README.md

---
base_model:
- mistralai/Mistral-7B-v0.1
- cognitivecomputations/dolphin-2.2.1-mistral-7b
- Open-Orca/Mistral-7B-OpenOrca
- openchat/openchat-3.5-0106
- mlabonne/NeuralHermes-2.5-Mistral-7B
- GreenNode/GreenNode-mini-7B-multilingual-v1olet
- berkeley-nest/Starling-LM-7B-alpha
- viethq188/LeoScorpius-7B-Chat-DPO
- meta-math/MetaMath-Mistral-7B
- Intel/neural-chat-7b-v3-3
library_name: transformers
inference: false
tags:
- mergekit
- merge
license: apache-2.0
---
# Moza-7B-v1.0

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63474d73511cd17d2c790ed7/e7hw2xIzfpUseCFEOINg7.png)

This is a [meme-merge](https://en.wikipedia.org/wiki/Joke) of pre-trained language models,
created using [mergekit](https://github.com/cg123/mergekit).
Use at your own risk.

## Details
### Quantized Model
- [GGUF](https://huggingface.co/kidyu/Moza-7B-v1.0-GGUF)

### Merge Method

This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method,
using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.

The value for `density` are from [this blogpost](https://huggingface.co/blog/mlabonne/merge-models),
and the weight was randomly generated and then assigned to the models,
with priority (of using the bigger weight) to `NeuralHermes`, `OpenOrca`, and `neural-chat`.
The models themselves are chosen by "vibes".

### Models Merged

The following models were included in the merge:
* [cognitivecomputations/dolphin-2.2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.2.1-mistral-7b)
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
* [GreenNode/GreenNode-mini-7B-multilingual-v1olet](https://huggingface.co/GreenNode/GreenNode-mini-7B-multilingual-v1olet)
* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
* [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO)
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)
* [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3)

### Prompt Format

You can use `Alpaca` formatting for inference

```
### Instruction:

### Response:
```

### Configuration

The following YAML configuration was used to produce this model:

```yaml
base_model: mistralai/Mistral-7B-v0.1
models:
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.83
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.63
      weight: 0.74
  - model: meta-math/MetaMath-Mistral-7B
    parameters:
      density: 0.63
      weight: 0.22
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.63
      weight: 0.37
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      density: 0.63
      weight: 0.76
  - model: cognitivecomputations/dolphin-2.2.1-mistral-7b
    parameters:
      density: 0.63
      weight: 0.69
  - model: viethq188/LeoScorpius-7B-Chat-DPO
    parameters:
      density: 0.63
      weight: 0.38
  - model: GreenNode/GreenNode-mini-7B-multilingual-v1olet
    parameters:
      density: 0.63
      weight: 0.13
  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      density: 0.63
      weight: 0.33
merge_method: dare_ties
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16
```