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README.md

@@ -10,7 +10,7 @@ license: cc-by-nc-sa-4.0
 **(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**  
 **The license is `cc-by-nc-sa-4.0`.**  
 
-# **🐳Korean-OpenOrca-13B🐳**  
+# **🐳KOR-Orca-Platypus-13B🐳**  
 ![img](./Korean-OpenOrca.png)  
 
 ## Model Details
@@ -42,8 +42,8 @@ I use A100 GPU 40GB and COLAB, when trianing.
 
 | Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
 | --- | --- | --- | --- | --- | --- | --- |
-| [Korean-OpenOrca-13B] | 46.59 | 42.06 | 53.95 | 42.28 | 43.55 | 51.12 |  
-| Korean-OpenOrca-13B-v2 | 49.48 | 44.03 | 54.43 | 42.23 | 41.64 | 65.05 |  
+| [KOR-Orca-Platypus-13B🐳] | 46.59 | 42.06 | 53.95 | 42.28 | 43.55 | 51.12 |  
+| KOR-Orca-Platypus-13B🐳-v2 | 49.48 | 44.03 | 54.43 | 42.23 | 41.64 | 65.05 |  
 
 > Compare with Top 4 SOTA models. (update: 10/09)
 
@@ -54,7 +54,7 @@ I use A100 GPU 40GB and COLAB, when trianing.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-repo = "kyujinpy/Korean-OpenOrca-13B-v2"
+repo = "kyujinpy/KOR-Orca-Platypus-13B-v2"
 OpenOrca = AutoModelForCausalLM.from_pretrained(
         repo,
         return_dict=True,