Seoul Economic Daily EnglishCodex · 实盘不做把握 8/10
北京时间·🕒 发布 22:39:32时区推断·⟳ 抓取 23:14:50·⚡ 决策 07-28 23:15:26 +36s·90.7 天前
Kakao 开源 Kanana-2 轻量级 AI 模型
Kakao 发布并开源四款 Kanana-2 轻量级模型,商业使用许可有助于扩大韩国本土 AI 生态影响力。
📄 原文Seoul Economic Daily English↗ 原文
Kakao Open-Sources 'Kanana-2' Lightweight AI Models with 30% Better Korean
Kakao has open-sourced the small language models (SLM) of its in-house developed artificial intelligence (AI) model "Ka…
不做 Kakao(035720.KS)
自研轻量级模型开源属于软催化,未披露订单、付费客户或相对市值可量化的收入利润增量,不做。
展开详情(成交明细 / 研究档案 / 原文全文 / 决策记录链)
研究档案
🔎 新鲜度:最早出现 2026-07-28 · 扩散 low · price-in 风险 medium ↗
关键理由
- Kakao 发布并开源四款 Kanana-2 轻量级模型,商业使用许可有助于扩大韩国本土 AI 生态影响力。
- 事件本质是自研模型开源与技术宣传,未披露新增订单、付费客户、收入分成或可量化商业化增量。
- 对 Kakao 这类大市值平台公司,单次开源模型发布难以形成短周期可回测的同向交易 edge。
主要风险
- 开源许可可能提升生态开发者采用,但短期变现路径和财务贡献不清晰。
原文全文
展开原文 · 2,872 字
Kakao has open-sourced the small language models (SLM) of its in-house developed artificial intelligence (AI) model "Kanana."
Kakao (035720) said on the 28th that it has open-sourced four Kanana-2 series SLMs, which can run on device environments such as smartphones, on the global AI platform Hugging Face.
In the AI industry recently, the importance of lightweight language models that can run directly in environments such as smartphones and personal PCs has been highlighted. Global companies are focusing on gaining an early lead in the lightweight AI ecosystem, either by presenting on-device AI as a key strategy or by successively releasing lightweight open-source models. Amid this trend, Kakao has continuously researched lightweight language models with performance levels applicable to actual services.
The models released this time are characterized by achieving global-level performance in both Korean and English despite their small size. Compared to the latest state-of-the-art (SOTA) open-source models of similar size, they recorded superior performance in most representative benchmarks, including Korean, English, knowledge, math and code. In particular, the open-sourced "Kanana-2-1.3B-Instruct" and the smaller "Kanana-2-0.9B-Instruct" showed competitive performance against global models in the same class, such as Qwen and Gemma, in actual service capabilities including conversation, knowledge, code, math, instruction-following and tool calling. In addition, Kakao (035720) applied its in-house developed Korean-specialized tokenizer to improve Korean processing efficiency by more than 30% compared to before.
It is also notable that the models are optimized for on-device environments with limited memory and computing resources. To solve the problem of memory usage surging sharply when processing long conversations, a "sliding window attention" structure was introduced, allowing memory to be reduced by up to 72.7% while maintaining stable performance even in conversations of up to 32K (32,000 tokens, about 24,000 words) in length.
Kakao is distributing the models under the "Kanana Open License," which permits even commercial use. Anyone, including developers, startups and research institutions, can use the technology without separate restrictions. Through this, the models are expected to contribute to expanding the foundation for using high-performance Korean AI models domestically and to invigorating the development of new services.
Noh Byoung-seok, performance leader of Kakao's Unified Foundation Model team, said, "As we prepare for the era of agentic AI, we are feeling the importance of both large-scale cloud AI and lightweight on-device AI." He added, "We hope these models will lead to the development of new AI services by more developers and companies, contributing to the revitalization of the domestic AI ecosystem."
Kakao (035720) said on the 28th that it has open-sourced four Kanana-2 series SLMs, which can run on device environments such as smartphones, on the global AI platform Hugging Face.
In the AI industry recently, the importance of lightweight language models that can run directly in environments such as smartphones and personal PCs has been highlighted. Global companies are focusing on gaining an early lead in the lightweight AI ecosystem, either by presenting on-device AI as a key strategy or by successively releasing lightweight open-source models. Amid this trend, Kakao has continuously researched lightweight language models with performance levels applicable to actual services.
The models released this time are characterized by achieving global-level performance in both Korean and English despite their small size. Compared to the latest state-of-the-art (SOTA) open-source models of similar size, they recorded superior performance in most representative benchmarks, including Korean, English, knowledge, math and code. In particular, the open-sourced "Kanana-2-1.3B-Instruct" and the smaller "Kanana-2-0.9B-Instruct" showed competitive performance against global models in the same class, such as Qwen and Gemma, in actual service capabilities including conversation, knowledge, code, math, instruction-following and tool calling. In addition, Kakao (035720) applied its in-house developed Korean-specialized tokenizer to improve Korean processing efficiency by more than 30% compared to before.
It is also notable that the models are optimized for on-device environments with limited memory and computing resources. To solve the problem of memory usage surging sharply when processing long conversations, a "sliding window attention" structure was introduced, allowing memory to be reduced by up to 72.7% while maintaining stable performance even in conversations of up to 32K (32,000 tokens, about 24,000 words) in length.
Kakao is distributing the models under the "Kanana Open License," which permits even commercial use. Anyone, including developers, startups and research institutions, can use the technology without separate restrictions. Through this, the models are expected to contribute to expanding the foundation for using high-performance Korean AI models domestically and to invigorating the development of new services.
Noh Byoung-seok, performance leader of Kakao's Unified Foundation Model team, said, "As we prepare for the era of agentic AI, we are feeling the importance of both large-scale cloud AI and lightweight on-device AI." He added, "We hope these models will lead to the development of new AI services by more developers and companies, contributing to the revitalization of the domestic AI ecosystem."
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