Update README.md

README.md

@@ -40,9 +40,11 @@ This version of Cephalo, lamm-mit/Cephalo-Idefics-2-vision-10b-alpha, is based o
 
 The model was trained in several stages:
 
-Step 1: Train https://huggingface.co/lamm-mit/Cephalo-Idefics-2-vision-8b-beta by fine-tuning the HuggingFaceM4/idefics2-8b-chatty model.
-Step 2: Combine the https://huggingface.co/lamm-mit/Cephalo-Idefics-2-vision-8b-beta decoder with the last 8 layers of the HuggingFaceM4/idefics2-8b-chatty decoder. 
-Step 3: Fine-tune the merged model, which now has 40 decoder layers and a total of 10b parameters. 
+**Step 1**: Train https://huggingface.co/lamm-mit/Cephalo-Idefics-2-vision-8b-beta by fine-tuning the HuggingFaceM4/idefics2-8b-chatty model.
+
+**Step 2**: Combine the https://huggingface.co/lamm-mit/Cephalo-Idefics-2-vision-8b-beta decoder with the last 8 layers of the HuggingFaceM4/idefics2-8b-chatty decoder. 
+
+**Step 3**: Fine-tune the merged model, which now has 40 decoder layers and a total of 10b parameters. 
 
 The model was trained on a combination of scientific text-image data extracted from Wikipedia and scientific papers. For further details on the base model, see: https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty. More details about technical aspects of the model, training and example applications to materials science problems are provided in the paper (reference at the bottom).