Update README.md

README.md

@@ -9,7 +9,7 @@ language:
 base_model: beomi/Yi-Ko-6B
 ---
 
-# Yi-Ko-6B-Instruct-v1.1
+# Yi-Ko-6B-Instruct-v1.1_
 
 ## Model Details
 
@@ -20,18 +20,7 @@ base_model: beomi/Yi-Ko-6B
 1. [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3) 🙇
 2. [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) 🙇
 3. [maywell/ko_wikidata_QA](https://huggingface.co/datasets/maywell/ko_wikidata_QA) 🙇
-4. AIHub 데이터 선별 후 Instruction Format 맞게 변경 후 사용
-
-## Benchmark Results
-
-### AI-Harness Evaluation  
-https://github.com/Beomi/ko-lm-evaluation-harness 
-  
-| Model | kobest_boolq | kobest_copa | kobest_hellaswag | kobest_sentineg | korunsmile | pawsx_ko |  
-| --- | --- | --- | --- | --- | --- | --- |
-|  | *Zero-shot* ||||||
-| Yi-Ko-6B-Instruct-v1.1 | | | | | | |
-| Yi-Ko-6B | 0.7070 | 0.7696 | 0.5009 | 0.4044 | 0.3828 | 0.5145 |
+4. AIHub 데이터 활용
 
 ## Instruction Format
 ```python
@@ -47,9 +36,9 @@ https://github.com/Beomi/ko-lm-evaluation-harness
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("wkshin89/Yi-Ko-6B-Instruct-v1.1")
+tokenizer = AutoTokenizer.from_pretrained("wkshin89/Yi-Ko-6B-Instruct-v1.1_")
 model = AutoModelForCausalLM.from_pretrained(
-    "wkshin89/Yi-Ko-6B-Instruct-v1.1",
+    "wkshin89/Yi-Ko-6B-Instruct-v1.1_",
     device_map="auto",
     torch_dtype=torch.bfloat16,
 )