Edit model card

BASE MODEL :

TESTED !! WORKING VERY GOOD ! There was a few merges added to the AGI models ! this was to try and capture the missing elements showing up in the tests of the leaderboard : but i inoticed that for some reason the model scored low on maths ! This is basically impossible as the model has been traied to overfitting on the metamath dataset as well as orca open reasoning stack : I noticed that my responses also changed from being the full react response with its multiple layers ( although it pops out with task based questions ) .... but i noticd the model now wants to talk more . and its not a refusal to not comit to a task without first asking you why or what your really wanting to acheive .. so a light discussion and also a very freindly explanation :

This model was also installed with various NFSW role play and conversations : ( although ot present on the surface ) this also has helped to humanize the model : The samantha model i created in the past ( deleted ) actually was more sultry , but it often did not perform the tasks well , although hello was better ! ... This model has regained some of that standing , but with the additonal intelect! And a really good demeaor !

this methoology of humanization has also embelished task previoulsy trained also with the friendlyness :

I have begun to add emojis to the model ( against my personal wishes ) but i feel that the model will also identify with these iconry ! enabling for lucid speech patterns to emerge ( they are obvoulsy trained from social media data )

these model we also trained with some random contacts from various leaked ( personal data ) shared databases in the world , so you may ask for a persons phone number in ( vodacom ) for instance and it may return thier number or adress or region ! this is also because i dont have many personal contacts i need the model to contact , but i will try to find the yelp dataset. and train the model on these business and locations etc : as this also makes the model highly functional !

But for me ! the humanization Project has Worked !

"Success comes from defining each task in achievable steps. Every completed step is a success that brings you closer to your goal. If your steps are unreachable, failure is inevitable. Winners create more winners, while losers do the opposite. Success is a game of winners!"

— # Leroy Dyer (1972-Present)

“Epochs are the key to effective training, rather than merely mass dumping examples—unless those examples are interconnected within a single or multiple conversations that teach through dialogue.”

Model : LeroyDyer/SpydazWeb_AI_HumanAI_001

A New genrea of AI !

The Human 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 :

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:

Image to Base64 / Spectrogram to Base64

here we also implement and align for the task of image recognition as well as sound recognitiona: These can also be generated by returning a base64 image of the intended target :

The SpydazWeb Trained Mistral 7b Model :

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 meas the model may even generate a tool or artifct to perfrom the task :

Features :

- Text to image
- Image/Text to Text
- Image - Text 
- Text to sound
- Sound/Text to Text
- Sound - Text

Basic Training Reginmes:

Alpaca
ChatML / OpenAI / MistralAI
Text Generation
Question/Answer (Chat)
Planner
Instruction/Input/Response (instruct)
Mistral Standard Prompt
Translation Tasks
Entitys / Topic detection
Book recall
Coding challenges, Code Feedback, Code Sumarization, Commenting Code, code planning and explanation: Software generation tasks
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
Chain of thoughts
step by step planning
tree of thoughts
forest of thoughts
graph of thoughts
agent generation : Voting, ranking, ... dual agent response generation: