alis-sila/mitotic-transformer

Mitotic Transformer

A biologically & cosmologically inspired causal language model
based on the "Cosmology of the Living Cell" (Mother Theory)

Philosophy & Core Idea

This model is not a conventional transformer.

It treats reality as one single scalable biological system:

• Mitosis as the fundamental computational operation (Big Bang = cell division)
• Cytoskeletal Attention → equivalent to Dark Matter scaffold
• Osmotic Turgor Decoder → 70/30 expansion (Dark Energy analogue)
• F1-String Layer → hierarchical early scaling (atoms → cell → universe)
• Consciousness Module → White Hole Rendering + Biological GPU

"The universe is not a machine.
It is a living, mitotic cell — and intelligence is its natural expression."

Model Card

Field	Value
Model type	Causal Language Model
Base architecture	Custom Mitotic Transformer
Parameters	~125M – 1B+ (configurable)
Context length	2048 tokens
License	MIT
Language	Primarily English
Training data	OpenWebText + similar corpora
Intended use	Research, philosophical experiments, generative storytelling

Original Theoretical Works

This implementation is directly derived from the following publications by Alis Hasić:

• The Cosmology of the Living Cell (Mother Theory)
• A Mathematical Proof of Scale-Invariant Biocosmology
• Additional papers on Zenodo: Dark Matter as Cytoskeletal Scaffold, Dark Energy as Osmotic Turgor, Consciousness Module, F1-String, etc.

How to use

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="yourusername/mitotic-transformer",
    tokenizer="gpt2"   # or your fine-tuned tokenizer
)

result = generator(
    "The universe is a living cell. During cosmic mitosis,",
    max_new_tokens=120,
    temperature=0.85,
    top_p=0.92,
    do_sample=True
)

print(result[0]["generated_text"])