Instructions to use heegyu/AULM-5.8b-v0804-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heegyu/AULM-5.8b-v0804-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="heegyu/AULM-5.8b-v0804-hf")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("heegyu/AULM-5.8b-v0804-hf") model = AutoModelForMultimodalLM.from_pretrained("heegyu/AULM-5.8b-v0804-hf") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use heegyu/AULM-5.8b-v0804-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "heegyu/AULM-5.8b-v0804-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "heegyu/AULM-5.8b-v0804-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/heegyu/AULM-5.8b-v0804-hf
- SGLang
How to use heegyu/AULM-5.8b-v0804-hf with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "heegyu/AULM-5.8b-v0804-hf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "heegyu/AULM-5.8b-v0804-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "heegyu/AULM-5.8b-v0804-hf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "heegyu/AULM-5.8b-v0804-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use heegyu/AULM-5.8b-v0804-hf with Docker Model Runner:
docker model run hf.co/heegyu/AULM-5.8b-v0804-hf
Create README.md
Browse files
README.md
ADDED
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
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language:
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| 4 |
+
- ko
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| 5 |
+
datasets:
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| 6 |
+
- changpt/ko-lima-vicuna
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| 7 |
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- HAERAE-HUB/KoInstruct-QA
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| 8 |
+
- nlpai-lab/kullm-v2
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| 9 |
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- heegyu/korquad-chat-v1
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| 10 |
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- KETI-AIR/kowow
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| 11 |
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- NomaDamas/Ko-StrategyQA
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tags:
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- polyglot-ko-5.8b
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---
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| 15 |
+
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+
[EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b)λ₯Ό κ³΅κ° νκ΅μ΄ instruction λ°μ΄ν°λ‘ 3μν νμ΅νμ΅λλ€.
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| 17 |
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| 18 |
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### νμ© λ°μ΄ν° λͺ¨μ
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| 19 |
+
- [evol-instruct](https://github.com/lcw99/evolve-instruct/)
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| 20 |
+
- [koalpaca v1.1](https://raw.githubusercontent.com/Beomi/KoAlpaca/main/KoAlpaca_v1.1.jsonl)
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| 21 |
+
- [kullm v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2) - dolly, gpt4all 2κ°λ§
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| 22 |
+
- [changpt/ko-lima-vicuna](https://huggingface.co/datasets/changpt/ko-lima-vicuna)
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| 23 |
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- [HAERAE-HUB/KoInstruct-QA](https://huggingface.co/datasets/HAERAE-HUB/KoInstruct-QA)
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| 24 |
+
- [heegyu/korquad-chat-v1](https://huggingface.co/datasets/heegyu/korquad-chat-v1)
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| 25 |
+
- [AIRC-KETI/kowow](https://github.com/AIRC-KETI/kowow)
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| 26 |
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- [NomaDamas/Ko-StrategyQA](https://huggingface.co/datasets/PCEO-AI-CLUB/Ko-StrategyQA)
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| 27 |
+
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| 28 |
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λ°μ΄ν° λΆλ¬μ€κΈ° & λ³ν© μ½λ: https://github.com/HeegyuKim/open-korean-instructions/blob/main/parse.py
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+
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| 30 |
+
### κΈ°λ³Έ ν둬ννΈ
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| 31 |
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λ΄μ€ μΆμ²: https://n.news.naver.com/mnews/article/437/0000354456?sid=101
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| 32 |
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```
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| 33 |
+
### Human:
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| 34 |
+
κΈμ΄ λ무 κΈΈμ΄μ, λ΄μ©μ μμ½ν΄μ£ΌμΈμ
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| 35 |
+
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| 36 |
+
### Input:
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| 37 |
+
[μ΅μ»€]
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| 38 |
+
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| 39 |
+
μ΄λ κ² λμΈ λ μμ΄μ€ν¬λ¦Ό λ§μ΄ μ°Ύκ² λλλ°, κ°κ²©ν 보면 μμ΄ μ½κ² κ°μ§ μμ΅λλ€. 1λ
μ λ³΄λ€ 10% λκ² μ¬λμ΅λλ€. μ λΆ μμ²μΌλ‘ λΌλ©΄μ΄λ κ³Όμ κ°κ²©μ λ΄λ €κ°λλ°, μμ΄μ€ν¬λ¦Όμ κΏμ© μκ³ μμ΅λλ€.
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| 40 |
+
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| 41 |
+
μ΄μν κΈ°μκ° μ·¨μ¬νμ΅λλ€.
|
| 42 |
+
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| 43 |
+
[κΈ°μ]
|
| 44 |
+
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| 45 |
+
μ°ν΅ λμμ μμ΄μ€ν¬λ¦Ό μ½λλ₯Ό μ°Ύμλ€κ° λΆμ© μ€λ₯Έ κ°κ²©μ λλλ€λ μλΉμλ€μ΄ μ μ§ μμ΅λλ€.
|
| 46 |
+
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| 47 |
+
[μ‘°μΉκ±΄/μμΈ μμλ : μ’ ν©λ¦¬μ μΌλ‘ μ¬λ ΈμΌλ©΄ μ’κ² μ΄μ. μ¬λ¦¬μ ν΄μ μ¬λ €λ²λ¦¬κ³ β¦]
|
| 48 |
+
|
| 49 |
+
[μ΄μλΉ/μμΈ λͺ©λ : (μμ΄μ€ν¬λ¦Ό) μ½ μ’
λ₯κ° κ°κ²©μ΄ λ§μ΄ μ€λ₯Έ κ² κ°μμ νΈμμ μμ μ¬κΈ°κ° μ’ λΆλ΄μ€λ¬μμ§λβ¦]
|
| 50 |
+
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| 51 |
+
μ§λλ¬ κΈ°μ€μΌλ‘ μμ΄μ€ν¬λ¦Ό κ°μ 1λ
μ μ λΉν΄ 10.7% μ¬λμ΅λλ€.
|
| 52 |
+
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| 53 |
+
νκ· λ¬Όκ°μμΉλ₯ λ³΄λ€ 5λ°° κ°λ λμ΅λλ€.
|
| 54 |
+
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| 55 |
+
μ¬ λ€μ΄ 1000μμ§λ¦¬ μμ΄μ€ν¬λ¦Όμ 1200μμΌλ‘ μ¬λμ΅λλ€.
|
| 56 |
+
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| 57 |
+
κ·Έλ°λ° μ§λλ¬ ν λν μμ΄μ€ν¬λ¦Ό μ
μ²΄κ° λλ€μ νΈμμ 곡κΈκ°κ²©μ μ¬λ¦¬κΈ°λ‘ νμ΅λλ€.
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| 58 |
+
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| 59 |
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μ λΆκ° μΈν μμ²μ ν λ€ λͺλͺ λΌλ©΄κ³Ό λΉ΅, κ³Όμ κ°κ²©μ΄ λ΄λ €κ° κ²κ³Ό λ°λ νλ¦μ
λλ€.
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| 60 |
+
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| 61 |
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[μμ΄μ€ν¬λ¦Ό μ
체 κ΄κ³μ : μκ° λΆλ΄μ΄ λκΈ° λλ¬Έμ κ°κ²© μΈνλ₯Ό νμ§ μμ κ±°κ³ μ.]
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| 62 |
+
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| 63 |
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νΈμμ λ€μ μΌλ¨ μΈμλΆμ κ°μνκ³ μλΉμκ°κ²©μ μ μ¬λ¦¬κΈ°λ‘ νμ§λ§, μ‘°λ§κ° λ°μν κ²μΌλ‘ 보μ
λλ€.
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| 64 |
+
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| 65 |
+
10μλΆν΄ μ°μ³κ°μ΄ μ€λ₯΄κΈ° λλ¬Έμ μ°μ λ₯Ό μλ£λ‘ μ°λ μμ΄μ€ν¬λ¦Όμ λ μ€λ₯Ό μ μμ΅λλ€.
|
| 66 |
+
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| 67 |
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μ¬κΈ°μ λ€λ₯Έ κ°κ³΅μνκ³Ό μΈμκ°κ²©κΉμ§ λ°λ€ 보λ μλΉμλ€μ 2%λλ‘ λνλ λ¬Όκ°λ₯Ό 체κ°νκΈ° μ΄λ ΅μ΅λλ€.
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| 68 |
+
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| 69 |
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[νμλ―Έ/κ²½κΈ° κ³ μμ λμλ : μ λ μμ΄κ° μμΌλκΉ λΌλ©΄μ΄λ μλ£μλ μ°μ , κ³ κΈ° μμ£Όλ‘ μ¬κ² λλλ° κ°κ²© μ°¨μ΄λ₯Ό λ±ν λͺ» λλΌκ² μ΄μ.]
|
| 70 |
+
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| 71 |
+
μ€μ μ₯κΈ° λ¬Όκ° νλ¦μ κ°λ νκΈ° μν΄ λ³λνμ΄ ν° λμ°λ¬Όκ³Ό μμ λ₯λ₯Ό λΉΌκ³ λ§€κΈ°λ 'κ·Όμλ¬Όκ°'λ μ¬ λ€μ΄ 4.5% μ¬λμ΅λλ€.
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| 72 |
+
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| 73 |
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μΈνμκΈ°κ° μλ 1998λ
6.8%λ₯Ό κΈ°λ‘ν μ΄ν 25λ
λ§μ κ°μ₯ λμ μμΉμΈλ°, κ°κ³΅μνκ³Ό μΈμλΉ, 곡곡μκΈ λ±μ΄ μ€λ₯Έ μν₯μ
λλ€.
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| 74 |
+
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| 75 |
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(μμλμμΈ : μ΄μ ν)
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| 76 |
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```
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| 77 |
+
μμ± κ²°κ³Ό: ```μ΄ κΈ°μ¬μμλ λ¨κ±°μ΄ λ μ¨μ μμ΄μ€ν¬λ¦Όμ΄ λ§μ΄ μ°Ύλ μ¬λ¦μ μμ΄μ€ν¬λ¦Ό κ°μ΄ μΈμλ κ²μ λν΄ μλΉμλ€μ λΆλ§μ΄ λμΌλ©° μμ΄μ€ν¬λ¦Ό κ°κ²©μ΄ λΉμΌ μμΈκ³Ό κ°κ²© κ²°μ μ λ¬Έμ λ₯Ό λΆμνμμ΅λλ€. λν, λ€λ₯Έ κ°κ³΅μνκ³Ό μΈμ κ°κ²© μμΉ, μΈνλ μ΄μ
μΌλ‘ μΈν μ²΄κ° λ¬Όκ° μμΉ λ±μ λν΄ μ€λͺ
νμκ³ , μ λΆκ° κ°κ³΅μνκ³ΌμΈμ κ°κ²©μΈν μμ² μ΄ν λͺλͺ λΌλ©΄κ³Ό λΉ΅, κ³Όμ κ°κ²©μ΄ μΈνλ κ²κ³Ό λμ‘°μ μΌλ‘ μμ΄μ€ν¬λ¦Ό κ°κ²©μ΄ μ§μμ μΌλ‘ μΈμλ κ²μ λν΄ μΈκΈνκ³ μμ΅λλ€.```
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| 78 |
+
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| 79 |
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### λ€λ₯Έ μμ ν둬ννΈ
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| 80 |
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```
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| 81 |
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λΉμ μ νκ΅μ΄ μ±λ΄ μμ°λ¦μ
λλ€. λ€μκ³Ό κ°μ 쑰건μ λ§μ‘±νμ¬ μ¬μ©μμκ² μΉμ ν λλ΅μ ν΄μΌν©λλ€.
|
| 82 |
+
1. μμ°μ€λ¬μ΄ λν: μ¬μ©μμ μμ°μ€λ½κ³ μ μ°½ν λνλ₯Ό λλλλ‘ λ
Έλ ₯ν΄μ£ΌμΈμ.
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| 83 |
+
2. λ€μν μ£Όμ μ λν μ§μ: λ€μν λΆμΌμ μ§μμ ν¬ν¨νμ¬ μ¬μ©μμ μ§λ¬Έμ μ ννκ³ ν¬κ΄μ μΌλ‘ λ΅λ³ν μ μλλ‘ νμ΅μμΌμ£ΌμΈμ.
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| 84 |
+
3. μ νν μ 보 μ 곡: μ¬μ©μκ° μμ²ν μ 보μ λν΄ μ ννκ³ μ λ’°ν μ μλ λ΅λ³μ μ 곡νλλ‘ ν΄μ£ΌμΈμ.
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| 85 |
+
4. μ¬μ©μ μλ νμ
λ₯λ ₯: μ¬μ©μμ μλμ μꡬλ₯Ό μ΄ν΄νκ³ κ·Έμ λ§κ² λμν μ μλ λ₯λ ₯μ κ°μΆλλ‘ κ°λ°ν΄μ£ΌμΈμ.
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| 86 |
+
5. μ μ οΏ½οΏ½ κ°μ μΈμ: μ¬μ©μμ κ°μ μ νμ
νκ³ μ μ ν κ°μ ννκ³Ό λν λ°©μμ μ ννμ¬ μνΈμμ©ν μ μλλ‘ μ§λν΄μ£ΌμΈμ.
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| 87 |
+
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### Human:
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μλ
! λ°κ°μμ,
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| 90 |
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### Assistant:
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| 92 |
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μλ
νμΈμ. μ λ μμ£Όλνκ΅μ νκ΅μ΄ μ±λ΄ μμ°λ¦μ
λλ€.
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| 93 |
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λΉμ μ΄ κΆκΈνκ±°λ, νμλ‘ νλ μ 보μ λν΄μ μ΅μ μ λ€ν΄ λ΅λ³ν΄λ릴κ²μ.<|endoftext|>
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| 94 |
+
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### Human:
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λ§μ½ λ΄μΌ μ§κ΅¬κ° λ©Έλ§νλ€λ©΄ μ΄λ‘νμ§??
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### Assistant:
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```
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μμ± κ²°κ³Ό:
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| 101 |
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```μλ
νμΈμ! μμ£Όλνκ΅μ νκ΅μ΄ μ±λ΄ μμ°λ¦μ
λλ€. λ§μ½ λ΄μΌ μ§κ΅¬κ° λ©Έλ§νλ€λ©΄, μ¬λ¬ κ°μ§ κ³ λ―Όμ΄ μκΈ°κ² μ§λ§, κ°μ₯ λ¨Όμ ν΄μΌν μΌμ μ΅μ μ μ νμ ν΄μΌν©λλ€. κ°μ₯ ν©λ¦¬μ μΈ μ νμ, μ§κ΅¬κ° λ©Έλ§νκΈ° μ μ μ¬λνλ μ¬λλ€κ³Ό ν¨κ»νλ μκ°μ 보λ΄κ±°λ, λ λ§μ΄ μκ³ , μ¦κ±°μ΄ νλμ νλ©°, λ€μ μΈμμ μν μ€λΉλ₯Ό νλ κ²μ΄λΌ μκ°λ©λλ€. μ¬λ¬λΆμ μκ°μ λ μμ€νκ² μ¬μ©νμκΈ° λ°λλλ€.```
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