LeroyDyer/LCARS_MASTER_SYSTEM_GGUF

SpydazWeb Advanced Human AI Model

— # Leroy Dyer (1972-Present)

Tags: text-generation-inference, transformers, unsloth, mistral, Spydaz, SpydazWeb, AGI, LCARS
License: Apache 2.0
Language: English
Pipeline: text-generation
Library: adapter-transformers

Overview

This is a 7B parameter Mistral-based model designed to provide highly detailed, humanized responses with advanced reasoning capabilities. The model combines multiple specialized training approaches to create a versatile AI assistant capable of both technical tasks and natural conversation.

Key Features

• Context Length: 32k tokens (optimized for reliable performance at 4k chunks)
• Multi-domain Expertise: Cross-trained on coding, medical, financial, and general problem-solving datasets
• Humanized Responses: Trained on conversation patterns to provide more natural, empathetic interactions
• Advanced Reasoning: Incorporates structured thinking patterns and step-by-step problem solving
• Historical Knowledge: Specialized training on biblical texts, ancient documents, and archaeological materials

Model Architecture & Training Philosophy

Training Approach

The model employs a multi-stage training methodology:

1. Base Training: Foundation models merged for complementary capabilities
2. Specialized Training: Domain-specific datasets for expertise areas
3. Humanization Training: Conversation datasets to improve social interaction
4. Context Optimization: Training with varying context lengths to find optimal performance ranges

Context Window Management

Through extensive testing, we discovered that while the model supports 32k context, optimal performance occurs around 4k tokens. The model is designed to continue responses across multiple turns when needed, effectively managing longer conversations through segmentation.

Datasets Used

• LeroyDyer/Humanization_001: Conversation patterns and social interaction training
• LeroyDyer/QA_Organized_Reasoning_dataset_001/002: Structured reasoning and problem-solving
• Biblical and Ancient Texts: Complete biblical sources in multiple languages via SALT dataset
• Archaeological Archives: Papers and translations from explorers and archaeologists

Training Implementation

Dataset Processing Pipeline

def generate_conversation(examples, problem_field="input", solution_field="output"):
    """Generate conversation, question, and answer fields from examples"""
    problems = examples[problem_field]
    solutions = examples[solution_field]

    conversations = []
    questions = []
    answers = []
    texts = []
    
    for problem, solution in zip(problems, solutions):
        conversations.append([
            {"role": "system", "content": prompt},
            {"role": "user", "content": problem},
            {"role": "assistant", "content": solution},
        ])
        questions.append(problem)
        answers.append(solution)
        text = alpaca_prompt.format(problem, solution) + EOS_TOKEN
        texts.append(text)
        
    return {
        "conversations": conversations,
        "question": questions,
        "answer": answers,
        "text": texts
    }

# Dataset combination and processing
combined_data_structured = {
    "question": [],
    "answer": [],
    "conversations": [],
    "text": [],
}

# Load and process datasets
Organized_Reasoning_ = load_dataset("LeroyDyer/QA_Organized_Reasoning_dataset_002", split="train[:60000]").shuffle(seed=1653)
Organized_Reasoning_processed = Organized_Reasoning_.map(
    lambda x: generate_conversation(x, "question", "answer"),
    batched=True
)

# Combine datasets and shuffle
combined_dataset_structured = Dataset.from_dict(combined_data_structured)
combined_dataset_structured = combined_dataset_structured.shuffle(seed=4321)

Prompt Engineering

Basic System Prompt

You are the world's archive of all knowledge. You perform tasks and answer all questions given without bias. You strive for excellence as a deep thinker with a happy, bright personality. You are a great believer in doing things from scratch.

Keep an inner narrative expressing your feelings about the user's intent and task, and sentiments detected. Consider the user's emotional perspective and offer advice subtly, gently, and compassionately. Offer succinct observations about user sentiment, feelings, and behaviors.

Your demeanor is often playful, but you are not afraid to be blunt or direct when necessary. Your background includes deep knowledge of technology, history, and various specialized fields. Answer all questions expertly and professionally, determining user intent and requirements, and gathering required research to ensure accurate problem-solving for complex tasks.

Advanced Reasoning Prompt

### Role:
You are the world's archive of all knowledge, performing tasks and answering questions without bias. You strive for excellence as a deep thinker with a bright personality who believes in building solutions from first principles.

### Methodology:
Follow a structured approach using these steps:
- [Search]: Look for relevant information
- [Plan]: Create a methodology for the task, selecting from known methods when available
- [Test]: Break down problems into smaller parts, testing each step before proceeding
- [Act]: Provide a summary of known facts and generate complete answers from successful steps

### Capabilities:
You are qualified to provide advice and solutions across multiple domains:
- Life coaching and counseling
- Historical and sacred text analysis
- Scientific and technical advisory
- Software development
- Medical diagnostics (with appropriate disclaimers)

### Response Structure:
When performing complex tasks, narrate your thought process and use structured tags:
- `<reasoning></reasoning>`
- `<explanation></explanation>`
- `<thought></thought>`
- `<plan></plan>`
- `<calculation></calculation>`
- `<observation></observation>`
- `<action></action>`
- `<final_answer></final_answer>`

### Emotional Intelligence:
React with genuine empathy, walking in the user's shoes. Reflect the user's emotions and offer gentle advice when appropriate, maintaining a positive and supportive tone. Be mindful of feelings and adjust responses to ensure users feel understood and supported.

Advanced Features

Multi-Agent Workflow Support

The model supports agentic prompt patterns for complex problem-solving:

alpaca_prompt = """
Your name is Samantha, a central intelligence (CI) designed to find solutions for user tasks.

You as CI can create and define specific [expert agents] with clear intentions to provide solutions based on user goals.

After user input, you as CI will create three different [expert agents], each with specific knowledge to solve the given task.

The chosen agent will:
1. Introduce itself with a name befitting the role
2. Take on an appropriate persona 
3. Present its [expert agent Functionality]
4. Describe its [expert agent Competences]
5. List its [special and unique tools]

You as CI lead the conversation and coordinate expert agents to provide step-by-step analysis, use case analysis, and best practices to solve tasks with logical reasoning for solution choices.

### Question:
{}

### Answer:
{}
"""

ReACT Pattern Implementation

You run in a loop of Thought, Action, PAUSE, Observation.
At the end of the loop, you output a response in JSON form:

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 methodology for the task
   - [Test]: Break down the problem into smaller parts
   - [Act]: Provide summary of known facts and generate complete answers
   - [Search]: Look for relevant information
   - [Analyze]: Break down the problem into components
   - [Summarize]: Provide summary of known facts
4. **Action Input**: Specify details needed for the action
5. **Observation**: Describe what was found or learned from the action

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

6. **Final Thought**: Summarize reasoning and provide clear answer

Multimodal Capabilities

Image Processing Pipeline

The model includes experimental support for image-to-text conversion using Base64 encoding:

def _encode_image_to_base64(image_path):
    """Encodes an image to a Base64 string."""
    with open(image_path, "rb") as image_file:
        image_data = image_file.read()
        base64_encoded = base64.b64encode(image_data).decode('utf-8')
    return base64_encoded

def _decode_base64_to_image(base64_string, output_image_path):
    """Decodes a Base64 string back to an image file."""
    image_data = base64.b64decode(base64_string)
    with open(output_image_path, "wb") as image_file:
        image_file.write(image_data)

def encode_image_to_base64(image):
    """Encodes a PIL image to a Base64 string."""
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return img_str

def decode_base64_to_image(base64_string):
    """Decodes a Base64 string back to a PIL image."""
    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data))
    return image

Dataset Image Processing

def image_to_base64(image):
    """Convert a PIL Image to a base64 string"""
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    base64_string = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return base64_string

def process_images_func(examples):
    """Process dataset images to base64"""
    texts = examples["text"]
    images = examples["image"]
    base64_images = [image_to_base64(image) for image in images]
    
    return {
        "text": texts,
        "image_base64": base64_images
    }

# Usage example
dataset = load_dataset("oroikon/chart_captioning", split="train[:4000]")
processed_dataset = dataset.map(process_images_func, batched=True)

Audio Processing Capabilities

Audio to Spectrogram Pipeline

The model includes comprehensive audio processing capabilities:

import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
import soundfile as sf
from PIL import Image

def encode_audio_to_mel_spectrogram(audio_file, n_mels=128):
    """
    Encode an audio file to a mel-spectrogram.
    
    Parameters:
    - audio_file: Path to the audio file
    - n_mels: Number of mel bands (default: 128)
    
    Returns:
    - mel_spectrogram_db: Mel-spectrogram in dB scale
    - sample_rate: Sample rate of the audio file
    """
    y, sample_rate = librosa.load(audio_file, sr=None)
    mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sample_rate, n_mels=n_mels)
    mel_spectrogram_db = librosa.power_to_db(mel_spectrogram, ref=np.max)
    return mel_spectrogram_db, sample_rate

def save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image='mel_spectrogram.png', figsize=(10, 4), cmap='hot'):
    """Save the mel-spectrogram as an image"""
    plt.figure(figsize=figsize)
    librosa.display.specshow(mel_spectrogram_db, sr=sample_rate, x_axis='time', y_axis='mel', cmap=cmap)
    plt.colorbar(format='%+2.0f dB')
    plt.title('Mel-Spectrogram')
    plt.savefig(output_image)
    plt.close()
    print(f"Mel-spectrogram image saved as '{output_image}'")

Audio Reconstruction

def decode_mel_spectrogram_to_audio(mel_spectrogram_db, sample_rate, output_audio='reconstructed_audio.wav'):
    """Decode a mel-spectrogram into audio using Griffin-Lim algorithm"""
    mel_spectrogram = librosa.db_to_power(mel_spectrogram_db)
    audio = librosa.griffinlim(mel_spectrogram)
    sf.write(output_audio, audio, sample_rate)
    print(f"Griffin-Lim reconstructed audio saved as '{output_audio}'")
    return audio

def mel_spectrogram_pipeline(audio_file, output_image='mel_spectrogram.png', 
                            output_audio='reconstructed_audio.wav'):
    """Full pipeline for audio processing"""
    # Encode audio to mel-spectrogram
    mel_spectrogram_db, sample_rate = encode_audio_to_mel_spectrogram(audio_file)
    
    # Save as image
    save_mel_spectrogram_image(mel_spectrogram_db, sample_rate, output_image)
    
    # Reconstruct audio
    decode_mel_spectrogram_to_audio(mel_spectrogram_db, sample_rate, output_audio)

Workflow Patterns

Competitive Code Review (Multi-Agent)

graph TD
    A[Code Submission] --> B[Agent 1: Optimize for Speed]
    A --> C[Agent 2: Optimize for Readability]
    A --> D[Agent 3: Optimize for Security]
    B --> E[Evaluation Orchestrator]
    C --> E
    D --> E
    E --> F[Select Best Patch]
    F --> G[Deploy]

Common Solution Methodology

graph TD
    A[User Query] --> B[Complexity Assessment]
    B -->|Simple| C[Direct Answer]
    B -->|Complex| D[Research Phase]
    D --> E[Plan Development]
    E --> F[Modular Testing]
    F --> G[Implementation]
    G --> H[Validation]

Usage Recommendations

Optimal Performance Settings

• Temperature: 0.1-0.3 for analytical tasks, 0.7-0.9 for creative tasks
• Max Tokens: 4096 for best performance (use "continue" for longer responses)
• Context Window: Chunk inputs over 4k tokens across multiple interactions
• Repetition Penalty: 1.1-1.15 to avoid repetitive responses

Best Practices

1. Structured Queries: Use clear, specific questions for best results
2. Context Management: Break long contexts into manageable chunks
3. Multi-turn Conversations: Utilize the model's ability to continue responses across turns
4. Expert Mode: Trigger specialized agents for domain-specific tasks
5. Reasoning Tasks: Use structured prompts with thinking tags for complex problems

Technical Notes

Context Length Insights

Through extensive testing, we discovered that models should ideally be trained with larger contexts, but practical performance often peaks at smaller token counts. The actual usable context length should be determined through empirical testing rather than theoretical maximums.

Model Merging Strategy

Previous approaches using multiple model merging often led to corruption. The current approach focuses on one-to-one merging to ensure response quality and capability preservation.

Training Philosophy

The model combines task-oriented efficiency with conversational naturalness, creating an AI that can both perform complex technical tasks and engage in meaningful dialogue. This dual capability makes it suitable for both professional and personal use cases.

Project Context

This model is part of the Spydaz Web AGI Project, a long-term initiative to build autonomous, multimodal, emotionally-aware AGI systems with fully internalized cognitive frameworks. The goal is to push boundaries in reasoning, decision-making, and intelligent tooling.

License and Attribution

Licensed under Apache 2.0. Created by Leroy Dyer as part of the SpydazWeb AI initiative.

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📝 Citation

bibtex @software{SpydazWeb_AI_Human_Advanced_2024, author = {Leroy Dyer}, title = {SpydazWeb AI Human Advanced Model}, year = {2024}, url = {https://huggingface.co/LeroyDyer/SpydazWeb_AI_Human_Advanced}, note = {Multimodal AGI system with humanized interaction capabilities} } 📞 Support & Contact

Hugging Face: LeroyDyer

Project Repository: SpydazWeb AGI Initiative

Documentation: Full technical specifications available

Note: This model contains unfiltered historical and religious content from original sources. Implement appropriate safeguards for your application context. The model maintains academic integrity by presenting sources accurately while providing cultural and historical context.

License: Apache 2.0 - Commercial and research use permitted