commit cc4eb988e654103feef9788632464a95211389ec Author: ModelHub XC Date: Wed Jun 3 03:55:14 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: kakaocorp/kanana-nano-2.1b-instruct Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..7be2684 --- /dev/null +++ b/README.md @@ -0,0 +1,767 @@ +--- +language: +- en +- ko +library_name: transformers +license: cc-by-nc-4.0 +pipeline_tag: text-generation +model_id: kakaocorp/kanana-nano-2.1b-instruct +repo: kakaocorp/kanana-nano-2.1b-instruct +developers: Kanana LLM +training_regime: bf16 mixed precision +--- + +# Kanana +

+
+ + + +
+

🤗 Models   |   📕 Blog   |   📜 Technical Report |   💻 Github + +
+ +
+ +## Introduction + +We introduce Kanana, a series of bilingual language models (developed by [Kakao](https://github.com/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. + +> [!Note] +> Neither the pre-training nor the post-training data includes Kakao user data. + +

+ + + + +
+ +## Table of Contents + +- [News](#news) +- [Performance](#performance) +- [Quickstart](#quickstart) +- [License](#license) +- [Citation](#citation) +- [Contributors](#contributors) +- [Contact](#contact) + +
+ +## News + +- 📜`2025/02/27`: Released [Technical Report](https://arxiv.org/abs/2502.18934) and 🤗[HF model weights](https://huggingface.co/collections/kakaocorp/kanana-nano-21b-67a326cda1c449c8d4172259). +- 📕`2025/01/10`: Published a blog post about the development of `Kanana-Nano` model. ([Kanana-Nano](https://tech.kakao.com/posts/682)) +- 📕`2024/11/14`: Published blog posts about the development of `Kanana` models. ([Kanana LLM: Pre-training](https://tech.kakao.com/posts/661), [Kanana LLM: Post-training](https://tech.kakao.com/posts/662)) +- ▶️`2024/11/06`: Published a presentation video about the development of the `Kanana` models. ([if(kakaoAI)2024](https://youtu.be/HTBl142x9GI?si=o_we6t9suYK8DfX3)) + +
+ +## Performance + +Below are partial report on the performance of the `Kanana` model series. Please refer to the [Technical Report](https://arxiv.org/abs/2502.18934) for the full results. + +### Pre-trained Model Performance + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsMMLUKMMLUHAERAEHumanEvalMBPPGSM8K
27b+ scale
Kanana-Flag-32.5b77.6862.1090.4751.2263.4070.05
Qwen2.5-32b83.1063.1575.1650.0073.4082.41
Gemma-2-27b75.4551.1669.1151.2264.6074.37
EXAONE-3.5-32b72.6846.3682.22---
Aya-Expanse-32b74.5249.5780.66---
7b+ scale
Kanana-Essence-9.8b67.6150.5784.9840.2453.6063.61
Llama-3.1-8b65.1841.0261.7835.3748.6050.87
Qwen2.5-7b74.1951.6867.4656.7163.2083.85
Gemma-2-9b70.3448.1866.1837.2053.6068.16
EXAONE-3.5-7.8b65.3645.3077.54---
Aya-Expanse-8b62.5240.1171.95---
2b+ scale
Kanana-Nano-2.1b54.8344.8077.0931.1046.2046.32
Llama-3.2-3b56.4035.5747.6625.6139.0027.37
Qwen2.5-3b65.5745.2861.3237.8055.6069.07
Gemma-2-2b52.8930.6745.5520.1228.2024.72
EXAONE-3.5-2.4b59.2743.5869.65---
70b+ scale
Llama-3.1-70b78.9353.0076.3557.3266.6081.73
Qwen2.5-72b86.1268.5780.8455.4976.4092.04
+ +
+ + +### Post-trained Model Performance + +#### Instruction-following Benchmarks + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsMT-BenchLogicKorKoMT-BenchWildBenchIFEval
27b+ scale
Kanana-Flag-32.5b8.3569.5248.05854.140.856
Qwen2.5-32b8.3318.9887.84751.130.822
Gemma-2-27b8.0888.8697.37346.460.817
EXAONE-3.5-32b8.3759.2027.90754.300.845
Aya-Expanse-32b7.7888.9417.62648.360.735
7b+ scale
Kanana-Essence-9.8b7.7698.9647.70647.270.799
Llama-3.1-8b7.5006.5125.33633.200.772
Qwen2.5-7b7.6257.9526.80841.310.760
Gemma-2-9b7.6338.6437.02940.920.750
EXAONE-3.5-7.8b8.2139.3578.01350.980.826
Aya-Expanse-8b7.1318.3577.00638.500.645
2b+ scale
Kanana-Nano-2.1b6.4007.9645.85725.410.720
Llama-3.2-3b7.0504.4523.96721.910.767
Qwen2.5-3b6.9696.4885.27425.760.355
Gemma-2-2b7.2255.9174.83528.710.428
EXAONE-3.5-2.4b7.9198.9417.22341.680.790
70b+ scale
Llama-3.1-70b8.2758.2506.97046.500.875
Qwen2.5-72b8.6199.2148.28155.250.861
+ +
+ +#### General Benchmarks + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsMMLUKMMLUHAE-RAEHumanEval+MBPP+GSM8KMATH
27b+ scale
Kanana-Flag-32.5b81.0864.1968.1877.4469.8490.8357.82
Qwen2.5-32b84.4059.3748.3082.3271.9695.3081.90
Gemma-2-27b78.0149.9846.0270.1270.9091.0553.80
EXAONE-3.5-32b78.3055.4452.2778.6670.9093.5676.80
Aya-Expanse-32b74.4942.3551.1464.6365.6175.0642.82
7b+ scale
Kanana-Essence-9.8b70.6450.7647.1672.5669.0584.9142.24
Llama-3.1-8b71.1839.2440.9160.9857.6782.7149.86
Qwen2.5-7b77.2346.8737.5073.7870.6391.5875.22
Gemma-2-9b73.4744.4739.7759.7664.5587.7248.10
EXAONE-3.5-7.8b72.6252.0946.0279.2766.6789.9973.50
Aya-Expanse-8b61.2335.7839.2042.6856.8878.8530.80
2b+ scale
Kanana-Nano-2.1b52.4838.5133.5263.4162.4372.3229.26
Llama-3.2-3b56.093.0717.0556.7150.2666.5738.18
Qwen2.5-3b69.1838.3332.3967.6864.0284.0065.72
Gemma-2-2b57.696.997.9535.3745.2449.8121.68
EXAONE-3.5-2.4b63.1914.2714.2070.7359.7983.7864.04
70b+ scale
Llama-3.1-70b83.4839.0853.4175.6166.4091.6663.98
Qwen2.5-72b87.1465.7860.8081.1075.6695.4582.60
+ +
+ +### Embedding Model Performance + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BackboneKanana-Nano-2.1bLlama-3.2-3bQwen2.5-3bLlama-3.2-1bQwen-2.5-1.5b
English51.5653.2854.0048.7750.60
Korean65.0059.4362.1054.6854.60
Avg.58.2856.3558.0551.7352.60
+ +
+ +## Quickstart + +### 🤗 HuggingFace Transformers + +- `transformers>=4.45.0` or the latest version is required to run `Kanana` model. +```bash +pip install transformers>=4.45.0 +``` + +#### Example Usage for `kanana-nano-2.1b-instruct` +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "kakaocorp/kanana-nano-2.1b-instruct" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + trust_remote_code=True, +).to("cuda") +tokenizer = AutoTokenizer.from_pretrained(model_name) + +prompt = "Convert these dates to YYYY/MM/DD format:\n12/31/2021\n02-01-22" +messages = [ + {"role": "system", "content": "You are a helpful AI assistant developed by Kakao."}, + {"role": "user", "content": prompt} +] + +input_ids = tokenizer.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt" +).to("cuda") + +_ = model.eval() +with torch.no_grad(): + output = model.generate( + input_ids, + max_new_tokens=72, + do_sample=False, + ) + +print(tokenizer.decode(output[0], skip_special_tokens=True)) + +# Output: +# Sure! Here are the given dates converted to the `YYYY/MM/DD` format: + +# 1. **12/31/2021** +# - **YYYY/MM/DD:** 2021/12/31 + +# 2. **02-01-22** +# - **YYYY/MM/DD:** 2022/02/01 + +# So, the converted dates are ... +``` + +
+ +## License + +The `Kanana` models are licensed under [CC-BY-NC-4.0](https://spdx.org/licenses/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 + +
+ +## Contact +- Kanana LLM Team Technical Support: kanana-llm@kakaocorp.com +- Business & Partnership Contact: alpha.k@kakaocorp.com \ No newline at end of file diff --git a/assets/logo/kanana-logo.png b/assets/logo/kanana-logo.png new file mode 100644 index 0000000..e446d47 Binary files /dev/null and b/assets/logo/kanana-logo.png differ diff --git a/assets/performance/flops-vs-mmlu.jpg b/assets/performance/flops-vs-mmlu.jpg new file mode 100644 index 0000000..14e5aec Binary files /dev/null and b/assets/performance/flops-vs-mmlu.jpg differ diff --git a/config.json b/config.json new file mode 100644 index 0000000..1b81ff6 --- /dev/null +++ b/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 1792, + "initializer_range": 0.02, + "intermediate_size": 8064, + "max_position_embeddings": 8192, + 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