README.md

---
license: mit
tags:
- Mistral_Star
- Mistral_Quiet
- Mistral
- Mixtral
- Question-Answer
- Token-Classification
- Sequence-Classification
- SpydazWeb-AI
- chemistry
- biology
- legal
- code
- climate
- medical
- text-generation-inference
language:
- en
- sw
- ig
- zu
- ca
- es
- pt
- ha
pipeline_tag: text-generation
---
# SpydazWeb AGI 


This is based on the Quiet Star Reasoning Project : which was abandoned earlier in the year :)
 These are some associated files ...
# Introduction :

## STAR REASONERS ! 

this provides a platform for the model to commuicate pre-response , so an internal objective can be set ie adding an extra planning stage to the model improving its focus and output:
the thought head can be charged with a thought or methodolgy, such as a ststing to take a step by step approach to the problem or to make an object oriented model first and consider the use cases before creating an output:
so each thought head can be dedicated to specific ppurpose such as Planning or artifact generation or use case design : or even deciding which methodology should be applied before planning the potential solve route for the response :
Another head could also be dedicated to retrieving content  based on the query from the self which can also be used in the pregenerations stages :
all pre- reasoners can be seen to be Self Guiding ! essentially removing the requirement to give the model a system prompt instead aligning the heads to a thoght pathways ! 
these chains produce data which can be considered to be thoughts : and  can further be displayed by framing these thoughts with thought tokens : even allowing for editors comments giving key guidance to the model during training : 
these thoughts will be used in future genrations assisting the model as well a displaying explantory informations in the output :

these tokens can be displayed or with held also a setting in the model ! 

### can this be applied in other areas ?

Yes! , we can use this type of method to allow for the model to generate code in another channel or head potentially creating a head to produce artifacts for every output , or to produce entity lilsts for every output and framing the outputs in thier relative code tags or function call tags :
these can also be displayed or hidden for the response . but these can also be used in problem solvibng tasks internally , which again enables for the model to simualte the inpouts and outputs from an interpretor ! 
it may even be prudent to include a function executing internally to the model ! ( allowing the model to execute functions in the background! before responding ) as well this oul hae tpo also be specified in the config , as autoexecute or not !. 

### Conclusion 

the resonaer methodology , might be seen to be the way forwards , adding internal funciton laity to the models instead of external connectivity enables for faster and seemless model usage : as well as enriched and informed responses , as even outputs could essentially be cleanss and formated before being presented to the Calling interface, internally to the model :
the  take away is that arre we seeing the decoder/encoder model as simple a function of the inteligence which  in truth need to be autonomus ! 
ie internal functions and tools as well as disk interaction : an agent must have awareness and control over its environment with sensors and actuators : as a fuction callingmodel it has actuators and canread the directorys it has sensors ... its a start: as we can eget media in and out , but the model needs to get its own control to inpout and output also ! 
.... 

Fine tuning :  agin this issue of fine tuning : the disussion above eplains the requirement to control the environment from within the moel ( with constraints ) does this eliminate theneed to fine tune a model ! 
in fact it should as this give  transparency to ther growth ofthe model and if the model fine tuned itself we would be in danger of a model evolveing !
hence an AGI ! 

#### AI AGI ?
so yes we can see we are not far from an ai which can evolve : an advance general inteligent system ( still non sentient by the way ) 



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

    * 32k context window (vs 8k context in v0.1)
    * Rope-theta = 1e6
    * No Sliding-Window Attention
    * Talk heads  - produce resposnes which can be used towards the final output
    * Pre-Thoughts  - Enables for pre-generation steps of potential artifacts for task solving: 
      * Generates plans for step by step thinking 
      * Generates python Code Artifacts for future tasks
      * Recalls context for task internally to be used as refference for task:
    * show thoughts or hidden thought usages ( Simular to self-Rag )

    
This model will be a custom model with internal experts and rag systems
enabling for preprocessing of the task internally before outputting a response 

## SpydazWeb AI model :

This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind ,
who may need to construct shelters develop technologys , or medical resources as well as maintain the history of the past . keeping store of all the religious knowledge and data of the world:
A friendly interface with a personality caring and flirtatious at times : non binary !...
and Expert in all feilds: ie Uncensored and will not refuse to give information : the model can be used for role play as many character dialogues were als trained into the model as its personality to enable a greater perspective and outlook and natural discussion with the agents:
the model was trained to operateinaragenvironment utilizing content and internal knowledge to respond to questions or create enriched sumarys.



### General Intenal Methods:

Trained for multi-task operations as well as rag and function calling :

This model is a fully functioning model and is fully uncensored: 

the model has been trained on multiple datasets on the huggingface hub and kaggle :

the focus has been mainly on methodology : 

* Chain of thoughts
* step by step planning
* tree of thoughts
* forest of thoughts
* graph of thoughts
* agent generation : Voting, ranking, ... dual agent response generation:

with these methods the model has gained insights into tasks, enabling for knowldge transfer between tasks :

the model has been intensivly trained in recalling data previously entered into the matrix:
The model has also been trained on rich data and markdown outputs as much as possible : 
the model can also generate markdown charts with mermaid.


## Training Reginmes:
  * Alpaca
  * ChatML / OpenAI / MistralAI
  * Text Generation
  * Question/Answer (Chat)
  * Instruction/Input/Response (instruct)
  * Mistral Standard Prompt
  * Translation Tasks
  * Entitys / Topic detection
  * Book recall
  * Coding challenges, Code Feedback, Code Sumarization, Commenting Code
  * Agent Ranking and response anyalisis
  * Medical tasks
    * PubMed
    * Diagnosis
    * Psychaitry
    * Counselling
    * Life Coaching
    * Note taking
    * Medical smiles
    * Medical Reporting
  * Virtual laboritys simulations
  * Chain of thoughts methods
  * One shot / Multi shot prompting tasks