MODEL PROFILE · 模型档案

Skywork-Reward-V2-Llama-3.1-8B

Skywork · Skywork-Reward-V2-Llama-3.1-8B

**Skywork-Reward-V2** is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared with the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements: In the following table, we categorize the models into two types: Bradley-Terry (BT) reward models and Generative reward models. The Skywork-Reward-V2 series outperforms models in both categories with much smaller model sizes. 1. For most use cases, we recommend Skywork-Reward-V2-Llama-3.1-8B and consider smaller variants for low-resource settings. 2. All models are trained on preference data with a maximum length of 16,384 tokens. It is recommended to perform inference within this limit. 3. Do not include system prompts when using chat templates.

译文排队中,当前展示来源原文。

文本分析license:llama3.1
上下文窗口来源未披露
输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:17:06
OVERVIEW

模型介绍

**Skywork-Reward-V2** is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared with the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements: In the following table, we categorize the models into two types: Bradley-Terry (BT) reward models and Generative reward models. The Skywork-Reward-V2 series outperforms models in both categories with much smaller model sizes. 1. For most use cases, we recommend Skywork-Reward-V2-Llama-3.1-8B and consider smaller variants for low-resource settings. 2. All models are trained on preference data with a maximum length of 16,384 tokens. It is recommended to perform inference within this limit. 3. Do not include system prompts when using chat templates.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:llama3.1。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。