.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

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。

よくある質問

出典と追跡記録

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。