Hermes-2-Pro-Llama-3-8B-Llama3-8B-Chinese-Chat-slerp-merge

Hermes-2-Pro-Llama-3-8B-Llama3-8B-Chinese-Chat-slerp-merge is a sophisticated language model resulting from the strategic merging of two distinct models: NousResearch/Hermes-2-Pro-Llama-3-8B and shenzhi-wang/Llama3-8B-Chinese-Chat. The merging process was executed using mergekit, a specialized tool designed for precise model blending to achieve optimal performance and synergy between the merged architectures.

🧩 Merge Configuration

slices:
  - sources:
      - model: NousResearch/Hermes-2-Pro-Llama-3-8B
        layer_range: [0, 31]
      - model: shenzhi-wang/Llama3-8B-Chinese-Chat
        layer_range: [0, 31]
merge_method: slerp
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

Model Features

This merged model combines the advanced generative capabilities of NousResearch/Hermes-2-Pro-Llama-3-8B, which excels in function calling and structured outputs, with the robust performance of shenzhi-wang/Llama3-8B-Chinese-Chat, which is fine-tuned for Chinese and English interactions. The result is a versatile model that supports a wide range of text generation tasks, including conversational AI, structured data outputs, and multilingual capabilities.

Use Cases

• Conversational AI: Engage in natural dialogues in both English and Chinese, leveraging the strengths of both parent models.
• Function Calling: Utilize advanced function calling capabilities for structured outputs, making it suitable for applications requiring precise data handling.
• Multilingual Support: Effectively communicate in both English and Chinese, catering to a diverse user base.

Evaluation Results

Hermes-2-Pro-Llama-3-8B

• Function Calling Evaluation: 90%
• JSON Structured Outputs Evaluation: 84%

Llama3-8B-Chinese-Chat

• Enhanced performance in roleplay, function calling, and math capabilities, particularly in the latest version.

Limitations

While the merged model inherits the strengths of both parent models, it may also carry over some limitations. For instance, the model's performance in highly specialized domains may not match that of dedicated models. Additionally, biases present in the training data of either parent model could influence the outputs, necessitating careful consideration in sensitive applications.

In summary, Hermes-2-Pro-Llama-3-8B-Llama3-8B-Chinese-Chat-slerp-merge represents a significant advancement in language modeling, combining the best features of its predecessors to deliver a powerful tool for a variety of applications.