Update README and webpage

README.md

@@ -6,12 +6,12 @@ ChemDFM is the pioneering open-sourced dialogue foundation model for Chemistry a
 
 ## News
 
-* **2024-03-07**: The parameter of ChemLLM-13B is open-sourced!
+[//]: # (* **2024-03-07**: The parameter of ChemLLM-13B is open-sourced!)
 * **2024-01-26**: The paper of ChemLLM-13B is released on arXiv: [ChemDFM: Dialogue Foundation Model for Chemistry](https://arxiv.org/abs/2401.14818)
 
 ## Usage Details
 
-The online demo of ChemDFM will be up soon!
+The model parameters and online demo of ChemDFM will be up soon!
 
 ### local inference
 

docs/index.html

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-                Large language models (LLMs) have established great success in the general domain of natural language processing. Their emerging task generalization and free-form dialogue capabilities can greatly help to design <b>Chemical General Intelligence (CGI)</b> to assist real-world research in chemistry. However, the existence of specialized language and knowledge in the field of chemistry, such as the highly informative SMILES notation, hinders the performance of general-domain LLMs in chemistry. To this end, we develop <b>ChemDFM</b>, the first LLM towards CGI. ChemDFM-13B is trained on 34B tokens from chemical literature, textbooks, and instructions as well as various data from the general domain. <i>Therefore, it can store, understand, and reason over chemical knowledge and languages while still possessing advanced free-form language comprehension capabilities.</i> Extensive quantitative evaluation shows that ChemDFM can <i>significantly outperform the representative open-sourced LLMs</i>. Moreover, ChemDFM can also <i>surpass GPT-4 on a great portion of chemical tasks, despite the significant size difference</i>. Further qualitative evaluations demonstrate the efficiency and effectiveness of ChemDFM in real-world research scenarios.
+                Large language models (LLMs) have established great success in the general domain of natural language processing. Their emerging task generalization and free-form dialogue capabilities can greatly help to design <b>Chemical General Intelligence (CGI)</b> to assist real-world research in chemistry. However, the existence of specialized language and knowledge in the field of chemistry, such as the highly informative SMILES notation, hinders the performance of general-domain LLMs in chemistry. To this end, we develop <b>ChemDFM</b>, the pioneering LLM towards CGI. ChemDFM-13B is trained on 34B tokens from chemical literature, textbooks, and instructions as well as various data from the general domain. <i>Therefore, it can store, understand, and reason over chemical knowledge and languages while still possessing advanced free-form language comprehension capabilities.</i> Extensive quantitative evaluation shows that ChemDFM can <i>significantly outperform the representative open-sourced LLMs</i>. Moreover, ChemDFM can also <i>surpass GPT-4 on a great portion of chemical tasks, despite the significant size difference</i>. Further qualitative evaluations demonstrate the efficiency and effectiveness of ChemDFM in real-world research scenarios.
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