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---
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}
}
``` |