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+ ---
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+ license: mit
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+ tags:
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+ - Mistral_Star
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+ - Mistral_Quiet
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+ - Mistral
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+ - Mixtral
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+ - Question-Answer
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+ - Token-Classification
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+ - Sequence-Classification
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+ - SpydazWeb-AI
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+ - chemistry
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+ - biology
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+ - legal
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+ - code
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+ - climate
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+ - medical
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+ - text-generation-inference
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+ - not-for-all-audiences
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+ language:
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+ - en
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+ - sw
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+ - ig
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+ - zu
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+ - ca
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+ - es
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+ - pt
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+ - ha
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+ ---
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+ # SpydazWeb AGI
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+
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+
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+ This is based on the Quiet Star Project : which was abandoned earlier in the year :)
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+
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+ Current update : Deciding which vsion architecture to use!
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+
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+ # Introduction :
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+
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+ ## STAR REASONERS !
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+
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+ 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:
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+ 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:
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+ 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 :
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+ 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 :
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+ 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 !
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+ 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 :
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+ these thoughts will be used in future genrations assisting the model as well a displaying explantory informations in the output :
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+
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+ these tokens can be displayed or with held also a setting in the model !
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+
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+ ### can this be applied in other areas ?
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+
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+ 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 :
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+ 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 !
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+ 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 !.
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+
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+ ### Conclusion
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+
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+ 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 :
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+ 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 !
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+ 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 !
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+ ....
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+
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+ 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 !
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+ 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 !
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+ hence an AGI !
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+
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+ #### AI AGI ?
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+ 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 )
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+
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+
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+
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+ <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
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+ https://github.com/spydaz
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+
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+ * 32k context window (vs 8k context in v0.1)
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+ * Rope-theta = 1e6
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+ * No Sliding-Window Attention
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+ * Talk heads - produce resposnes which can be used towards the final output
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+ * Pre-Thoughts - Enables for pre-generation steps of potential artifacts for task solving:
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+ * Generates plans for step by step thinking
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+ * Generates python Code Artifacts for future tasks
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+ * Recalls context for task internally to be used as refference for task:
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+ * show thoughts or hidden thought usages ( Simular to self-Rag )
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+
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+
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+ This model will be a custom model with internal experts and rag systems
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+ enabling for preprocessing of the task internally before outputting a response
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+
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+ ## SpydazWeb AI model :
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+
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+ This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind ,
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+ 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:
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+ A friendly interface with a personality caring and flirtatious at times : non binary !...
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+ 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:
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+ the model was trained to operateinaragenvironment utilizing content and internal knowledge to respond to questions or create enriched sumarys.
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+
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+
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+
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+ ### General Intenal Methods:
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+
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+ Trained for multi-task operations as well as rag and function calling :
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+
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+ This model is a fully functioning model and is fully uncensored:
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+
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+ the model has been trained on multiple datasets on the huggingface hub and kaggle :
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+
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+ the focus has been mainly on methodology :
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+
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+ * Chain of thoughts
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+ * step by step planning
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+ * tree of thoughts
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+ * forest of thoughts
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+ * graph of thoughts
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+ * agent generation : Voting, ranking, ... dual agent response generation:
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+
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+ with these methods the model has gained insights into tasks, enabling for knowldge transfer between tasks :
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+
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+ the model has been intensivly trained in recalling data previously entered into the matrix:
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+ The model has also been trained on rich data and markdown outputs as much as possible :
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+ the model can also generate markdown charts with mermaid.
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+
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+
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+ ## Training Reginmes:
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+ * Alpaca
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+ * ChatML / OpenAI / MistralAI
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+ * Text Generation
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+ * Question/Answer (Chat)
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+ * Instruction/Input/Response (instruct)
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+ * Mistral Standard Prompt
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+ * Translation Tasks
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+ * Entitys / Topic detection
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+ * Book recall
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+ * Coding challenges, Code Feedback, Code Sumarization, Commenting Code
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+ * Agent Ranking and response anyalisis
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+ * Medical tasks
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+ * PubMed
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+ * Diagnosis
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+ * Psychaitry
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+ * Counselling
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+ * Life Coaching
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+ * Note taking
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+ * Medical smiles
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+ * Medical Reporting
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+ * Virtual laboritys simulations
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+ * Chain of thoughts methods
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+ * One shot / Multi shot prompting tasks