transformers safetensors mistral text-generation mergekit merge conversational arxiv:2203.05482 base_model:LeroyDyer/LCARS_TOP_SCORE base_model:merge:LeroyDyer/LCARS_TOP_SCORE base_model:LeroyDyer/SpydazWeb_AI_HumanAGI_002 base_model:merge:LeroyDyer/SpydazWeb_AI_HumanAGI_002 doi:10.57967/hf/4494 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- LeroyDyer/SpydazWeb_AI_HumanAGI_002
- LeroyDyer/LCARS_TOP_SCORE
- LeroyDyer/_Spydaz_Web_AI_CheckPointsMerged
- LeroyDyer/_Spydaz_Web_AI_ReasoningCheckPointsMerged
library_name: transformers
tags:
- mergekit
- merge
---

# "Success comes from defining each task in achievable steps.
Every completed step is a success that brings you closer to your goal.

# Winners create more winners, while losers do the opposite.
Success is a game of winners.

— # Leroy Dyer (1972-Present)
<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>

# The Human AI .

# SpydazWeb AI (7b Mistral) (512k)

This model has been trained to perform with contexts of 512k , although in training it has been trained mainly with the 2048 for general usage : the long context aspect also allows fro advanced projects and sumarys as well as image and audio translationns and generations:

Highly trained as well as methodolgy oriented , this model has been trained on the reAct Prcess and other structured processes . hence structured outputs (json) are very highly trained as well as orchestration of other agents and tasks : the model has been trained for tools use as well as funtion use : as well as custom processes and tools : some tools do not need code either as thier implication means the model may even generate a tool or artifct to perfrom the task :



A New genrea of AI ! This is Trained to give highly detailed humanized responses : Performs tasks well, a Very good model for multipupose use : the model has been trained to become more human in its reposes as well as role playing and story telling : This latest model has been trained on Conversations with a desire to respond with expressive emotive content , As well as discussions on various topics: It has also been focused on conversations by human interactions. hence there maybe NFSW contet in the model : This has no way inhibited its other tasks which were also aligned using the new intensive and Expressive prompt :
## Thinking Humanly:

AI aims to model human thought, a goal of cognitive science across fields like psychology and computer science.
## Thinking Rationally:

AI also seeks to formalize “laws of thought” through logic, though human thinking is often inconsistent and uncertain.
## Acting Humanly:

Turing's test evaluates AI by its ability to mimic human behavior convincingly, encompassing skills like reasoning and language.
## Acting Rationally:

Russell and Norvig advocate for AI that acts rationally to achieve the best outcomes, integrating reasoning and adaptability to environments.


# BASE MODEL - REASONER

The base model has been created as a new staarting point : It has been fully primed with various types of chains of thoughts and step by step solutions : enabling for reward training to take place . this model has been trained with various languges ( not intensivly ), enabling for cross languge understanding ; 
Here we create a valid start point for agent based modelling , As we find that some training actually affects existing knowledge , hence agents become a thing ! or if you prefr, distillations ....
These agents can be medical , technical , roleplayers etc . 

## Rewards and modelling reasoning capablitys

Modelling reasoning begins with mathmatics , here we focus where the mdel should have been inesivly pretrained but was not , SO we focus on basic mathmatical tasks , then programming , diagnosis etc :
This scheme can be used also with other tasks , such as planning providing structured outputs for the task being performed. as well explanationsif required :

Advance reasoning does not come from chain of thoughts !!! or distilation !!! ... It comes from the ability for the model to create a explanation for exisrting problems , and finding alturnative solutions , then optimising the best solutions whilst learning each route taken to get to the answer :
Previously it has been simulating a answer using patern recognition . or recall of a verbatum problem .. SO now we would like it to find the inner part of the task...  Ie calculate  .. this calccualtion process enables thinking !
We can also use it for emotive responses , and interview techniques . so it ill explain why it asked that particular question or gave that type of response , ie if it was empathic or had sentimental value etc , such as determoining the sentiment of the use and the intent and using this also as a reflective point on the response given and why could it have been different to acheive the same goals !



### Merge Method ( past Checkpoints and Pretraining)

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:
* [LeroyDyer/SpydazWeb_AI_HumanAGI_002](https://huggingface.co/LeroyDyer/SpydazWeb_AI_HumanAGI_002)
* [LeroyDyer/LCARS_TOP_SCORE](https://huggingface.co/LeroyDyer/LCARS_TOP_SCORE)
* [LeroyDyer/_Spydaz_Web_AI_CheckPointsMerged](https://huggingface.co/LeroyDyer/_Spydaz_Web_AI_CheckPointsMerged)
* [LeroyDyer/_Spydaz_Web_AI_ReasoningCheckPointsMerged](https://huggingface.co/LeroyDyer/_Spydaz_Web_AI_ReasoningCheckPointsMerged)



