README.md

metadata

base_model: LeroyDyer/_Spydaz_Web_AI_V1_4BIT
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

SpydazWeb AI

https://github.com/spydaz

* 32k context window (vs 8k context in v0.1)
* Rope-theta = 1e6
* No Sliding-Window Attention

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 Project : which was abandoned earlier in the year :)

Introduction :

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This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind , who may need to construct shelters develop technologys , or medical resources as well as maintain the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