reaperdoesntknow / SAGI

SAGI - Swarm AGI Language Model

SAGI 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.

Model Description

Key Innovations

• Differentiable Routing : Continuous mixture-of-experts via attention ( DiffRouter ) instead of hard module selection
• Adaptive Gating & Trust : MetaController activates capacity under resource constraints; trust dynamics bias reliable components
• Episodic + Semantic Memory : Dual memory system with trainable retrieval utility
• Curiosity Engine : Injects novel goals when surprise is low, promoting exploration
• Self-Model & Rollback : Predicts state transitions and detects anomalies for self-correction
• Resource Dynamics : Soft conservation with learned converter; cognition consumes/recovers compute, memory, energy
• Value Monitoring : Tracks alignment to core values and freezes plasticity under drift

How It Works

┌─────────────────────────────────────────────────────────┐
│                       SAGI Model                         │
├─────────────────────────────────────────────────────────┤
│  ┌─────────────────┐      ┌─────────────────────────┐   │
│  │   Swarm-7 V2.2  │─────▶│  Swarm State S, T       │   │
│  │  (Cognitive     │      │  (Working Memory)       │   │
│  │   Dynamics)     │      └───────────┬─────────────┘   │
│  └────────▲────────┘                  │                 │
│           │                           ▼                 │
│           │              ┌─────────────────────────┐    │
│           │              │  Transformer Decoder    │    │
│           │              │  - Swarm-conditioned    │    │
│           │              │    attention & FFN      │    │
│           │              │  - RoPE embeddings      │    │
│           │              └───────────┬─────────────┘    │
│           │                          │                  │
│  ┌────────┴────────┐      ┌─────────────────────────┐   │
│  │   Observation   │◀─────│      LM Head            │   │
│  │   (from tokens) │      └─────────────────────────┘   │
│  └─────────────────┘                                    │
└─────────────────────────────────────────────────────────┘

The swarm processes observations derived from token embeddings, updating its internal state S . This state conditions the transformer's attention patterns and feed-forward activations via learned projections, creating bidirectional information flow between symbolic (tokens) and subsymbolic (swarm dynamics) processing.

Usage

Installation

pip install torch transformers datasets

Quick Start

from transformers import AutoTokenizer
from transformers import  AutoModelForCausalLM, AutoConfig

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

# Generate text
model.eval()

prompt = "Once upon a time"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)

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

Model Architecture Details

Swarm Configuration

Resource Budgets

Trust & Plasticity

• Trust Learning Rate : 0.07
• Fast EMA (Plasticity) : 0.10
• Slow EMA (Consolidation) : 0.002
• Core Values : ["truth", "safety", "efficiency"]

Limitations

• Early Research Model : This is an experimental architecture exploring swarm-transformer integration
• Training Data : Currently trained on TinyStories subset; may produce simple, story-like outputs
• Compute Requirements : Swarm dynamics add overhead compared to standard transformers
• Generation Quality : Model is undertrained; outputs may be repetitive or incoherent

Intended Use

This model is intended for:

• Research into multi-agent cognitive architectures
• Exploration of dynamic, adaptive language models
• Educational purposes in understanding swarm intelligence + LLMs

Not intended for:

• Production applications
• Safety-critical systems
• Generation of factual content

Training Details

• Dataset : TinyStories (subset)
• Optimizer : AdamW (lr=3e-4, betas=(0.9, 0.999), weight_decay=0.01)
• Scheduler : Cosine annealing
• Precision : FP32
• Hardware : CPU training (compatible with CUDA)

Citation

@software{sagi2026,
  title={SAGI: Swarm AGI Language Model},
  author={Reaperdoesntknow},
  year={2026},
  url={https://huggingface.co/your-reaperdoesntknow/SAGI}
}