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  license: cc-by-nc-sa-4.0
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  ---
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+ language:
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+ - ko
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+ datasets:
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+ - kyujinpy/KOR-OpenOrca-Platypus-v3
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+ library_name: transformers
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+ pipeline_tag: text-generation
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  license: cc-by-nc-sa-4.0
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  ---
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+ **(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
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+ **The license is `cc-by-nc-sa-4.0`.**
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+
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+ # **🐳KOR-Orca-Platypus-13B🐳**
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+ ![img](./Korean-OpenOrca.png)
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+
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+ ## Model Details
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+
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+ **Model Developers** Kyujin Han (kyujinpy)
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+
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+ **Input** Models input text only.
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+
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+ **Output** Models generate text only.
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+
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+ **Model Architecture**
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+ Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
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+
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+ **Repo Link**
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+ Github Korean-OpenOrca: [🐳Korean-OpenOrca🐳](https://github.com/Marker-Inc-Korea/Korean-OpenOrca)
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+
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+ **Base Model** [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)
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+
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+ **Training Dataset**
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+ I use [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).
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+ (with NEFTune.)
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+
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+ I use A100 GPU 40GB and COLAB, when trianing.
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+
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+
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+ # **Model Benchmark**
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+
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+ ## KO-LLM leaderboard
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+ - Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
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+
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+ | Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
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+ | --- | --- | --- | --- | --- | --- | --- |
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+ | [KOR-Orca-Platypus-13B🐳] | 46.59 | 42.06 | 53.95 | 42.28 | 43.55 | 51.12 |
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+ | **KOR-Orca-Platypus-13B🐳-v2** | 49.48 | 44.03 | 54.43 | 42.23 | 41.64 | 65.05 |
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+ | KOR-Orca-Platypus-13B🐳-v3 | 48.37 | 43.77 | 54.27 | 42.66 | 38.58 | 62.57 |
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+ > Compare with Top 4 SOTA models. (update: 10/09)
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+
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+
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+ # Implementation Code
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+ ```python
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+ ### KO-Platypus
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ repo = "kyujinpy/KOR-Orca-Platypus-13B-v3"
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+ OpenOrca = AutoModelForCausalLM.from_pretrained(
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+ repo,
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+ return_dict=True,
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+ torch_dtype=torch.float16,
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+ device_map='auto'
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+ )
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+ OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
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+ ```
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+
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+ ---