README.md

metadata

library_name: transformers
tags:
  - swarm
  - ai
  - agent
  - llm
  - convergent
  - cpu
  - fp32
  - agi
license: apache-2.0
datasets:
  - roneneldan/TinyStories
  - openai/gsm8k
  - MuskumPillerum/General-Knowledge
  - agentica-org/DeepCoder-Preview-Dataset
  - tangyuhang/KnowLogic
language:
  - en
pipeline_tag: text-generation
new_version: reaperdoesntknow/CasualSwarms

SAGI V3.2 - SELF-AWARE AGI

SAGI (Swarm AGI) is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory.

V3.2 introduces a revolutionary Self-Assessment Layer, allowing the system to predict its own performance, identify skill gaps, and autonomously design its own learning curriculum.

🌟 Architecture Evolution: Swarm-8 V3.2

┌─────────────────────────────────────────────────────────────────────────┐
│                    Swarm-8 V3.2 - SELF-AWARE AGI                        │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                    SELF-ASSESSMENT LAYER                       │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Performance Predictor     • Skill Gap Analyzer              │   │
│  │  • Auto-Curriculum Gen       • Real-Time Error Detector        │   │
│  │  • Capability Boundary Detector                                 │   │
│  └────────────────────────────────────────────────────────────────┘   │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                  AGI CORE (7 Subsystems)                       │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Hierarchical Memory       • Causal World Model              │   │
│  │  • Meta-Learner              • Concept Library                 │   │
│  │  • Reflection Engine         • Uncertainty Reasoner            │   │
│  │  • Adversarial Self-Play                                       │   │
│  └────────────────────────────────────────────────────────────────┘   │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                 SWARM CORE (20 Agents)                         │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Vectorized Agents         • Differentiable Routing          │   │
│  │  • Dynamic Resource Mgmt     • Trust-Based Activation          │   │
│  └────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────┘

🚀 Key V3.2 Enhancements

• Predictive Self-Awareness: Estimates success probability and identifies risks before attempting a task.
• Skill Taxonomy: Systematic tracking of 24 core skills across Cognition, Knowledge, Code, Creativity, and Planning.
• Autonomous Learning: Self-designed, personalized learning paths via the Auto-Curriculum Generator.
• Real-Time Correction: Proactive error detection during the generation process.
• Boundary Mapping: Precise identification of capability edges with expansion strategies.

💻 Usage

Installation

pip install torch transformers datasets sagi-swarm

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SAGI")
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/SAGI")

# Generate text
prompt = "Explain the concept of emergence in swarm intelligence:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Skill Taxonomy (24 Core Skills)

• Cognition: Pattern recognition, Causal reasoning, Concept formation.
• Knowledge: Fact retrieval, Knowledge integration, Common sense.
• Code: Syntax understanding, Algorithm design, Debugging, Optimization.
• Creativity: Divergent thinking, Novel combination, Generative synthesis.
• Planning: Goal decomposition, Dependency analysis, Resource allocation.
• Meta-Cognition: Self-monitoring, Error detection, Strategy selection, Uncertainty quantification.

🧠 Decision Flow (V3.2)

1. Pre-Assessment: Predict success, identify risks, recommend strategy.
2. Execution: Generate with selected strategy.
3. Real-Time Monitoring: Catch and correct errors during generation.
4. Post-Assessment: Update skill proficiencies, check boundaries, refine future predictions.
5. Learning: Update internal models and curricula.

⚠️ Safety & Limitations

• Experimental Research Prototype: Not intended for production use.
• Code Execution: Model includes tool-use capabilities (Python sandbox). Use with caution.
• Intrinsic Motivation: Self-improving systems may exhibit unpredictable growth patterns.

📄 License

Apache License 2.0

📝 Citation

@software{sagi2026,
  title={SAGI: Self-Aware General Intelligence System},
  author={Reaperdoesntknow},
  year={2026},
  url={https://huggingface.co/reaperdoesntknow/SAGI},
  version={3.2.0}
}