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README.md

@@ -32,35 +32,41 @@ dtype: float16
 
 ## Model Details
 
-The merged model combines the conversational question answering capabilities of Llama3-ChatQA-1.5-8B with the bilingual proficiency of Llama3-8B-Chinese-Chat. The former excels in retrieval-augmented generation (RAG) and conversational QA, while the latter is fine-tuned for Chinese and English interactions, enhancing its role-playing and tool-using abilities. This fusion aims to create a model that can effectively handle diverse queries in both languages, making it suitable for a wider audience.
+The merged model combines the conversational question answering capabilities of Llama3-ChatQA-1.5 with the bilingual proficiency of Llama3-8B-Chinese-Chat. Llama3-ChatQA-1.5 is designed for conversational QA and retrieval-augmented generation, leveraging a rich dataset to enhance its performance in understanding and generating contextually relevant responses. On the other hand, Llama3-8B-Chinese-Chat is fine-tuned specifically for Chinese and English users, excelling in tasks such as roleplaying and tool usage.
+
+## Description
+
+This model aims to provide a seamless experience for users who require both English and Chinese language support in conversational contexts. By merging these two models, we enhance the ability to handle diverse queries, allowing for more nuanced and context-aware interactions. The model is particularly useful for applications that require bilingual capabilities, such as customer support, educational tools, and interactive chatbots.
 
 ## Merge Hypothesis
 
-The hypothesis behind this merge is that by combining the strengths of both models, we can achieve a more comprehensive understanding of context and improve the model's ability to generate nuanced responses in both English and Chinese. The linear merging approach allows for a balanced integration of the two models' capabilities.
+The hypothesis behind this merge is that combining the strengths of both models will yield a more capable and flexible language model. Llama3-ChatQA-1.5's proficiency in conversational QA complements Llama3-8B-Chinese-Chat's bilingual capabilities, resulting in a model that can effectively engage users in both languages while maintaining high-quality responses.
 
 ## Use Cases
 
-- **Conversational AI**: Engaging users in natural dialogues in both English and Chinese.
-- **Question Answering**: Providing accurate answers to user queries across various topics.
-- **Language Learning**: Assisting users in learning and practicing both English and Chinese through interactive conversations.
-- **Content Generation**: Generating creative content, such as stories or poems, in either language.
+- **Bilingual Customer Support**: Providing assistance in both English and Chinese, catering to a wider audience.
+- **Educational Tools**: Assisting learners in understanding concepts in their preferred language.
+- **Interactive Chatbots**: Engaging users in natural conversations across different languages.
 
 ## Model Features
 
-This merged model benefits from:
-- Enhanced conversational capabilities, allowing for more engaging interactions.
-- Bilingual proficiency, enabling effective communication in both English and Chinese.
-- Improved context understanding, leading to more relevant and accurate responses.
+- **Conversational QA**: Enhanced ability to answer questions in a conversational manner.
+- **Bilingual Proficiency**: Supports both English and Chinese, making it suitable for diverse user bases.
+- **Contextual Understanding**: Improved performance in understanding and generating contextually relevant responses.
 
 ## Evaluation Results
 
-The evaluation results of the parent models indicate strong performance in their respective tasks. For instance, Llama3-ChatQA-1.5-8B has shown impressive results in the ChatRAG Bench, outperforming many existing models in conversational QA tasks. Meanwhile, Llama3-8B-Chinese-Chat has demonstrated superior performance in Chinese language tasks, surpassing ChatGPT in various benchmarks.
+The evaluation results of the parent models indicate strong performance in their respective tasks. For instance, Llama3-ChatQA-1.5 achieved notable scores in various benchmarks, such as:
 
-## Limitations of Merged Model
+| Benchmark | Score |
+|-----------|-------|
+| Doc2Dial  | 41.26 |
+| QuAC      | 38.82 |
+| CoQA      | 78.44 |
+| Average (all) | 58.25 |
 
-While the merged model offers significant advantages, it may also inherit some limitations from its parent models. Potential issues include:
-- **Biases**: Any biases present in the training data of the parent models may be reflected in the merged model's outputs.
-- **Performance Variability**: The model's performance may vary depending on the language used, with potential weaknesses in less common queries or topics.
-- **Contextual Limitations**: Although the model is designed to handle bilingual interactions, it may still struggle with highly context-dependent queries that require deep cultural understanding.
+Llama3-8B-Chinese-Chat has also shown significant improvements in its capabilities, particularly in roleplay and function calling tasks, as evidenced by its performance in C-Eval and CMMLU benchmarks.
+
+## Limitations of Merged Model
 
-This model represents a step forward in creating a more inclusive and capable conversational AI, but users should remain aware of its limitations and use it accordingly.
+While the merged model benefits from the strengths of both parent models, it may also inherit some limitations. Potential biases present in the training data of either model could affect the responses generated. Additionally, the model may struggle with highly specialized queries or contexts that require deep domain knowledge beyond its training scope. Users should be aware of these limitations when deploying the model in real-world applications.