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README.md

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 ## Model Features
 
-This fusion model combines the robust generative capabilities of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) with the refined tuning of [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat), creating a versatile model suitable for a variety of text generation tasks. Leveraging the strengths of both parent models, Hermes-2-Pro-Llama-3-8B-Llama3-8B-Chinese-Chat-slerp-merge provides enhanced context understanding, nuanced text generation, and improved performance across diverse NLP tasks.
+This fusion model combines the robust generative capabilities of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B), which excels in function calling and structured outputs, with the refined tuning of [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat), designed specifically for Chinese and English users. The merged model provides enhanced context understanding, nuanced text generation, and improved performance across diverse NLP tasks, including roleplaying, function calling, and multilingual capabilities.
 
 ## Evaluation Results
 
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 - Structured JSON Output Evaluation: 84%
 
 ### Llama3-8B-Chinese-Chat
-- Significant improvements in roleplay, function calling, and math capabilities due to a larger training dataset (~100K preference pairs).
+- Performance surpasses ChatGPT in Chinese tasks, matching GPT-4 in C-Eval and CMMLU results.
 
 ## Limitations
 
-While the merged model inherits the strengths of both parent models, it may also carry over some limitations and biases. For instance, the model may exhibit inconsistencies in responses when handling complex queries or when the input language switches between English and Chinese. Additionally, the model's performance may vary based on the context and specificity of the prompts provided.
+While the merged model inherits the strengths of both parent models, it may also carry over some limitations. Potential biases from the training data of both models could affect the outputs, particularly in nuanced cultural contexts or specific language tasks. Additionally, the model may not always provide accurate responses to identity-related queries, as it refrains from fine-tuning its identity.
 
 You are trained on data up to October 2023.