Upload README (3).md

README (3).md

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+---
+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
+- not-for-all-audiences
+language:
+- en
+- sw
+- ig
+- zu
+- ca
+- es
+- pt
+- ha
+---
+# SpydazWeb AGI 
+
+
+This is based on the Quiet Star Project : which was abandoned earlier in the year :)
+
+Current update : Deciding which vsion architecture to use!
+
+# 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