Update README.md

README.md

@@ -20,13 +20,13 @@ metrics:
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. scidpr-question-encoder is the Question Encoder trained using the Scientific Question Answer (QA) dataset (Pradeep et al., 2021).
+Mozi is the first large-scale language model for the scientific paper domain, such as question answering and emotional support. With the help of the large-scale language and evidence retrieval models, SciDPR, Mozi generates concise and accurate responses to users' questions about specific papers and provides emotional support for academic researchers.
 
 
 - **Developed by:** See [GitHub repo](https://github.com/gmftbyGMFTBY/science-llm) for model developers
 - **Model date:** LLaMA was trained In May. 2023.
 - **Model version:** This is version 1 of the model.
-- **Model type:** mozi_llama is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B parameters.
+- **Model type:** mozi is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B parameters.
 - **Language(s) (NLP):** [Apache 2.0](https://github.com/gmftbyGMFTBY/science-llm/blob/main/LICENSE)
 - **License:** English
 
@@ -34,5 +34,5 @@ Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [Girhub Repo](https://github.com/gmftbyGMFTBY/science-llm)
+- **Repository:** [Github Repo](https://github.com/gmftbyGMFTBY/science-llm)
 - **Paper [optional]:** [Paper Repo]()