--- 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) # 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)