🌌 Geometric LLM (GLLM): The $S^3$ One-Pass Learning Architecture

Welcome to GLLM (Geometric Large Language Model) — a fundamental departure from standard flat-space Transformers.

Current LLMs suffer from catastrophic forgetting and require massive datacenters to learn new information through backpropagation. GLLM solves this by combining a traditional Transformer with a Non-Von Neumann $S^3$ Geometric Genome. It projects language into a unit quaternion manifold, allowing it to "memorize" and retrieve new datasets instantly in a single pass—mimicking the human hippocampus.

🚀 Try it instantly in Google Colab:

Train, Inject Data, and Chat in Colab Here

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🧠 Why is GLLM Different?

1. Topological Memory (The $S^3$ Genome): Standard attention uses flat-space dot products (which wash out context). GLLM computes geodesic distance ($\sigma$) on a 4D quaternion sphere.
2. Instant One-Pass Learning: Want to teach the model a new book, a private dataset, or new code? You do not need to train it. Just pass the text through the model once. It writes the semantic carriers directly into the $S^3$ Genome mesh.
3. Zero Catastrophic Forgetting: Because memories are stored as topological anchors on a sphere, learning a new task does not overwrite the weights of the previous task.
4. BKT Consolidation: The mesh self-organizes. As memories get too crowded, it uses the Berezinskii-Kosterlitz-Thouless (BKT) phase threshold to automatically merge identical concepts (like biological sleep consolidation).

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🛠️ How to use this repository

You can load the pre-trained base model, inject your own data instantly, and chat with it.

1. Load the Model

import torch
from transformers import AutoTokenizer

# Load tokenizer and initialize GLLM
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = HybridGeometricLLM(vocab_size=len(tokenizer), d_model=768, n_heads=12, num_layers=6)

# Download weights from Hugging Face
from huggingface_hub import hf_hub_download
weights_path = hf_hub_download(repo_id="SofiTesfay2010/GLLM", filename="gllm_weights.pt")
model.load_state_dict(torch.load(weights_path))
model.cuda()

2. Instant One-Pass Learning (Add Your Own Dataset)

No optimizers, no backpropagation, no gradients. Just forward passes. You can inject plain text or an entire dataset directly into the model's geometric memory.

from datasets import load_dataset

# Load the model and set to EVAL
model.eval() 

# Example 1: Injecting an ENTIRE Dataset instantly!
my_dataset = load_dataset("squad", split="train[:500]")
texts_to_inject = my_dataset["context"] 

# The model absorbs all 500 articles in seconds without backprop.
instant_learn(texts_to_inject)

📜 Technical Foundation

This architecture is based on the research paper: "The Geometric Computer: Turing Completeness, Free Energy, and Learning in a Digital Brain on $S^3$". By utilizing Hamilton products and Kuramoto critical coupling, the model bridges the gap between quantum states, biology, and autoregressive language modeling.

Created and maintained by SofiTesfay2010.