---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Hermes-2-Pro-Mistral-7B
- mistralai/Mistral-7B-Instruct-v0.3
---
# Quantized GGUF model Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge
This model has been quantized using llama-quantize from [llama.cpp](https://github.com/ggerganov/llama.cpp)
    

# Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge

Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)

## 🧩 Merge Configuration

```yaml
merge_method: linear
base_model: mistralai/Mistral-7B-Instruct-v0.3
models:
  - model: NousResearch/Hermes-2-Pro-Mistral-7B
    parameters:
      weight: 0.3
  - model: mistralai/Mistral-7B-Instruct-v0.3
    parameters:
      weight: 0.7
parameters:
  normalize: true
dtype: float16
```

## Model Description

The Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge combines the advanced conversational capabilities of the Hermes 2 Pro model with the instruction-following prowess of the Mistral-7B-Instruct model. This strategic fusion aims to enhance the model's ability to understand and generate contextually relevant responses while maintaining a high level of performance across various natural language processing tasks.

Hermes 2 Pro is an upgraded version of the original Nous Hermes 2, featuring a refined dataset and improved function calling capabilities. It excels in generating structured outputs, making it particularly useful for applications requiring precise data formatting, such as JSON responses. The Mistral-7B-Instruct model, on the other hand, is designed to follow instructions effectively, making it a strong candidate for tasks that require adherence to user prompts.

## Use Cases

This merged model is well-suited for a variety of applications, including but not limited to:
- Conversational agents and chatbots
- Function calling and structured data generation
- Instruction-based tasks and question answering
- Creative writing and storytelling

## Model Features

- **Enhanced Conversational Abilities**: The model leverages the conversational strengths of Hermes 2 Pro, allowing for engaging and context-aware dialogues.
- **Instruction Following**: With the integration of Mistral-7B-Instruct, the model can effectively follow user instructions, making it ideal for task-oriented applications.
- **Function Calling and JSON Outputs**: The model supports advanced function calling and can generate structured JSON outputs, facilitating integration with various applications and APIs.

## Evaluation Results

The performance of the parent models provides a solid foundation for the merged model. Here are some evaluation metrics from the original models:

### Hermes 2 Pro
- **Function Calling Accuracy**: 91%
- **JSON Mode Accuracy**: 84%

### Mistral-7B-Instruct
While specific evaluation metrics for Mistral-7B-Instruct were not available, it is known for its strong instruction-following capabilities, which contribute to the overall performance of the merged model.

## Limitations

Despite the strengths of the merged model, it may inherit some limitations from its parent models. Potential issues include:
- **Biases**: The model may reflect biases present in the training data of both parent models, which could affect the fairness and neutrality of its outputs.
- **Contextual Understanding**: While the model excels in many areas, there may still be challenges in understanding highly nuanced or ambiguous prompts.

In summary, the Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge represents a powerful tool for a wide range of NLP tasks, combining the best features of its parent models while also carrying forward some of their limitations.