---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
- code
---
# Magic-Dolphin-7b
<img src="https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b/resolve/main/magic-dolphin.jfif" width="500"/>


A linear merge of:
- [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
- [Locutusque/Hyperion-1.5-Mistral-7B](https://huggingface.co/Locutusque/Hyperion-1.5-Mistral-7B)
- [ibm/merlinite-7b](https://huggingface.co/ibm/merlinite-7b)


These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO.



This was my first experiment with merging models so any feedback is greatly appreciated.

Uses Alpaca template.

<p align="center">

</p>

<b>Sample Question</b>
<img src="https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b/resolve/main/magic-dolphin.JPG" width="750"/>

## Merge Details
### Merge Method

This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.

### Models Merged

The following models were included in the merge:
* models/Hyperion-1.5-Mistral-7B
* models/dolphin-2.6-mistral-7b-dpo-laser
* models/merlinite-7b

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: models/dolphin-2.6-mistral-7b-dpo-laser
    parameters:
      weight: 1.0
  - model: models/Hyperion-1.5-Mistral-7B
    parameters:
      weight: 0.3
  - model: models/merlinite-7b
    parameters:
      weight: 0.5
merge_method: linear
dtype: float16

```