---
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 dolphin-2.6-mistral-7b-dpo-laser, merlinite-7b, and Hyperion-1.5-Mistral-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.

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<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

```