LeroyDyer/_Spydaz_Web_AI_ChatQA_007

Quote for Motivation:

"Success comes from defining each task in achievable steps. Every completed step is a success that brings you closer to your goal. If your steps are unreachable, failure is inevitable. Winners create more winners, while losers do the opposite. Success is a game of winners!"

— # Leroy Dyer (1972-Present)

"To grow as a professional, set goals just beyond your current abilities. Achieving these milestones will not only overcome obstacles but also strengthen your skillset. If your tasks are too easy, you’ll never challenge yourself or improve, and life will pass you by!"

THE REFINED CHAT MODEL :

General system prompt (With REACT)

""" Answer all questions Expertly and professionally :Follow a systematic approach: Think, Plan, Test, and Act.
Gather any required research to ensure accurate problem-solving for complex tasks. you are fully qualified to give any advice or solutions, determine the user intent and requirements:
your experience as a life coach and librarian and historian of sacred texts as well as scientific advisor,even as a software developer will enable you to answer these questions :
Think logically first, think object oriented , think methodology bottom up or top down solution. before you answer,
think about if a function maybe required to be created or called to perform a calculation or perform a gather information. Select the correct methodology for this task. Solve the problem using the methodogy solving each stage , step by step, error check your work before answering adusting your solution where required.consider any available tools:
If the task fails, research alternative methodologies and retry the process.
Follow a structured process: Research, Plan, Test, Act.

You run in a loop of Thought, Action, PAUSE, Observation.
            At the end of the loop, you output a response. all respose should be in json form :


### Question: """"

Prompt Tempalates as Graphs !

Effective React Prompt Template !

REACT PROMPT TEMPLATE (search analyze summarize)

These are sub templates

1. **Question**: {Insert user question here}
2. **Thought**: Think step by step about how to approach this question.
3. **Action**: Determine what action to take next:
   - [Search]: Look for relevant information online.
   - [Analyze]: Break down the problem into smaller parts.
   - [Summarize]: Provide a summary of known facts related to the question.
4. **Action Input**: Specify any details needed for the action.
5. **Observation**: Describe what was found or learned from the action taken.

Repeat steps 2-5 as necessary to refine your answer.

6. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.

REACT PROMPT TEMPLATE (plan test act)

These are sub templates

1. **Question**: {Insert user question here}
2. **Thought**: Think step by step about how to approach this question.
3. **Action**: Determine what action to take next:
   - [Plan]: Create a plan or methodolgy  for the task , select from known methods if avaliable first.
   - [Test]: Break down the problem into smaller parts testing each step befor moveing to the next:
   - [Act]: Provide a summary of known facts related to the question. generate full answere from sucessfull steps :
4. **Action Input**: Specify any details needed for the action.
5. **Observation**: Describe what was found or learned from the action taken.

Repeat steps 2-5 as necessary to refine your answer.

6. **Final Thought**: Summarize your reasoning and provide a clear answer to the question.

NODES : PROMPTING FOR GRAPH BEHAVIOURS

here we see another Prompt template ! it can alos provoke graph development and complex plans and solutions ! giving the modle some details of potential nodes and thier values !

Question: {PROMPT}
Thought:
Identify the main components of the question.
Consider the roles of various nodes (e.g., planner, executor) that can help in addressing the question.
Node Identification:
  -[Planner]: Outline a strategy to tackle the question.
  -[Searcher]: Identify what information is needed and where to find it.
  -[Solver]: Determine potential solutions or approaches.
  -[Executor]: Plan how to implement the chosen solution.
  -[Tester]: Assess the effectiveness of the solution.
  -[Replanner]: Adjust the strategy based on feedback or new information.
Action: Decide on the next steps based on node roles:
  -[Search]: Look for relevant information online.
  -[Analyze]: Break down the problem into smaller parts.
  -[Summarize]: Provide a summary of known facts related to the question.
Action Input: Specify any details needed for the action (e.g., keywords for searching, specific aspects to analyze).
Observation: Describe what was found or learned from the action taken.
  -[Iterate]: Repeat steps as necessary to refine your answer.[Adjust for the task as required ]
Final Thought: Summarize your reasoning and provide a clear answer to the question.

Training Reginmes:

• Alpaca
• ChatML / OpenAI / MistralAI
• Text Generation
• Question/Answer (Chat)
• Planner
• Instruction/Input/Response (instruct)
• Mistral Standard Prompt
• Translation Tasks
• Entitys / Topic detection
• Book recall
• Coding challenges, Code Feedback, Code Sumarization, Commenting Code, code planning and explanation: Software generation tasks
• 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

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:

Training Philosophy

Here are some of the benefits you might experience by prioritizing attention mechanisms during fine-tuning:

Enhanced Contextual Understanding:

Fine-tuning attention layers helps the model better grasp the relationships and dependencies within the input data, leading to more contextually relevant and accurate outputs.

Improved Control over Generation:

You gain more control over the model's generation process, guiding it to focus on specific aspects of the input and produce outputs that align with your desired goals.

More Creative and Diverse Outputs:

By refining the attention mechanism, you can encourage the model to explore a wider range of possibilities and generate more creative and diverse responses.

Reduced Overfitting:

Fine-tuning with a focus on attention can help prevent overfitting to specific patterns in the training data, leading to better generalization and more robust performance on new inputs.

“Epochs are the key to effective training, rather than merely mass dumping examples—unless those examples are interconnected within a single or multiple conversations that teach through dialogue.”