Update README.md

README.md

@@ -292,11 +292,12 @@ We initialized a fp16-precision ChatMusician-Base from the LLaMA2-7B-Base weight
 was 2048. We utilized 16 80GB-A800 GPUs for one epoch pre-training and 8 32GB-V100 GPUs for two epoch fine-tuning. DeepSpeed was employed for memory efficiency, and the AdamW optimizer was used with a 1e-4 learning rate and a 5% warmup cosine scheduler. Gradient clipping was set at 1.0. The LoRA parameters dimension, alpha, and
 dropout were set to 64, 16, and 0.1, with a batch size of 8.
 
+
 ## Evaluation
 
 1. Music understanding abilities are evaluated on the [MusicTheoryBench](https://huggingface.co/datasets/m-a-p/MusicTheoryBench). The following figure is zero-shot accuracy on MusicTheoryBench.
 We included GPT-3.5, GPT-4, LLaMA2-7B-Base, ChatMusician-Base, and ChatMusician. The blue bar represents the performance on the music knowledge metric, and the red bar represents the music reasoning metric. The dashed line corresponds to a random baseline, with a score of 25%.![MusicTheoryBench_result](./MusicTheoryBench_result_plt.png)
-3. General language abilities of ChatMusician are evaluated  on the [Massive Multitask Language Understanding (MMLU) dataset](https://huggingface.co/datasets/lukaemon/mmlu).
+2. General language abilities of ChatMusician are evaluated  on the [Massive Multitask Language Understanding (MMLU) dataset](https://huggingface.co/datasets/lukaemon/mmlu).
 
 
 ## Usage