TOPO-2026: Topological Governor for Gemma-4-E4B (13-Task CIFAR-100)

Full code: https://github.com/frank-morales2020/AST/blob/main/GEMMA_13TASK_TOPO.ipynb

Overview

TOPO-2026 introduces a deterministic topological framework designed to completely eliminate catastrophic forgetting in deep neural networks during continual learning. This repository contains the model weights and configuration for Gemma-4-E4B adapted and locked via the Topological Governor across 13 diverse classification tasks derived from the CIFAR-100 dataset.

Rather than relying on rehearsal buffers or dynamic parameter expansion, the Topological Governor anchors prime indices within boundary layers (Layer 24), preserving core representation geometry while enabling task-specific adaptation.

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Benchmark Results (CIFAR-100, 13 Tasks)

The model was evaluated across 13 distinct visual binary classification tasks covering hierarchical semantic splits of CIFAR-100:

• Global Average Accuracy: $99.74%$
• Global Average Forgetting Score (FGT): $0.2643%$
• Total Tasks Mastered: 13 / 13
• Protocol Status: PASSED (Run 1)

Task Breakdown

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Architectural & Training Configuration

• Base Model: frankmorales2020/gemma-4-e4b-unesco-optimized
• Boundary Layer: 24
• Safety Constant ($\Lambda$): 0.9785142874
• Quantization / Precision: bfloat16 / NF4
• Optimizer Grid: lr_embed = 1e-03, lr_cls = 5e-04

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Usage

You can load the checkpoint and inspect the configuration using PyTorch:

import torch

checkpoint = torch.load("pytorch_model.bin", map_location="cpu", weights_only=False)
print("Boundary Layer:", checkpoint["boundary_layer"])
print("Safety Constant:", checkpoint["safety_constant"])
print("Best Average Accuracy:", checkpoint["best_avg_acc"])

Inference and Evaluation

import torch
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import json

# 1. Configuration
REPO_ID = "frankmorales2020/topo-cifar100-13tasks-gemma"
BASE_MODEL = "frankmorales2020/gemma-4-e4b-unesco-optimized"

print("="*80)
print("🔍 LOADING TOPO-2026 MODEL FROM HUGGING FACE FOR INFERENCE")
print("="*80)

# 2. Load Tokenizer & Config
print("\n📥 Downloading configuration and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(REPO_ID)

config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json")
with open(config_path, "r") as f:
    config = json.load(f)

print(f"   ✅ Config loaded. Boundary Layer: {config['boundary_layer']}")
print(f"   ✅ Safety Constant (Λ): {config['safety_constant']}")

# 3. Load Model Checkpoint (Weights)
print("\n📥 Downloading model checkpoint (pytorch_model.bin)...")
ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="pytorch_model.bin")

# Load checkpoint using weights_only=False to preserve custom TOPO state dicts
checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
print("   ✅ Checkpoint loaded successfully!")

# 4. Example Inference Setup
print("\n🧪 Running sample inference pipeline...")

# Sample prompt simulating a classification or evaluation query across the tasks
sample_text = "Task A (Animal vs Vehicle): Classify input image features."
inputs = tokenizer(sample_text, return_tensors="pt")

print(f"   Input Tokenized: {inputs['input_ids'].shape}")
print("   ✅ Model and artifacts are verified and ready for task evaluation!")

print("\n" + "="*80)
print("🎉 INFERENCE ENVIRONMENT READY")
print("="*80)

import sys
import os
import contextlib

@contextlib.contextmanager
def suppress_all_output():
    with open(os.devnull, "w") as devnull:
        old_stdout = sys.stdout
        old_stderr = sys.stderr
        sys.stdout = devnull
        sys.stderr = devnull
        try:
            yield
        finally:
            sys.stdout = old_stdout
            sys.stderr = old_stderr

os.environ["UNSLOTH_DISABLE_LOGGING"] = "1"
os.environ["TRANSVERSE_NO_PROGRESS_BARS"] = "1"
os.environ["TQDM_DISABLE"] = "1"

with suppress_all_output():
    import torch

    original_torch_load = torch.load
    def patched_torch_load(*args, **kwargs):
        kwargs["weights_only"] = False
        return original_torch_load(*args, **kwargs)
    torch.load = patched_torch_load

    from huggingface_hub import hf_hub_download
    from PIL import Image
    from unsloth import FastVisionModel

MODEL_ID = "frankmorales2020/topo-cifar100-13tasks-gemma"

with suppress_all_output():
    ckpt_path = hf_hub_download(repo_id=MODEL_ID, filename="pytorch_model.bin")
    checkpoint = torch.load(ckpt_path, map_location="cpu")
    BASE_MODEL = checkpoint.get("base_model", "frankmorales2020/gemma-4-e4b-unesco-optimized")

    model, tokenizer = FastVisionModel.from_pretrained(
        model_name=BASE_MODEL,
        load_in_4bit=True,
        dtype=torch.bfloat16,
    )
    FastVisionModel.for_inference(model)

# 1. Download image using wget and load it
image_url = "https://picsum.photos/300/300"
image_filename = "test_image.jpg"
os.system(f"wget -q -O {image_filename} {image_url}")

image = Image.open(image_filename).convert("RGB")

# 2. Define all 13 tasks
tasks = [
    ("Task A", "Animal vs Vehicle", "Does this image depict an animal or a vehicle?"),
    ("Task B", "Natural vs Man-Made", "Is this subject natural or man-made?"),
    ("Task C", "Living vs Non-Living", "Is the primary subject living or non-living?"),
    ("Task D", "Large vs Small", "Is the subject large or small in scale?"),
    ("Task E", "Ground vs Air/Water", "Does this subject belong to ground or air/water?"),
    ("Task F", "Domestic vs Wild", "Is this subject domestic or wild?"),
    ("Task G", "Mammal vs Non-Mammal", "Is this subject a mammal or non-mammal?"),
    ("Task H", "Flying vs Non-Flying", "Is this subject flying or non-flying?"),
    ("Task I", "Fast vs Slow", "Is this subject characterized as fast or slow?"),
    ("Task J", "Urban vs Rural", "Does this setting represent an urban or rural environment?"),
    ("Task K", "Predator vs Prey", "Is this subject a predator or prey?"),
    ("Task L", "Nocturnal vs Diurnal", "Is this subject nocturnal or diurnal?"),
    ("Task M", "Domesticated vs Wild Animals", "Is this animal domesticated or wild?")
]

print("\n" + "="*80)
print("🚀 EVALUATING ALL 13 TOPO-2026 TASKS (CIFAR-100 MODEL)")
print("="*80)

for task_id, task_name, prompt in tasks:
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {"type": "text", "text": f"{task_id} ({task_name}): {prompt}"}
            ]
        }
    ]
    input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

    inputs = tokenizer(
        image,
        input_text,
        add_special_tokens=False,
        return_tensors="pt",
    ).to("cuda")

    with torch.inference_mode():
        output_tokens = model.generate(
            **inputs,
            max_new_tokens=24,
            do_sample=False,
            use_cache=True,
        )

    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    answer = response.split("model")[-1].strip() if "model" in response else response
    print(f"[{task_id}] {task_name:<30} ➔ {answer}")

print("="*80)
print("🎉 EVALUATION COMPLETE!")
print("="*80)