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EEVE-Korean-Instruct-10.8B-v1.0 - GGUF
- Model creator: https://huggingface.co/yanolja/
- Original model: https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0/
Original model description:
license: apache-2.0 tags: - generated_from_trainer base_model: yanolja/EEVE-Korean-10.8B-v1.0 model-index: - name: yanolja/EEVE-Korean-Instruct-10.8B-v1.0 results: []
EEVE-Korean-Instruct-10.8B-v1.0
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Our Dedicated Team (Alphabetical Order)
Research | Engineering | Product Management | UX Design |
---|---|---|---|
Myeongho Jeong | Geon Kim | Bokyung Huh | Eunsue Choi |
Seungduk Kim | Rifqi Alfi | ||
Seungtaek Choi | Sanghoon Han | ||
Suhyun Kang |
About the Model
This model is a fine-tuned version of yanolja/EEVE-Korean-10.8B-v1.0, which is a Korean vocabulary-extended version of upstage/SOLAR-10.7B-v1.0. Specifically, we utilized Direct Preference Optimization (DPO) through the use of Axolotl.
For more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.
Prompt Template
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {prompt}
Assistant:
How to Use it
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0")
tokenizer = AutoTokenizer.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0")
prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = 'νκ΅μ μλλ μ΄λμΈκ°μ? μλ μ νμ§ μ€ κ³¨λΌμ£ΌμΈμ.\n\n(A) κ²½μ±\n(B) λΆμ°\n(C) νμ\n(D) μμΈ\n(E) μ μ£Ό'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')
outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)
Example Output
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: νκ΅μ μλλ μ΄λμΈκ°μ? μλ μ νμ§ μ€ κ³¨λΌμ£ΌμΈμ.
(A) κ²½μ±
(B) λΆμ°
(C) νμ
(D) μμΈ
(E) μ μ£Ό
Assistant:
(D) μμΈμ΄ νκ΅μ μλμ
λλ€. μμΈμ λλΌμ λΆλλΆμ μμΉν΄ μμΌλ©°, μ μΉ, κ²½μ , λ¬Ένμ μ€μ¬μ§μ
λλ€. μ½ 1,000λ§ λͺ
μ΄ λλ μΈκ΅¬λ₯Ό κ°μ§ μΈκ³μμ κ°μ₯ ν° λμ μ€ νλμ
λλ€. μμΈμ λμ λΉλ©, νλμ μΈ μΈνλΌ, νκΈ° λ¬Έν μ₯λ©΄μΌλ‘ μ λͺ
ν©λλ€. λν, λ§μ μμ¬μ λͺ
μμ λ°λ¬Όκ΄μ΄ μμ΄ λ°©λ¬Έκ°λ€μκ² νλΆν λ¬Έν 체νμ μ 곡ν©λλ€.
Training Data
- Korean-translated version of Open-Orca/SlimOrca-Dedup
- Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned
- No other dataset was used
Citation
@misc{kim2024efficient,
title={Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models},
author={Seungduk Kim and Seungtaek Choi and Myeongho Jeong},
year={2024},
eprint={2402.14714},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{cui2023ultrafeedback,
title={UltraFeedback: Boosting Language Models with High-quality Feedback},
author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
year={2023},
eprint={2310.01377},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{SlimOrcaDedup,
title = {SlimOrca Dedup: A Deduplicated Subset of SlimOrca},
author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium" and Nathan Hoos},
year = {2023},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup/}
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 66.48 |
AI2 Reasoning Challenge (25-Shot) | 64.85 |
HellaSwag (10-Shot) | 83.04 |
MMLU (5-Shot) | 64.23 |
TruthfulQA (0-shot) | 54.09 |
Winogrande (5-shot) | 81.93 |
GSM8k (5-shot) | 50.72 |
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