---
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

```bash
pip install torch transformers datasets sagi-swarm
```

### Quick Start

```python
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

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