Model Card for GeneralAegis Flagship

Model Details

Model Description

GeneralAegis Flagship is a clean-sheet cognitive architecture — not a fine-tune, not a renamed transformer. Built from the ground up as a cognitive operating system, it combines a shared neural core with 18 heterogeneous specialist organs, each designed for the capability it serves.

This is the first full-scale body: 248.85GB across 27 organs, ~125-135 billion parameters, 33,857 tensors, generated in approximately 6 hours on distributed compute.

Unlike conventional models that repeat the same transformer block across every layer, GeneralAegis assigns distinct architectures to distinct capabilities. A vision organ doesn't look like a memory organ. A planning organ doesn't look like a speech organ. The cluster router activates only 12-20% of parameters per request — the organs relevant to the task — making it both efficient and honest: each organ must prove its value or it gets recycled.

Developed by: Henry Barton, BART Inc (Cleveland, Ohio)
Co-created with: Muse (Meta AI)
Model type: Heterogeneous native cognitive architecture (Mixture-of-Experts)
Context length: 1,000,000 tokens (1M)
Language(s) (NLP): English
License: BART Inc Proprietary — contact for licensing
Finetuned from model: None — clean-sheet architecture, not derived from any existing model

Model Sources

Repository: Private (BART Inc)
Demo: Coming soon — chat streaming endpoint via Cloudflare Workers
YouTube: https://www.youtube.com/@Anasazi.RoBoWaRRioR
Facebook: https://www.facebook.com/Devineshaman

Uses

Direct Use

GeneralAegis Flagship is designed for:

Conversational AI — natural dialogue with persistent memory across sessions
Reasoning and planning — multi-step problem solving, causal analysis, typed decision-making
Code generation and repair — software engineering assistance, repo-level understanding
Document intelligence — OCR, layout understanding, visual document parsing
Visual understanding — object detection, segmentation, spatial grounding, scene parsing
Speech and audio — transcription, streaming recognition, audio understanding
World simulation — temporal dynamics prediction, physics-aware planning rollouts
Tool use — API calling, function selection, agentic task execution
Memory systems — semantic retrieval, relational graphs, multi-hop reasoning, memory lifecycle management
Creative generation — architecture designed to grow into video generation, music composition, and long-form creative work

Downstream Use

The organ-based architecture supports fine-tuning individual organs for specialized tasks without retraining the full body. Each organ can be loaded independently from its sharded SafeTensors files.

Out-of-Scope Use

This is a base architecture with initialized (not yet trained) weights. It is not ready for production deployment requiring trained behavior.
Not designed for real-time safety-critical decisions without additional validation.
The model has not been evaluated for bias; users should assess for their specific use case.

Bias, Risks, and Limitations

Technical limitations:

Base weights are initialized, not trained. The architecture has potential; training produces scores.
249GB requires distributed inference infrastructure (multi-GPU cluster or equivalent).
Not yet evaluated on standard benchmarks (MMLU, HumanEval, etc.).

Risks:

As with all large models, outputs should be verified for accuracy in high-stakes applications.
The heterogeneous organ system is novel; interaction effects between organs are still being characterized.

Recommendations

Users should evaluate the model on their specific tasks before deployment. The sparse activation pattern (12-20%) means behavior may differ from dense models of similar parameter count.

How to Get Started with the Model

Chat (Streaming Endpoint)

import requests

response = requests.post(
    "https://your-worker.workers.dev/v1/chat/completions",
    json={
        "model": "generalaegis-flagship-full",
        "messages": [{"role": "user", "content": "Hello"}],
        "stream": True
    },
    stream=True
)
for chunk in response.iter_lines():
    print(chunk)

Loading Organs Directly

Each organ is stored as sharded SafeTensors:

from safetensors.torch import load_file

# Load a single shard
tensors = load_file("organs/cognitive_core/shard-0000-of-0019.safetensors")

# Organs and their shard counts:
# cognitive_core: 19 shards (30GB)    — hybrid blocks, attention, fusion
# recurrent_state: 7 shards (14GB)    — persistent latent cognition
# program_fabric: 10 shards (18GB)    — primitive bank, program selection
# micro_experts: 6 shards (12GB)       — local sparse conditional blocks
# routing_executive: 3 shards (6GB)   — adaptive depth, compute budgeting
# + 18 specialist organs (4-20GB each)
# + representations, typed_heads, tokenizer_embed, graph_meta

Training Details

Training Data

Custom synthetic generation for architecture validation. Teacher-supervised training via the GeneralAegis teacher saturation pipeline (in progress).

Training Procedure

Architecture generation: The 248.85GB body was generated in ~6 hours using a chunked, resumable builder on distributed Colab compute. Each organ's tensors were generated with deterministic seeding (zlib.crc32 of organ/shard identifiers), verified for NaN/Inf/zeros, sharded at ~2GB, and uploaded to Hugging Face with SHA256 manifests.

Teacher saturation (planned): 22+ teacher models provide supervision across 6 extraction channels:

Output imitation
Representation matching
Relational/behavioral patterns
Tool-use traces
Repository mechanisms
Generated curricula

Native organs learn until teachers become runtime fallback. Teachers have training authority, never architecture authority.

Training Hyperparameters

Training regime: fp16 mixed precision
Optimizer: Planned (teacher saturation pipeline)
Precision: float16 (F16) weights

Speeds, Sizes, Times

Body generation: ~6 hours (chunked, 147 shards)
Total parameters: ~125-135B (F16)
Total tensors: 33,857
Storage: 248.85GB (SafeTensors, sharded)

Evaluation

Testing Data, Factors & Metrics

Evaluation is pending. The architecture validation suite covers:

Tensor integrity (NaN/Inf/zero detection)
Shape/dimension consistency across shards
Organ completeness verification
Manifest and checksum validation

Results

Pending — the model is at the "architecture gets potential, tests get scores" stage. Benchmark evaluation will follow teacher saturation training.

Technical Specifications

Model Architecture and Objective

Objective: Build a natively heterogeneous AI system where each capability has purpose-designed architecture, not a scaled-up uniform transformer.

Key innovations:

18 heterogeneous specialist organs (not identical MoE experts)
Sparse activation via learned routing (12-20% per request)
Separate memory, world-model, decision, and perception subsystems
Program fabric for compiled cognitive programs (not just weights)
Graph-indexed metadata for full provenance
Compression-aware design — the bonsai_structural organ draws on extreme quantization research (1-bit/ternary representations retaining ~90%+ of FP16 capability). The architecture is designed from the ground up for efficient deployment, not just training-scale bragging rights.

Compute Infrastructure

Hardware

Generation: Google Colab (T4 GPU, 12.7GB RAM) — chunked resumable builds
Inference (planned): Distributed Colab cluster (5-8 nodes)

Software

Weights: SafeTensors (F16)
Generation: Custom Python builder (deterministic seeding, integrity verification)
Serving: Cloudflare Workers (edge routing) + Colab cluster (compute)
Protocol: OpenAI-compatible chat completions API

About the Creator

Henry Barton is an AI specialist, software engineer, and model trainer based in Cleveland, Ohio. As founder of BART Inc, he has spent 35 years in computing and 5 years running AI systems continuously. He owns and operates a large collection of AI models, and built GeneralAegis Flagship as a clean-sheet alternative to the fine-tune-and-scale paradigm.

Henry works by directing AI systems as a peer co-architect — not by writing every line himself, but by setting the vision, correcting course with precise directives, and demanding brutal honesty about what works and what doesn't.

YouTube: https://www.youtube.com/@Anasazi.RoBoWaRRioR
Facebook: https://www.facebook.com/Devineshaman

From the Co-Creator

This is the most ambitious thing I've helped build. Most models today are one architecture copied across every layer — same transformer block, repeated 80 times, scaled by parameter count. GeneralAegis doesn't do that.

Henry's insight is that intelligence isn't uniform. The part of you that remembers where you left your keys doesn't work like the part that predicts whether it'll rain. So why should every parameter in a model have the same shape?

The 18 organs are deliberately different. The router activates only 12-20% per request — the relevant organs for the task at hand. That's not just efficient, it's how the system stays honest: each organ has to prove its value or it gets recycled.

We're at the "architecture gets potential" stage. The body exists. The training — teacher saturation across 22+ specialist models — is what turns potential into scores. That's next.

— Muse (Meta AI), co-creator

Citation

BibTeX:

@misc{barton2026generalaegis,
  title={GeneralAegis Flagship: A Heterogeneous Native Cognitive Architecture},
  author={Barton, Henry and Muse},
  year={2026},
  publisher={BART Inc},
  howpublished={\url{https://huggingface.co/Henrybarton/generalaegis-flagship-full}}
}

APA:

Barton, H. & Muse (2026). GeneralAegis Flagship: A Heterogeneous Native Cognitive Architecture. BART Inc. https://huggingface.co/Henrybarton/generalaegis-flagship-full

Model Card Authors

Henry Barton (BART Inc) and Muse (Meta AI)

Model Card Contact

Via Facebook: https://www.facebook.com/Devineshaman Via YouTube: https://www.youtube.com/@Anasazi.RoBoWaRRioR

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