gguf merge mergekit lazymergekit nvidia/Llama3-ChatQA-1.5-8B shenzhi-wang/Llama3-8B-Chinese-Chat license:apache-2.0 endpoints_compatible region:us conversational
---
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.

## Description

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 environments and complex question-answering scenarios.

## Merge Hypothesis

The hypothesis behind this merge is that combining the strengths of a model optimized for conversational QA with one that excels in bilingual interactions will yield a model capable of understanding and generating responses in both languages effectively. This is particularly useful in applications where users switch between languages or require context-aware responses.

## Use Cases

- **Multilingual Customer Support**: Providing assistance in both English and Chinese for customer inquiries.
- **Educational Tools**: Assisting learners in practicing language skills through interactive conversations.
- **Content Generation**: Creating bilingual content for blogs, articles, or social media posts.

## Model Features

- **Bilingual Proficiency**: Capable of understanding and generating text in both English and Chinese.
- **Conversational QA**: Enhanced ability to answer questions based on context, making it suitable for interactive applications.
- **Role-Playing and Tool-Using**: Supports complex interactions that require understanding user intent and context.

## Evaluation Results

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 generating coherent and contextually relevant responses in Chinese. 

| Model | Average Score (ChatRAG Bench) |
|---
# Quantized GGUF model Llama3-ChatQA-1.5-8B-Llama3-8B-Chinese-Chat-linear-merge
This model has been quantized using llama-quantize from [llama.cpp](https://github.com/ggerganov/llama.cpp)
    ----|-------------------------------|
| Llama3-ChatQA-1.5-8B | 55.17 |
| Llama3-8B-Chinese-Chat | Not explicitly provided, but noted for surpassing ChatGPT in performance. |

## Limitations of Merged Model

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. 

Overall, this merged model aims to provide a more comprehensive solution for users requiring bilingual conversational capabilities, while also addressing the challenges of context and nuance in language understanding.