kanana-1.5-15.7b-a3b-base
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`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
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`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
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- Kanana-1.5-v-3b-instruct - Intended Use - Model Details - Evaluation - Model Configuration Summary - Overview - Image Benchmarks (EN) - Image Benchmarks (KO) - Multimodal Instruction Following Benchmarks (EN, KO) - Note on Benchmarking Methodology - Usage - Requirements - Quickstart - Limitations - Contributors - Contact The Unified Foundation Model (UFO) task force of Kanana at Kakao developed and released the Kanana-V family of multimodal large language models (MLLMs), a collection of pretrained text/image-to-text (TI2T) models. kanana-1.5-v-3b-instruct is intended for research and application development in multimodal understanding and text generation tasks. Typical use cases include image captioning, document understanding, OCR-based reasoning, and multimodal instruction following in both English and Korean. The model is optimized for both general-purpose and Korea-specific benchmarks, making it suitable for bilingual environments.
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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.
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