MODEL PROFILE · 模型档案

Qwen3-Swallow-8B-SFT-v0.2

tokyotech-llm · Qwen3-Swallow-8B-SFT-v0.2

**Qwen3-Swallow v0.2** is a family of large language models available in **8B**, **30B-A3B**, and **32B** parameter sizes. Built as bilingual Japanese-English models, they were developed through Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning with Verifiable Rewards (RLVR) based on the Qwen3 [Yang, 2025]. In addition to enhancing Japanese language proficiency and Japanese-English translation capabilities, we significantly improved or maintained performance on math and coding tasks during CPT by using high-quality math and code datasets with reasoning traces, along with custom-built datasets during SFT. Subsequently, we further improved the models' math and coding performance by enhancing reasoning capabilities through RLVR. We are releasing nine Qwen3-Swallow models: three each for CPT, SFT, and RL models. Quantized versions of RL models are also available. The complete list is as follows:

译文排队中,当前展示来源原文。

文本生成license:apache-2.0
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 19:15:46
OVERVIEW

模型介绍

**Qwen3-Swallow v0.2** is a family of large language models available in **8B**, **30B-A3B**, and **32B** parameter sizes. Built as bilingual Japanese-English models, they were developed through Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning with Verifiable Rewards (RLVR) based on the Qwen3 [Yang, 2025]. In addition to enhancing Japanese language proficiency and Japanese-English translation capabilities, we significantly improved or maintained performance on math and coding tasks during CPT by using high-quality math and code datasets with reasoning traces, along with custom-built datasets during SFT. Subsequently, we further improved the models' math and coding performance by enhancing reasoning capabilities through RLVR. We are releasing nine Qwen3-Swallow models: three each for CPT, SFT, and RL models. Quantized versions of RL models are also available. The complete list is as follows:

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

—

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: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。