Update README.md

README.md

@@ -29,78 +29,24 @@ pipeline_tag: text-generation
 ---
 # SpydazWeb AGI 
 
+## SpydazWeb AI model :
 
-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:
-
-
-```
-! 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
-
-```
-
-
-
-
-# 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 
+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.
 
-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 ! 
+<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
+https://github.com/spydaz
 
-#### 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 ) 
 
+### General Intenal Methods:
 
+Trained for multi-task operations as well as rag and function calling :
 
-<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
-https://github.com/spydaz
+This model is a fully functioning model and is fully uncensored: 
 
     * 32k context window (vs 8k context in v0.1)
     * Rope-theta = 1e6
@@ -112,26 +58,6 @@ https://github.com/spydaz
       * 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 : 
@@ -173,4 +99,76 @@ the model can also generate markdown charts with mermaid.
     * Medical Reporting
   * Virtual laboritys simulations
   * Chain of thoughts methods
-  * One shot / Multi shot prompting tasks
+  * 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
+
+```
+
+
+
+