_Spydaz_Web_AI_AGI_R1_Top_Student

Rank 1588 - TOP MISTRAL 7B

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 .

PERSONAL NOTE :

A nice a great model with very intersting and detailled answers : This model has been trained on resoning . The various Questions types can trigger sucha response : This model has also been trained ( grpo Rewarding method ) on the reAct methodolgy ! Rewarding the model for producing Observations and actions as well as thoughts and final answers @ This model has been heavily trained on ALL benchmark Data sets , Although it seemed to fail various maths evals : i has been heavily trained on maths and chaoin of thought maths reasoning paterns : this has included theeval sets as well as now various multiple choice setups : For me this model has not failed in maths at all but perhaps in its exact output not matching the exact expected or preffered output despite the answer being correct: I found that after training the model on more multiple choicce datasets its eval score rose higher : but thois may not have increaseditsinteligence level as i also detected other tasks becomeing obscured and blocked : This model has been trained on multiple medical tasks from concilling to diagnosis to research tasks and traiage ! it has also been trained to develop python code for medical calculations or problems and takss : these function or tools can be then repurposed as workable tools or agents to be used Later : I have fully enjoyed this model and it is my current unrestricted model ! It has also been merged in the past with the best of the best role play models as well as story telling models ! helping for character generation and tasks : i also took the liberty of trainign the model on various movie scripts and generated data with interviews of characters and personas on the same movie data and knowledge : enabling for unique persopectives regarding roles and characters : Truly this modle is about the prompt : as many large prompts havbe been used as well as no prompt ! enabling for embedings to alling well with large expectations !

subsequent models will be re-allinged to my personal datasets : Pretraining various large data books and wikipedia data : our model has been task so much that now we only need to add more epochs of factual information fpor the model to absorb ! after this process we will add a unique character on top of the model ... as we are also seekingn a true persona and personal assistant male or female is ireelivant but a unique personilty is expected !

Deep thinking Model - Highly Trained om Multiple Datasets

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 .

This model was trained on various datasets , such as the basic math ones . As well as some adaced reasoning tasks. here we experiment with various styles of data from finacial to medical to coding (althugh this seemms to have an issue with very long context ,, as the servers seems to crash out a lot whe pushing larger cotext and rewards - suggestion , only 1 sample perstep can solve it), very impressive with its diagnsis skill for medical.

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.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here