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README.md

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 ## Model Details
 
-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.
+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.
 
 ## 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.
+This model is designed to provide a seamless experience for users who require both English and Chinese language support in conversational contexts. By merging these two models, we aim to leverage the strengths of each, resulting in improved performance in multilingual settings and enhanced understanding of context in dialogues.
 
 ## Merge Hypothesis
 
-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.
+The hypothesis behind this merge is that combining the strengths of a model specialized in conversational QA with one that is adept in bilingual interactions will yield a model that can handle a wider range of queries and provide more nuanced responses in both languages.
 
 ## Use Cases
 
-- **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.
+- **Conversational Agents**: Ideal for applications requiring interactive dialogue in both English and Chinese.
+- **Customer Support**: Can be utilized in customer service platforms to assist users in their preferred language.
+- **Educational Tools**: Suitable for language learning applications that require conversational practice in both languages.
 
 ## Model Features
 
-- **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.
+- **Enhanced Context Understanding**: Improved ability to understand and generate contextually relevant responses.
+- **Role-Playing and Tool-Using**: Capable of engaging in role-play scenarios and utilizing external tools for enhanced interactivity.
 
 ## Evaluation Results
 
-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:
-
-| Benchmark | Score |
-|-----------|-------|
-| Doc2Dial  | 41.26 |
-| QuAC      | 38.82 |
-| CoQA      | 78.44 |
-| Average (all) | 58.25 |
-
-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.
+The evaluation results of the parent models indicate strong performance in their respective domains. For instance, Llama3-ChatQA-1.5-8B has shown significant improvements in conversational QA tasks, while Llama3-8B-Chinese-Chat has excelled in bilingual interactions, surpassing previous benchmarks in Chinese language tasks.
 
 ## Limitations of Merged Model
 
-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.
+While the merged model benefits from the strengths of both parent models, it may also inherit some limitations. Potential biases from the training data of each model could affect the responses, particularly in nuanced cultural contexts. Additionally, the model may still struggle with highly specialized queries that require deep domain knowledge.
+
+In summary, Llama3-ChatQA-1.5-8B-Llama3-8B-Chinese-Chat-linear-merge represents a significant step forward in creating a bilingual conversational agent, but users should remain aware of its limitations and biases.