Update README.md

README.md

@@ -18,8 +18,22 @@ license: llama3
 - **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. How to use the model
+### 4. Benchmark Results
+Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows:
+
+| Metric         | Score |
+|----------------|-------|
+| Reasoning      | 6.57  |
+| Math           | 8.00  |
+| Writing        | 8.92  |
+| Comprehension  | 8.85  |
+| Grammar        | 9.85  |
+| Singleton      | 7.07  |
+| Multiturn      | 8.42  |
+| Total Score    | 8.00  |
+| **Overall**        | **8.21**  |
+
+### 5. How to use the model
 ```python
 import transformers
 import torch
@@ -82,7 +96,7 @@ KISTI는 대전 본원, 서울 분원, 대구·경북 지원, 부산울산경남
 KISTI에 대한 더 자세한 정보는 KISTI 공식 웹사이트에서 확인할 수 있습니다.
 ```
 
-### 5. Citation
+### 6. Citation
 **Language Model**
 ```text
 @article{KISTI-KONI/KONI-Llama3-8B-Instruct-20240729,
@@ -93,13 +107,17 @@ KISTI에 대한 더 자세한 정보는 KISTI 공식 웹사이트에서 확인
 }
 ```
   
-### 6. Contributors
+### 7. Contributors
 - KISTI, Large-scale AI Research Group
 
-### 7. Acknowledgement
+### 8. Special Thanks
+- [@beomi](https://huggingface.co/beomi)
+- [@kuotient](https://huggingface.co/kuotient)
+
+### 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).
 
-### 8. References
+### 9. References
 - https://huggingface.co/meta-llama/Meta-Llama-3-8B
 - https://huggingface.co/meta-llama/meta-llama/Meta-Llama-3-8B-Instruct