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LCARS_AI_QstaR_Nemo_GGUF

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metadata
base_model: unsloth/mistral-nemo-instruct-2407-bnb-4bit
license: apache-2.0
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

Spydaz WEB AI

Model Architecture

Mistral Nemo is a transformer model, with the following architecture choices:

  • Layers: 40
  • Dim: 5,120
  • Head dim: 128
  • Hidden dim: 14,436
  • Activation Function: SwiGLU
  • Number of heads: 32
  • Number of kv-heads: 8 (GQA)
  • Vocabulary size: 2**17 ~= 128k
  • Rotary embeddings (theta = 1M)
  • Developed by: LeroyDyer
  • License: apache-2.0
  • Finetuned from model : unsloth/mistral-nemo-instruct-2407-bnb-4bit
https://github.com/spydaz

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 ::



# 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