AI is happening. The future is updating.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.
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.