---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- nvidia/Llama3-ChatQA-1.5-8B
- shenzhi-wang/Llama3-8B-Chinese-Chat
---

# Llama3-ChatQA-1.5-8B-Llama3-8B-Chinese-Chat-linear-merge

Llama3-ChatQA-1.5-8B-Llama3-8B-Chinese-Chat-linear-merge is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)
* [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat)

## 🧩 Merge Configuration

```yaml
models:
  - model: nvidia/Llama3-ChatQA-1.5-8B
    parameters:
      weight: 0.5
  - model: shenzhi-wang/Llama3-8B-Chinese-Chat
    parameters:
      weight: 0.5
merge_method: linear
parameters:
  normalize: true
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.

## 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.

## 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.

## 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.

## 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.

## Limitations of Merged Model

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.

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.