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---
base_model:
- LeroyDyer/Mixtral_AI_Multi_TEST
- LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
- LeroyDyer/Mixtral_AI_CyberLAW
- LeroyDyer/Mixtral_AI_CyberBrain_3_0
- LeroyDyer/Mixtral_AI_Cyber_5.0
- LeroyDyer/Mixtral_AI_CyberBrain_2.0
- ezelikman/quietstar-8-ahead
- KoboldAI/Mistral-7B-Erebus-v3
library_name: transformers
tags:
- mergekit
- megamerge
- code
- Cyber-Series
license: mit
language:
- en
datasets:
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin
- WhiteRabbitNeo/WRN-Chapter-2
- WhiteRabbitNeo/WRN-Chapter-1
- gate369/Alpaca-Star
- gate369/alpaca-star-ascii
---

<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
https://github.com/spydaz

Currently undegoing Fine tuning ! as this model contains all Previous models !


This model contains many hidden tensors : 
As it was emrged with many lora adapter for various task such as vision and sound . 
The problem was that for some reason i could not get the extra heads to show up like other models.
such as the llava model ... i suppose this model can change the config.json to be a llava model and yes ! it works! ie it can think and has hidden think heads ? but you need to config it up !, It has vision heads but also i could not set the config up !
so hidden talents: 
It was also merged with the mothers of these models for QUiet(thoughts) and (llava vision etc ) so the tensors are there . i just did not understand how to fine tne the addtional funcitonalitys. as they need a single trainign example to populate the hidden tensor hence te merges. and yet when the model is put in train mode , ie by setting the model after loading to model.TRAIN ... the tensors apear waiting for training so just add a peft and start the training!


THIS VERSION HAS BEEN UPDATED TO INCLUDE CYBERBRAIN ! (Hidden Tensors)

## Extended capabilities:
  * mistralai/Mistral-7B-Instruct-v0.1 - Prime-Base

  * ChaoticNeutrals/Eris-LelantaclesV2-7b - role play
 
  * ChaoticNeutrals/Eris_PrimeV3-Vision-7B - vision

  * rvv-karma/BASH-Coder-Mistral-7B - coding

  * Locutusque/Hercules-3.1-Mistral-7B - Unhinging

  * KoboldAI/Mistral-7B-Erebus-v3 - NSFW

  * Locutusque/Hyperion-2.1-Mistral-7B - CHAT

  * Severian/Nexus-IKM-Mistral-7B-Pytorch - Thinking

  * NousResearch/Hermes-2-Pro-Mistral-7B - Generalizing
 
  * mistralai/Mistral-7B-Instruct-v0.2 - BASE

  * Nitral-AI/ProdigyXBioMistral_7B - medical

  * Nitral-AI/Infinite-Mika-7b - 128k - Context Expansion enforcement

  * Nous-Yarn-Mistral-7b-128k - 128k - Context Expansion
 
  * yanismiraoui/Yarn-Mistral-7b-128k-sharded

  * ChaoticNeutrals/Eris_Prime-V2-7B - Roleplay


This Expert is a companon to the MEGA_MIND 24b CyberSeries represents a groundbreaking leap in the realm of language models, integrating a diverse array of expert models into a unified framework. At its core lies the Mistral-7B-Instruct-v0.2, a refined instructional model designed for versatility and efficiency.

Enhanced with an expanded context window and advanced routing mechanisms, the Mistral-7B-Instruct-v0.2 exemplifies the power of Mixture of Experts, allowing seamless integration of specialized sub-models. This architecture facilitates unparalleled performance and scalability, enabling the CyberSeries to tackle a myriad of tasks with unparalleled speed and accuracy.

Among its illustrious sub-models, the OpenOrca - Mistral-7B-8k shines as a testament to fine-tuning excellence, boasting top-ranking performance in its class. Meanwhile, the Hermes 2 Pro introduces cutting-edge capabilities such as Function Calling and JSON Mode, catering to diverse application needs.

Driven by Reinforcement Learning from AI Feedback, the Starling-LM-7B-beta demonstrates remarkable adaptability and optimization, while the Phi-1.5 Transformer model stands as a beacon of excellence across various domains, from common sense reasoning to medical inference.

With models like BioMistral tailored specifically for medical applications and Nous-Yarn-Mistral-7b-128k excelling in handling long-context data, the MEGA_MIND 24b CyberSeries emerges as a transformative force in the landscape of language understanding and artificial intelligence.

Experience the future of language models with the MEGA_MIND 24b CyberSeries, where innovation meets performance, and possibilities are limitless.
### Models Merged

The following models were included in the merge:
* [LeroyDyer/Mixtral_AI_Multi_TEST](https://huggingface.co/LeroyDyer/Mixtral_AI_Multi_TEST)
* [LeroyDyer/Mixtral_AI_CyberLAW](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberLAW)
* [LeroyDyer/Mixtral_AI_CyberBrain_3_0](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_3_0)
* [LeroyDyer/Mixtral_AI_Cyber_5.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_5.0)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
    parameters:
      density: [0.256, 0.512, 0.128] # density gradient
      weight: 0.382
  - model: LeroyDyer/Mixtral_AI_CyberLAW
    parameters:
      density: 0.382
      weight: [0.256, 0.128, 0.256, 0.128] # weight gradient
  - model: LeroyDyer/Mixtral_AI_CyberBrain_3_0
    parameters:
      density: 0.382
      weight: [0.128, 0.512, 0.128, 0.128] # weight gradient
  - model: LeroyDyer/Mixtral_AI_Multi_TEST
    parameters:
      density: 0.382
      weight: [0.128, 0.512, 0.128, 0.128] # weight gradient
  - model: LeroyDyer/Mixtral_AI_Cyber_5.0
    parameters:
      density: 0.382
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model:  LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
parameters:
  normalize: true
  int8_mask: true
dtype: float16

```