--- 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 language: - en - sw - ig - zu - ca - es - pt - ha pipeline_tag: text-generation --- # SpydazWeb AGI ## SpydazWeb AI model : This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind , who may need to construct shelters develop technologys , or medical resources as well as maintain the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. https://github.com/spydaz ### 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: * 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 ) 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 This model will be a custom model with internal experts and rag systems enabling for preprocessing of the task internally before outputting a response This is based on the Quiet Star Reasoning Project : which was abandoned earlier in the year :) Current Update : This model is working , AND TRAINED !!! to load the model it requires trust-remote=TRUE:: But also if it does not load then you need to clone the github: # 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 !. #### 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 ) ### 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 ! # LOAD MODEL ``` ! git clone https://github.com/huggingface/transformers.git ## copy modeling_mistral.py and configuartion.py to the Transformers foler / Src/models/mistral and overwrite the existing files first: ## THEN : !cd transformers !pip install ./transformers ``` then restaet the environment: the model can then load without trust-remote and WILL work FINE ! it can even be trained : hence the 4 bit optimised version :: ``` Python # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", trust_remote_code=True) model.tokenizer = tokenizer ```