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metadata
language:
  - en
  - ko
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
model_id: kakaocorp/kanana-nano-2.1b-base
repo: kakaocorp/kanana-nano-2.1b-base
developers: Kanana LLM
training_regime: bf16 mixed precision
model-index:
  - name: kanana-nano-2.1b-base
    results:
      - task:
          type: multiple_choice
          name: mmlu
        dataset:
          name: mmlu (5-shots)
          type: hails/mmlu_no_train
        metrics:
          - type: acc
            value: 54.83
            name: acc
      - task:
          type: generate_until
          name: kmmlu
        dataset:
          name: kmmlu-direct (5-shots)
          type: HAERAE-HUB/KMMLU
        metrics:
          - type: exact_match
            value: 44.83
            name: exact_match
      - task:
          type: multiple_choice
          name: haerae
        dataset:
          name: haerae (5-shots)
          type: HAERAE-HUB/HAE_RAE_BENCH
        metrics:
          - type: acc_norm
            value: 77.09
            name: acc_norm
      - task:
          type: generate_until
          name: gsm8k
        dataset:
          name: gsm8k (5-shots)
          type: openai/gsm8k
        metrics:
          - type: exact_match
            value: 46.32
            name: exact_match_strict
      - task:
          type: generate_until
          name: humaneval
        dataset:
          name: humaneval (0-shots)
          type: openai/openai_humaneval
        metrics:
          - type: pass@1
            value: 31.1
            name: pass@1
      - task:
          type: generate_until
          name: mbpp
        dataset:
          name: mbpp (3-shots)
          type: google-research-datasets/mbpp
        metrics:
          - type: pass@1
            value: 46.2
            name: pass@1

Kanana



πŸ€— Models   |   πŸ“• Blog   |   πŸ“œ Technical Report |   πŸ’» Github

Introduction

We introduce Kanana, a series of bilingual language models (developed by Kakao) that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana is significantly lower than that of state-of-the-art models of similar size. The report details the techniques employed during pre-training to achieve compute-efficient yet competitive models, including high-quality data filtering, staged pre-training, depth up-scaling, and pruning and distillation. Furthermore, the report outlines the methodologies utilized during the post-training of the Kanana models, encompassing supervised fine-tuning and preference optimization, aimed at enhancing their capability for seamless interaction with users. Lastly, the report elaborates on plausible approaches used for language model adaptation to specific scenarios, such as embedding, function calling, and Retrieval Augmented Generation (RAG). The Kanana model series spans from 2.1B to 32.5B parameters with 2.1B models (base, instruct, embedding, function call, and RAG) publicly released to promote research on Korean language models.

Neither the pre-training nor the post-training data includes Kakao user data.


Table of Contents


News


Performance

Below are partial report on the performance of the Kanana model series. Please refer to the Technical Report for the full results.

Pre-trained Model Performance

Models MMLU KMMLU HAERAE HumanEval MBPP GSM8K
27b+ scale
Kanana-Flag-32.5b 77.68 62.10 90.47 51.22 63.40 70.05
Qwen2.5-32b 83.10 63.15 75.16 50.00 73.40 82.41
Gemma-2-27b 75.45 51.16 69.11 51.22 64.60 74.37
EXAONE-3.5-32b 72.68 46.36 82.22 - - -
Aya-Expanse-32b 74.52 49.57 80.66 - - -
7b+ scale
Kanana-Essence-9.8b 67.61 50.57 84.98 40.24 53.60 63.61
Llama-3.1-8b 65.18 41.02 61.78 35.37 48.60 50.87
Qwen2.5-7b 74.19 51.68 67.46 56.71 63.20 83.85
Gemma-2-9b 70.34 48.18 66.18 37.20 53.60 68.16
EXAONE-3.5-7.8b 65.36 45.30 77.54 - - -
Aya-Expanse-8b 62.52 40.11 71.95 - - -
2b+ scale
Kanana-Nano-2.1b 54.83 44.80 77.09 31.10 46.20 46.32
Llama-3.2-3b 56.40 35.57 47.66 25.61 39.00 27.37
Qwen2.5-3b 65.57 45.28 61.32 37.80 55.60 69.07
Gemma-2-2b 52.89 30.67 45.55 20.12 28.20 24.72
EXAONE-3.5-2.4b 59.27 43.58 69.65 - - -
70b+ scale
Llama-3.1-70b 78.93 53.00 76.35 57.32 66.60 81.73
Qwen2.5-72b 86.12 68.57 80.84 55.49 76.40 92.04

Post-trained Model Performance

Instruction-following Benchmarks

Models MT-Bench LogicKor KoMT-Bench WildBench IFEval
27b+ scale
Kanana-Flag-32.5b 8.356 9.524 8.058 54.14 0.856
Qwen2.5-32b 8.331 8.988 7.847 51.13 0.822
Gemma-2-27b 8.088 8.869 7.373 46.46 0.817
EXAONE-3.5-32b 8.375 9.202 7.907 54.30 0.845
Aya-Expanse-32b 7.788 8.941 7.626 48.36 0.735
7b+ scale
Kanana-Essence-9.8b 7.769 8.964 7.706 47.27 0.799
Llama-3.1-8b 7.500 6.512 5.336 33.20 0.772
Qwen2.5-7b 7.625 7.952 6.808 41.31 0.760
Gemma-2-9b 7.633 8.643 7.029 40.92 0.750
EXAONE-3.5-7.8b 8.213 9.357 8.013 50.98 0.826
Aya-Expanse-8b 7.131 8.357 7.006 38.50 0.645
2b+ scale
Kanana-Nano-2.1b 6.400 7.964 5.857 25.41 0.720
Llama-3.2-3b 7.050 4.452 3.967 21.91 0.767
Qwen2.5-3b 6.969 6.488 5.274 25.76 0.355
Gemma-2-2b 7.225 5.917 4.835 28.71 0.428
EXAONE-3.5-2.4b 7.919 8.941 7.223 41.68 0.790
70b+ scale
Llama-3.1-70b 8.275 8.250 6.970 46.50 0.875
Qwen2.5-72b 8.619 9.214 8.281 55.25 0.861

General Benchmarks

Models MMLU KMMLU HAE-RAE HumanEval+ MBPP+ GSM8K MATH
27b+ scale
Kanana-Flag-32.5b 81.08 64.19 68.18 77.44 69.84 90.83 57.82
Qwen2.5-32b 84.40 59.37 48.30 82.32 71.96 95.30 81.90
Gemma-2-27b 78.01 49.98 46.02 70.12 70.90 91.05 53.80
EXAONE-3.5-32b 78.30 55.44 52.27 78.66 70.90 93.56 76.80
Aya-Expanse-32b 74.49 42.35 51.14 64.63 65.61 75.06 42.82
7b+ scale
Kanana-Essence-9.8b 70.64 50.76 47.16 72.56 69.05 84.91 42.24
Llama-3.1-8b 71.18 39.24 40.91 60.98 57.67 82.71 49.86
Qwen2.5-7b 77.23 46.87 37.50 73.78 70.63 91.58 75.22
Gemma-2-9b 73.47 44.47 39.77 59.76 64.55 87.72 48.10
EXAONE-3.5-7.8b 72.62 52.09 46.02 79.27 66.67 89.99 73.50
Aya-Expanse-8b 61.23 35.78 39.20 42.68 56.88 78.85 30.80
2b+ scale
Kanana-Nano-2.1b 52.48 38.51 33.52 63.41 62.43 72.32 29.26
Llama-3.2-3b 56.09 3.07 17.05 56.71 50.26 66.57 38.18
Qwen2.5-3b 69.18 38.33 32.39 67.68 64.02 84.00 65.72
Gemma-2-2b 57.69 6.99 7.95 35.37 45.24 49.81 21.68
EXAONE-3.5-2.4b 63.19 14.27 14.20 70.73 59.79 83.78 64.04
70b+ scale
Llama-3.1-70b 83.48 39.08 53.41 75.61 66.40 91.66 63.98
Qwen2.5-72b 87.14 65.78 60.80 81.10 75.66 95.45 82.60

Embedding Model Performance

Backbone Kanana-Nano-2.1b Llama-3.2-3b Qwen2.5-3b Llama-3.2-1b Qwen-2.5-1.5b
English 51.56 53.28 54.00 48.77 50.60
Korean 65.00 59.43 62.10 54.68 54.60
Avg. 58.28 56.35 58.05 51.73 52.60

Quickstart

πŸ€— HuggingFace Transformers

  • transformers>=4.45.0 or the latest version is required to run Kanana model.
pip install transformers>=4.45.0

Example Usage for kanana-nano-2.1b-base 

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "kakaocorp/kanana-nano-2.1b-base"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token

prompt1 = "이처럼 μΈκ°„μ²˜λŸΌ μƒκ°ν•˜κ³  ν–‰λ™ν•˜λŠ” AI λͺ¨λΈμ€ "
prompt2 = "Kakao is a leading company in South Korea, and it is known for "

input_ids = tokenizer(
    [prompt1, prompt2],
    padding=True,
    return_tensors="pt",
)["input_ids"].to("cuda")

_ = model.eval()
with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=32,
        do_sample=False,
    )

decoded = tokenizer.batch_decode(output, skip_special_tokens=True)
for text in decoded:
    print(text)

# Output:
# 이처럼 μΈκ°„μ²˜λŸΌ μƒκ°ν•˜κ³  ν–‰λ™ν•˜λŠ” AI λͺ¨λΈμ€ 2020λ…„λŒ€ μ€‘λ°˜μ— λ“±μž₯ν•  κ²ƒμœΌλ‘œ μ˜ˆμƒλœλ‹€. 2020λ…„λŒ€ μ€‘λ°˜μ— λ“±μž₯ν•  κ²ƒμœΌλ‘œ μ˜ˆμƒλ˜λŠ” AI λͺ¨λΈμ€ 인간
# Kakao is a leading company in South Korea, and it is known for 1) its innovative products and services, 2) its commitment to sustainability, and 3) its focus on customer experience. Kakao has been recognized as

License

The Kanana models are licensed under CC-BY-NC-4.0.


Citation 

@misc{kananallmteam2025kananacomputeefficientbilinguallanguage,
      title={Kanana: Compute-efficient Bilingual Language Models}, 
      author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo},
      year={2025},
      eprint={2502.18934},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.18934}, 
}

Contributors

  • Pre-training: Yunju Bak, Doohae Jung, Boseop Kim, Nayeon Kim, Hojin Lee, Jaesun Park, Minho Ryu
  • Post-training: Jiyeon Ham, Seungjae Jung, Hyunho Kim, Hyunwoong Ko, Changmin Lee, Daniel Wontae Nam, Kyoung-Woon On
  • Adaptation: Seulye Baeg, Junrae Cho, Taegyeong Eo, Sunghee Jung, Jieun Kang, EungGyun Kim, Eunhwa Kim, Byeongil Ko, Daniel Lee, Donghun Lee, Minchul Lee, Miok Lee, Shinbok Lee, Minho Ryu, Gaeun Seo

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