Update README.md

README.md

@@ -32,14 +32,19 @@ Training required approximately 20.6GB of VRAM without any quantization (default
 # Evaluate Results
 ## CMMLU
 <img src="./pics/cmmlu.png" alt="image_name png"/>
+
 ## C-eval
 <img src="./pics/ceval.png" alt="image_name png"/>
+
 ## TC-eval by MediaTek Research
 <img src="./pics/tc-eval.png" alt="image_name png"/>
+
 ## MT-bench
 <img src="./pics/dashB.png" alt="image_name png"/>
+
 ## LLM-eval by NTU Miu Lab
 <img src="./pics/llmeval.png" alt="image_name png"/>
+
 ## Bailong Bench
 
 
@@ -74,6 +79,7 @@ Please review his marvellous works!
 Download model
 
 Here is the example for you to download Hyacinth6B with huggingface transformers:
+
 ```
 from transformers import AutoTokenizer,AutoModelForCausalLM
 import torch
@@ -83,6 +89,7 @@ model = AutoModelForCausalLM.from_pretrained("chillymiao/Hyacinth6B")
 ```
 
 ### Citaion
+
 ```
 @misc{song2024hyacinth6b,
       title={Hyacinth6B: A large language model for Traditional Chinese}, 
@@ -92,4 +99,4 @@ model = AutoModelForCausalLM.from_pretrained("chillymiao/Hyacinth6B")
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```