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README.md

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 ## Model Details
 
-The Llama3-ChatQA-1.5 model excels in conversational question answering (QA) and retrieval-augmented generation (RAG). It is built on an improved training recipe from the [ChatQA paper](https://arxiv.org/pdf/2401.10225) and incorporates extensive conversational QA data to enhance its capabilities in tabular and arithmetic calculations. The model is designed to provide detailed and contextually relevant responses, making it suitable for a variety of applications.
+The merged model combines the strengths of Llama3-ChatQA-1.5, which excels in conversational question answering and retrieval-augmented generation, with Llama3-8B-Chinese-Chat, a model fine-tuned for Chinese and English users. This fusion enhances the model's ability to handle diverse language tasks, making it suitable for both English and Chinese conversational contexts.
 
-On the other hand, Llama3-8B-Chinese-Chat is specifically fine-tuned for Chinese and English users, showcasing remarkable performance in roleplaying, function calling, and math capabilities. It has been trained on a mixed dataset of approximately 100K preference pairs, significantly improving its ability to handle bilingual interactions.
+## Description
+
+Llama3-ChatQA-1.5-8B is designed to provide robust conversational capabilities, leveraging an improved training recipe that incorporates extensive conversational QA data. This model is particularly adept at arithmetic calculations and tabular data interpretation. On the other hand, Llama3-8B-Chinese-Chat is fine-tuned on a large dataset of Chinese-English preference pairs, significantly improving its performance in Chinese language tasks.
+
+## Merge Hypothesis and Justification
+
+The hypothesis behind this merge is that by combining the conversational strengths of Llama3-ChatQA-1.5 with the bilingual capabilities of Llama3-8B-Chinese-Chat, the resulting model would be more versatile and effective in handling a wider range of queries in both English and Chinese. This strategic blend aims to create a model that not only excels in QA tasks but also provides nuanced responses in both languages.
 
 ## Use Cases
 
-- **Conversational AI**: Engage users in natural dialogues, providing informative and context-aware responses.
-- **Question Answering**: Answer user queries accurately, leveraging the strengths of both English and Chinese language processing.
-- **Multilingual Support**: Cater to users who communicate in both English and Chinese, enhancing accessibility and user experience.
-- **Educational Tools**: Assist in learning and understanding complex topics through interactive Q&A sessions.
+- **Conversational AI**: Engage users in natural dialogues in both English and Chinese.
+- **Question Answering**: Provide accurate answers to user queries based on context.
+- **Multilingual Support**: Serve users who switch between English and Chinese seamlessly.
+- **Educational Tools**: Assist in language learning by providing contextually relevant examples and explanations.
 
 ## Model Features
 
-This merged model combines the robust generative capabilities of Llama3-ChatQA-1.5 with the refined tuning of Llama3-8B-Chinese-Chat. It offers:
-- Enhanced context understanding for both English and Chinese queries.
-- Improved performance in conversational QA tasks.
-- Versatile text generation capabilities across different languages.
+- **Bilingual Capabilities**: Proficient in both English and Chinese, making it suitable for diverse user bases.
+- **Enhanced Context Understanding**: Improved ability to understand and generate contextually relevant responses.
+- **Robust Performance**: Combines the strengths of both parent models to deliver high-quality outputs across various tasks.
 
 ## Evaluation Results
 
-The evaluation results of the parent models indicate strong performance in various benchmarks. For instance, Llama3-ChatQA-1.5 achieved notable scores in the ChatRAG Bench, demonstrating its effectiveness in conversational QA tasks. Meanwhile, Llama3-8B-Chinese-Chat has shown superior performance in Chinese language tasks, surpassing ChatGPT and matching GPT-4 in certain evaluations.
+The evaluation results of the input models indicate strong performance in conversational QA tasks. For instance, Llama3-ChatQA-1.5-8B achieved notable scores in benchmarks such as Doc2Dial and QuAC, while Llama3-8B-Chinese-Chat demonstrated superior performance in Chinese language tasks, surpassing previous models in various metrics.
 
-| Benchmark | Llama3-ChatQA-1.5-8B | Llama3-8B-Chinese-Chat |
-|-----------|-----------------------|-------------------------|
-| Doc2Dial  | 41.26                 | N/A                     |
-| QuAC      | 38.82                 | N/A                     |
-| CoQA      | 78.44                 | N/A                     |
-| Average    | 58.25                 | N/A                     |
+## Limitations of Merged Model
 
-## Limitations
+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 both models could affect the quality of responses, particularly in nuanced or culturally specific contexts. Additionally, the model's performance may vary depending on the complexity of the queries and the languages used.
 
-While the merged model benefits from the strengths of both parent models, it may also inherit some limitations. For instance, biases present in the training data of either model could affect the responses generated. Additionally, the model may struggle with highly specialized or niche topics that were not well-represented in the training datasets. Users should be aware of these potential biases and limitations when deploying the model in real-world applications.
+In summary, Llama3-ChatQA-1.5-8B-Llama3-8B-Chinese-Chat-linear-merge represents a significant advancement in multilingual conversational AI, offering enhanced capabilities for users across different languages and contexts.