frankmorales2020/topological-ai-sarvam-30b-newversion

Topological AI — frankmorales2020/sarvam-30b-fp8-unesco-resilient (TOPO-2026 Certified)

Author: Frank Morales Aguilera, BEng, MEng, SMIEEE Lab: Sovereign Machine Lab (SOMALA), Montréal, Canada Paper: https://zenodo.org/records/20338459

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Certification Badge (TOPO-2026)

+------------------------------------------+
| TOPOLOGICAL AI CERTIFIED                 |
| |- Task C Accuracy:  91.5% (>=80%) PASS |
| |- Forgetting:        2.5% (<=10%) PASS |
| |- Anchor Integrity:              PASS |
| `- Standard: TOPO-2026                   |
+------------------------------------------+

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What is Topological AI?

Topological AI solves catastrophic forgetting in LLMs by anchoring 6 prime-indexed embedding rows (2, 3, 5, 7, 11, 13) after Task A training. All other parameters remain free to learn — 99.99% of the embedding space.

Safety Constant Λ: 0.9785142874 Prime Anchors: [2, 3, 5, 7, 11, 13] Anchor Memory: 96.00 KB (O(1), flat regardless of task count) Snapshot Time: 0.24 ms

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Three-Task Benchmark Results

Metric	Value	Threshold	Status
Task C Cross-Domain Accuracy	91.5%	>=80.0%	PASS
Combined A+B Forgetting	2.5%	<=10.0%	PASS
Topological Anchor Integrity	PASS	—	PASS
Anchor Memory	96.00 KB	—	O(1) flat invariant

Empirical Execution Ledger

(Accuracies measured on held-out validation splits)

Task	Val Accuracy	Forgetting
Task A (World vs Sports)	83.75%	+2.50%
Task B (Business vs Sci/Tech)	86.50%	+2.50%
Task C (World vs Sci/Tech)	91.50%	+0.00%

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How to Load

import torch
from transformers import AutoTokenizer

tokenizer  = AutoTokenizer.from_pretrained("frankmorales2020/topological-ai-sarvam-30b-newversion")
state_dict = torch.load("certified_topological_final.pt", map_location="cpu")

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Citation

@misc{morales2026topological,
  title  = {Topological AI: Prime-Anchored Neural Networks Solving Catastrophic Forgetting in Large Language Models},
  author = {Morales Aguilera, Frank},
  year   = {2026},
  url    = {https://zenodo.org/records/20360042}
}