1. Model Description
- KONI (KISTI Open Natural Intelligence) is a specialized large language model (LLM) developed by the Korea Institute of Science and Technology Information (KISTI). This model is specifically designed for science and technology, making it highly effective for tasks in these fields.
2. Key Features
- Specialized in Science and Technology: The model is explicitly trained on a vast and specialized corpus of scientific and technological data.
- Enhanced Performance: This version of KONI shows significantly improved performance compared to its initial release in December, 2023.
- Base Model: The base model for KONI-Llama3.1-70B-Instruct is Meta-Llama-3.1-70B-Instruct.
- Alignment: SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) are applied
3. Data
- Approximately 11k SFT data and 7k DPO data are used.
- SFT Data: The SFT data includes both internally generated data and publicly available data on Hugging Face, translated into Korean where necessary.
- DPO Data: The DPO data consists of translated and curated data from argilla/dpo-mix-7k.
4. Benchmark Results
The performances were evaluated using the LogicKor benchmark dataset as follows:
Metric | Score |
---|---|
Reasoning | 9.07 |
Math | 9.65 |
Writing | 9.50 |
Coding | 9.65 |
Comprehension | 9.86 |
Grammar | 8.57 |
Single-turn | 9.48 |
Multi-turn | 9.29 |
Overall | 9.38 |
5. How to use the model
import transformers
import torch
model_id = "KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
pipeline.model.eval()
instruction = "์๋
? ๋๋ ๋๊ตฌ์ผ?"
messages = [
{"role": "user", "content": f"{instruction}"}
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(outputs[0]["generated_text"][len(prompt):])
์๋
ํ์ธ์! ์ ๋ KISTI์ KONI์
๋๋ค. ๊ณผํ๊ธฐ์ ๋ฐ์ดํฐ๋ฅผ ์ ๋ฌธ์ผ๋ก ์ฒ๋ฆฌํ๋ฉฐ, ์ฌ๋ฌ๋ถ์ ์ฐ๊ตฌ์ ์ง๋ฌธ์ ์ต์ ์ ๋คํด ๋์์ ๋๋ฆฌ๊ฒ ์ต๋๋ค. ๋ฌด์์ ๋์๋๋ฆด๊น์?
6. Citation
Language Model
@article{KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview,
title={KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview},
author={KISTI},
year={2024},
url={https://huggingface.co/KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview}
}
7. Contributors
- KISTI, Large-scale AI Research Group
8. Special Thanks
8. Acknowledgement
- This research was supported by Korea Institute of Science and Technology Information(KISTI).
- This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KISTI).
9. References
- Downloads last month
- 65