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AI 模型库/kakaocorp/kanana-2-3b-instruct
MODEL PROFILE · 模型档案

kanana-2-3b-instruct

kakaocorp · kanana-2-3b-instruct

We present Kanana-2 SLM, **Kakao's second series of Small Language Models (SLMs)**, designed to deliver strong language capabilities while remaining compact and efficient for practical deployment. The series includes 3B, 1.3B, and 0.9B models. This release publicly includes the 3B model and the compressed 1.3B model, providing a balance between capability and efficiency for a wide range of applications. Kanana-2-3B was **pretrained from scratch on TPU clusters** and further improved through post-training with **supervised fine-tuning and reinforcement learning**, resulting in strong instruction-following and reasoning capabilities. Kanana-2-1.3B models are derived from Kanana-2-3B through a **cascade pruning and distillation pipeline**. To further improve deployment efficiency, they adopt **Sliding Window Attention (SWA)**, enabling memory-efficient long-context inference with support for context lengths of up to **32K tokens** while substantially reducing KV-cache memory requirements. General Tasks MMLU acc 5 62.77 56.49 62.32 MMLU-Pro acc 5 36.54 29.87 37.08 BBH acc 3 54.23 45.58 53.60 SimpleQA † acc 5 27.39 23.76 16.83 Mathematics Tasks MATH em 4 35.08 30.82 41.74 GSM8K em 8 61.03 51.93 75.74 Coding Tasks HumanEval pass@1 0 55.96 51.33 45.31 MBPP pass@1 3 50.95 44.86 53.85 Korean Tasks KMMLU acc 5 47.92 44.17 43.08 KoSimpleQA † acc 5 32.50 28.50 13.40 HAE-RAE Bench (v1.0) acc 5 80.66 75.34 55.54 MATH-Ko ‡ em 4 31.54 24.75 31.92 MBPP-Ko § pass@1 3 44.55 39.38 47.97 Long Context Tasks RULER-32K acc 0 67.50 55.72 69.01 Instruction-following, chat, tool-calling, code, math, and knowledge benchmarks for the Kanana-2 SLM series. Scores use greedy decoding (temperature 0.0, top-p 1.0, max 4096 tokens); the metric for each benchmark is listed in the Metric column.

译文排队中，当前展示来源原文。

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输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:19
OVERVIEW

模型介绍

We present Kanana-2 SLM, **Kakao's second series of Small Language Models (SLMs)**, designed to deliver strong language capabilities while remaining compact and efficient for practical deployment. The series includes 3B, 1.3B, and 0.9B models. This release publicly includes the 3B model and the compressed 1.3B model, providing a balance between capability and efficiency for a wide range of applications. Kanana-2-3B was **pretrained from scratch on TPU clusters** and further improved through post-training with **supervised fine-tuning and reinforcement learning**, resulting in strong instruction-following and reasoning capabilities. Kanana-2-1.3B models are derived from Kanana-2-3B through a **cascade pruning and distillation pipeline**. To further improve deployment efficiency, they adopt **Sliding Window Attention (SWA)**, enabling memory-efficient long-context inference with support for context lengths of up to **32K tokens** while substantially reducing KV-cache memory requirements. General Tasks MMLU acc 5 62.77 56.49 62.32 MMLU-Pro acc 5 36.54 29.87 37.08 BBH acc 3 54.23 45.58 53.60 SimpleQA † acc 5 27.39 23.76 16.83 Mathematics Tasks MATH em 4 35.08 30.82 41.74 GSM8K em 8 61.03 51.93 75.74 Coding Tasks HumanEval pass@1 0 55.96 51.33 45.31 MBPP pass@1 3 50.95 44.86 53.85 Korean Tasks KMMLU acc 5 47.92 44.17 43.08 KoSimpleQA † acc 5 32.50 28.50 13.40 HAE-RAE Bench (v1.0) acc 5 80.66 75.34 55.54 MATH-Ko ‡ em 4 31.54 24.75 31.92 MBPP-Ko § pass@1 3 44.55 39.38 47.97 Long Context Tasks RULER-32K acc 0 67.50 55.72 69.01 Instruction-following, chat, tool-calling, code, math, and knowledge benchmarks for the Kanana-2 SLM series. Scores use greedy decoding (temperature 0.0, top-p 1.0, max 4096 tokens); the metric for each benchmark is listed in the Metric column.

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

huggingface · kakaocorp/kanana-2-3b-instruct ↗2026/10/29 17:01:56
huggingface · enrich:ec826434-786f-4407-85cf-433e7e06c073 ↗2026/10/30 04:34:35

许可证：other。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。