---
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- LeroyDyer/Mixtral_AI_Cyber_3.0
- LeroyDyer/Mixtral_AI_MultiToken
- LeroyDyer/Mixtral_AI_Multi_TEST
library_name: transformers
tags:
- mergekit
- merge
- code
- art
- Cyber-Series
datasets:
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
license: apache-2.0
---
UNDER DEVELOPMENT

This model is being constantly retuned and updated ! (these updates may not be reflected in the current gguf!)

This is a highly focused model which is dedicated to producing code and functions and applications. 
It has been erged with the top models of this repo and will be fine tuned on datasets dedicated to coding problems and other code related tasks. such as uml diagrams and object oriented planning etc.

# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [LeroyDyer/Mixtral_AI_Cyber_3.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_3.0) as a base.

### Models Merged

The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [LeroyDyer/Mixtral_AI_MultiToken](https://huggingface.co/LeroyDyer/Mixtral_AI_MultiToken)
* [LeroyDyer/Mixtral_AI_Multi_TEST](https://huggingface.co/LeroyDyer/Mixtral_AI_Multi_TEST)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: LeroyDyer/Mixtral_AI_Multi_TEST
    parameters:
      density: [0.87, 0.721, 0.451] # density gradient
      weight: 0.876
  - model: LeroyDyer/Mixtral_AI_MultiToken
    parameters:
      density: 0.232
      weight: [0.36, 0.3, 0.437, 0.76] # weight gradient
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      density: 0.475
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: LeroyDyer/Mixtral_AI_Cyber_3.0
parameters:
  normalize: true
  int8_mask: true
dtype: float16

```