Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language:
 - en
+- ko
 license: llama3.1
 tags:
 - llama-3.1
@@ -10,11 +11,11 @@ base_model:
 - meta-llama/Meta-Llama-3.1-8B-Instruct
 ---
 
-## Llama-3.1-Varco-8B-Instruct
+## Llama-VARCO-8B-Instruct
 
 ### About the Model
 
-**Llama-3.1-Varco-8B-Instruct** is a *generative model* based on Meta-Llama-3.1-8B, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.
+**Llama-VARCO-8B-Instruct** is a *generative model* based on Meta-Llama-3.1-8B, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.
 
 - **Developed by:** NC Research, Language Model Team
 - **Languages (NLP):** Korean, English
@@ -32,11 +33,11 @@ We recommend to use transformers v4.43.0 or later, as advised for Llama-3.1.
   import torch
 
   model = AutoModelForCausalLM.from_pretrained(
-      "NCSOFT/Llama-3.1-Varco-8B-Instruct",
+      "NCSOFT/Llama-VARCO-8B-Instruct",
       torch_dtype=torch.bfloat16,
       device_map="auto"
   )
-  tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-3.1-Varco-8B-Instruct")
+  tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-VARCO-8B-Instruct")
 
   messages = [
       {"role": "system", "content": "You are a helpful assistant Varco. Respond accurately and diligently according to the user's instructions."},
@@ -67,10 +68,8 @@ We used the [LogicKor](https://github.com/instructkr/LogicKor) code to measure p
 
 | Model        |  Math  |  Reasoning  |  Writing  |  Coding  |  Understanding  |  Grammer  |  Single turn  |  Multi turn  |  Overall  |
 |--------------|--------|-------------|-----------|----------|-----------------|-----------|---------------|--------------|-----------|
-| [Llama-3.1-Varco-8B-Instruct](https://huggingface.co/NCSOFT/Llama-3.1-Varco-8B-Instruct)| 6.71 / 8.57 | 8.86 / 8.29 | 9.86 / 9.71 | 8.86 / 9.29 | 9.29 / 10.0 | 8.57 / 7.86 | 8.69 | 8.95 | 8.82 |
+| [Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct)| 6.71 / 8.57 | 8.86 / 8.29 | 9.86 / 9.71 | 8.86 / 9.29 | 9.29 / 10.0 | 8.57 / 7.86 | 8.69 | 8.95 | 8.82 |
 | [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)| 6.86 / 7.71 | 8.57 / 6.71 | 10.0 / 9.29 | 9.43 / 10.0 | 10.0 / 10.0 | 9.57 / 5.14 | 9.07 | 8.14 | 8.61 |
 | [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)| 4.29 / 4.86  | 6.43 / 6.57 | 6.71 / 5.14 | 6.57 / 6.00 | 4.29 / 4.14 | 6.00 / 4.00 | 5.71 | 5.12 | 5.42 |
 | [Gemma-2-9B-Instruct](https://huggingface.co/google/gemma-2-9b-it)| 6.14 / 5.86 | 9.29 / 9.0 | 9.29 / 8.57 | 9.29 / 9.14 | 8.43 / 8.43 | 7.86 / 4.43 | 8.38 | 7.57 | 7.98
-| [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)| 5.57 / 4.86 | 7.71 / 6.43 | 7.43 / 7.00 | 7.43 / 8.00 | 7.86 / 8.71 | 6.29 / 3.29 | 7.05 | 6.38 | 6.71 |
-
-
+| [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)| 5.57 / 4.86 | 7.71 / 6.43 | 7.43 / 7.00 | 7.43 / 8.00 | 7.86 / 8.71 | 6.29 / 3.29 | 7.05 | 6.38 | 6.71 |