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--- |
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library_name: transformers |
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tags: |
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- swarm |
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- ai |
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- agent |
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- llm |
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- convergent |
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- cpu |
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- fp32 |
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- agi |
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license: apache-2.0 |
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datasets: |
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- roneneldan/TinyStories |
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- openai/gsm8k |
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- MuskumPillerum/General-Knowledge |
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- agentica-org/DeepCoder-Preview-Dataset |
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- tangyuhang/KnowLogic |
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language: |
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- en |
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pipeline_tag: text-generation |
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new_version: reaperdoesntknow/CasualSwarms |
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--- |
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# SAGI V3.2 - SELF-AWARE AGI |
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**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. |
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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. |
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## 🌟 Architecture Evolution: Swarm-8 V3.2 |
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``` |
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┌─────────────────────────────────────────────────────────────────────────┐ |
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│ Swarm-8 V3.2 - SELF-AWARE AGI │ |
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├─────────────────────────────────────────────────────────────────────────┤ |
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│ │ |
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│ ┌────────────────────────────────────────────────────────────────┐ │ |
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│ │ SELF-ASSESSMENT LAYER │ │ |
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│ ├────────────────────────────────────────────────────────────────┤ │ |
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│ │ • Performance Predictor • Skill Gap Analyzer │ │ |
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│ │ • Auto-Curriculum Gen • Real-Time Error Detector │ │ |
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│ │ • Capability Boundary Detector │ │ |
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│ └────────────────────────────────────────────────────────────────┘ │ |
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│ │ |
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│ ┌────────────────────────────────────────────────────────────────┐ │ |
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│ │ AGI CORE (7 Subsystems) │ │ |
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│ ├────────────────────────────────────────────────────────────────┤ │ |
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│ │ • Hierarchical Memory • Causal World Model │ │ |
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│ │ • Meta-Learner • Concept Library │ │ |
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│ │ • Reflection Engine • Uncertainty Reasoner │ │ |
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│ │ • Adversarial Self-Play │ │ |
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│ └────────────────────────────────────────────────────────────────┘ │ |
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│ │ |
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│ ┌────────────────────────────────────────────────────────────────┐ │ |
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│ │ SWARM CORE (20 Agents) │ │ |
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│ ├────────────────────────────────────────────────────────────────┤ │ |
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│ │ • Vectorized Agents • Differentiable Routing │ │ |
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│ │ • Dynamic Resource Mgmt • Trust-Based Activation │ │ |
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│ └────────────────────────────────────────────────────────────────┘ │ |
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└─────────────────────────────────────────────────────────────────────────┘ |
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``` |
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## 🚀 Key V3.2 Enhancements |
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* **Predictive Self-Awareness:** Estimates success probability and identifies risks *before* attempting a task. |
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* **Skill Taxonomy:** Systematic tracking of 24 core skills across Cognition, Knowledge, Code, Creativity, and Planning. |
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* **Autonomous Learning:** Self-designed, personalized learning paths via the Auto-Curriculum Generator. |
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* **Real-Time Correction:** Proactive error detection during the generation process. |
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* **Boundary Mapping:** Precise identification of capability edges with expansion strategies. |
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## 💻 Usage |
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### Installation |
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```bash |
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pip install torch transformers datasets sagi-swarm |
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``` |
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### Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SAGI") |
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tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/SAGI") |
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# Generate text |
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prompt = "Explain the concept of emergence in swarm intelligence:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=150, |
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temperature=0.7, |
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do_sample=True, |
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pad_token_id=tokenizer.eos_token_id, |
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) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## 📊 Skill Taxonomy (24 Core Skills) |
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* **Cognition:** Pattern recognition, Causal reasoning, Concept formation. |
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* **Knowledge:** Fact retrieval, Knowledge integration, Common sense. |
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* **Code:** Syntax understanding, Algorithm design, Debugging, Optimization. |
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* **Creativity:** Divergent thinking, Novel combination, Generative synthesis. |
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* **Planning:** Goal decomposition, Dependency analysis, Resource allocation. |
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* **Meta-Cognition:** Self-monitoring, Error detection, Strategy selection, Uncertainty quantification. |
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## 🧠 Decision Flow (V3.2) |
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1. **Pre-Assessment:** Predict success, identify risks, recommend strategy. |
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2. **Execution:** Generate with selected strategy. |
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3. **Real-Time Monitoring:** Catch and correct errors during generation. |
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4. **Post-Assessment:** Update skill proficiencies, check boundaries, refine future predictions. |
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5. **Learning:** Update internal models and curricula. |
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## ⚠️ Safety & Limitations |
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* **Experimental Research Prototype:** Not intended for production use. |
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* **Code Execution:** Model includes tool-use capabilities (Python sandbox). Use with caution. |
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* **Intrinsic Motivation:** Self-improving systems may exhibit unpredictable growth patterns. |
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## 📄 License |
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Apache License 2.0 |
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## 📝 Citation |
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```bibtex |
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@software{sagi2026, |
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title={SAGI: Self-Aware General Intelligence System}, |
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author={Reaperdoesntknow}, |
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year={2026}, |
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url={https://huggingface.co/reaperdoesntknow/SAGI}, |
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version={3.2.0} |
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} |
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``` |
