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index.html

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-  <meta name="description" content="TODO">
+  <meta name="description"
+    content="LlaSMol (large language models for small molecules) is a series of strong LLMs built for conducting various chemistry tasks.  LlaSMol is fine-tuned on SMolInstruct, an instruction dataset of 14 meticulously selected tasks.  LlaSMol models perform better than existing LLMs for chemistry.">
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-    content="Organic Chemistry, Small Molecules, Chemistry, Large Language Model, Large Multimodal Model, artificial intelligence, AI">
+    content="LLM, Organic Chemistry, Small Molecules, Chemistry, Large Language Model, Large Multimodal Model, artificial intelligence, AI, OSU">
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   <title> LlaSMol </title>
 
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           <!-- <h2 class="title is-3">Overview of SMolInstruct</h2> -->
           <div class="content has-text-justified">
             <p>
-              LlaSMol (<strong>l</strong>arge <strong>la</strong>nguage models for <strong>s</strong>mall <strong>mol</strong>ecules) are a series of LLMs built for conducting various chemistry tasks.
+              LlaSMol (<strong>l</strong>arge <strong>la</strong>nguage models for <strong>s</strong>mall <strong>mol</strong>ecules) is a series of LLMs built for conducting various chemistry tasks.
               Specifically, we use Galactica, Llama 2, Code Llama, and Mistral as the base models, and conduct instruction tuning with LoRA
               on our SMolInstruct dataset. The resulting models are named as <strong>LlaSMol<sub>Galactica</sub></strong>, 
               <strong>LlaSMol<sub>Llama 2</sub></strong>, <strong>LlaSMol<sub>Code Llama</sub></strong>, <strong>LlaSMol<sub>Mistral</sub></strong>,