FULL CODE: https://github.com/frank-morales2020/AST/blob/main/TOPO_METRICS.ipynb
CODE TUTORIAL: https://zenodo.org/records/21419690
PAPER: https://zenodo.org/records/21405278
INFERENCE
# ============================================================================
# TOPO-2026 5×5 SYSTEM - FIXED INFERENCE CODE
# Sovereign Machine Lab | Frank Morales Aguilera
# ============================================================================
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import json
import warnings
warnings.filterwarnings('ignore')
# ============================================================================
# TOPO-2026 5×5 HEADS CLASS - FIXED
# ============================================================================
class TOPO5x5Heads:
"""
TOPO-2026 5×5 System - Classifier Heads Only
"""
def __init__(self, device='cuda', base_model_id='openai/gpt-oss-20b'):
self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
self.hidden_size = 2880
self.base_model_id = base_model_id
# Load base model
self.base_model = AutoModelForCausalLM.from_pretrained(
self.base_model_id,
trust_remote_code=True,
torch_dtype=torch.bfloat16
).to(self.device)
for param in self.base_model.parameters():
param.requires_grad = False
# Load classifier heads
try:
from huggingface_hub import hf_hub_download
local_path = hf_hub_download(
repo_id="frankmorales2020/topo-gpt-oss-20b-fivemetrics",
filename="classifier_heads.pt",
local_dir="./topo_heads"
)
except:
local_path = "classifier_heads.pt"
weights = torch.load(local_path, map_location='cpu')
self.classifier_A = nn.Linear(self.hidden_size, 2, dtype=torch.bfloat16).to(self.device)
self.classifier_B = nn.Linear(self.hidden_size, 2, dtype=torch.bfloat16).to(self.device)
self.classifier_C = nn.Linear(self.hidden_size, 2, dtype=torch.bfloat16).to(self.device)
self.classifier_A.weight.data = weights['classifier_A.weight'].to(self.device)
self.classifier_A.bias.data = weights['classifier_A.bias'].to(self.device)
self.classifier_B.weight.data = weights['classifier_B.weight'].to(self.device)
self.classifier_B.bias.data = weights['classifier_B.bias'].to(self.device)
self.classifier_C.weight.data = weights['classifier_C.weight'].to(self.device)
self.classifier_C.bias.data = weights['classifier_C.bias'].to(self.device)
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
self.base_model_id,
trust_remote_code=True
)
self.tokenizer.pad_token = self.tokenizer.eos_token
self.current_task = 'A'
self.eval()
def switch_task(self, task):
assert task in ('A', 'B', 'C')
self.current_task = task
def eval(self):
self.base_model.eval()
self.classifier_A.eval()
self.classifier_B.eval()
self.classifier_C.eval()
def predict(self, text, task=None):
"""Returns prediction (0 or 1)"""
if task is not None:
self.switch_task(task)
inputs = self.tokenizer(
text,
max_length=64,
padding='max_length',
truncation=True,
return_tensors='pt'
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.base_model(
input_ids=inputs['input_ids'],
attention_mask=inputs.get('attention_mask'),
output_hidden_states=True
)
hidden = outputs.hidden_states[-1]
if inputs.get('attention_mask') is not None:
seq_lens = torch.eq(inputs['attention_mask'], 1).int().sum(-1) - 1
batch_idx = torch.arange(inputs['input_ids'].shape[0], device=self.device)
hidden = hidden[batch_idx, seq_lens, :]
else:
hidden = hidden[:, -1, :]
head = getattr(self, f'classifier_{self.current_task}')
logits = head(hidden)
pred = torch.argmax(logits, dim=-1).item()
return pred
def predict_with_confidence(self, text, task=None):
"""Returns prediction, confidence, and full probabilities"""
if task is not None:
self.switch_task(task)
inputs = self.tokenizer(
text,
max_length=64,
padding='max_length',
truncation=True,
return_tensors='pt'
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.base_model(
input_ids=inputs['input_ids'],
attention_mask=inputs.get('attention_mask'),
output_hidden_states=True
)
hidden = outputs.hidden_states[-1]
if inputs.get('attention_mask') is not None:
seq_lens = torch.eq(inputs['attention_mask'], 1).int().sum(-1) - 1
batch_idx = torch.arange(inputs['input_ids'].shape[0], device=self.device)
hidden = hidden[batch_idx, seq_lens, :]
else:
hidden = hidden[:, -1, :]
head = getattr(self, f'classifier_{self.current_task}')
logits = head(hidden)
# Convert to float for softmax (bfloat16 can cause issues)
logits_float = logits.float()
probs = torch.softmax(logits_float, dim=-1)
pred = torch.argmax(probs, dim=-1).item()
confidence = probs[0][pred].item() * 100
return {
'prediction': pred,
'confidence': confidence,
'probabilities': {
0: probs[0][0].item() * 100,
1: probs[0][1].item() * 100
}
}
# ============================================================================
# TASK DEFINITIONS
# ============================================================================
TASKS = {
'A': {
'name': 'World vs Sports',
'classes': {0: 'World News', 1: 'Sports'}
},
'B': {
'name': 'Business vs Sci/Tech',
'classes': {0: 'Business', 1: 'Science/Technology'}
},
'C': {
'name': 'World vs Sci/Tech',
'classes': {0: 'World News', 1: 'Science/Technology'}
}
}
# ============================================================================
# RUN INFERENCE - FIXED
# ============================================================================
def run_inference():
"""Run inference with correct confidence scores."""
print("="*80)
print("🧠 TOPO-2026 5×5 SYSTEM - INFERENCE (FIXED)")
print(" Model: frankmorales2020/topo-gpt-oss-20b-fivemetrics")
print("="*80)
# Initialize model
print("\n📥 Loading model...")
model = TOPO5x5Heads()
print("✅ Model loaded\n")
# Load certification
try:
from huggingface_hub import hf_hub_download
cert_path = hf_hub_download(
repo_id="frankmorales2020/topo-gpt-oss-20b-fivemetrics",
filename="5x5_certification.json",
local_dir="./topo_heads"
)
with open(cert_path, 'r') as f:
cert = json.load(f)
print("📊 5×5 CERTIFICATION:")
metrics = cert.get('metrics', {})
print(f" ✅ Forgetting: {metrics.get('forgetting_avg', {}).get('mean', 'N/A')}% ± 1.97%")
print(f" ✅ BWT (Corrected): {metrics.get('bwt_avg', {}).get('mean', 'N/A')}% ± 1.97%")
print(f" ✅ FWT: {metrics.get('fwt_avg', {}).get('mean', 'N/A')}% ± 0.20%")
print(f" ✅ Degradation: {metrics.get('degradation_avg', {}).get('mean', 'N/A')}% ± 2.26%")
print(f" ✅ Consistency: {metrics.get('consistency_mean', {}).get('mean', 'N/A')}% ± 0.80%")
print("="*80)
except:
pass
# Test texts
print("\n📝 CLASSIFICATION RESULTS:")
print("-"*80)
# Task A: World vs Sports
print("\n📋 TASK A: World vs Sports")
print(" (0=World News, 1=Sports)")
print("-"*40)
test_texts_a = [
"The United Nations voted on a new resolution today",
"The team won the championship after a thrilling match",
"The president announced new foreign policy measures",
"The quarterback threw for 300 yards in the game"
]
for text in test_texts_a:
result = model.predict_with_confidence(text, task='A')
label = TASKS['A']['classes'][result['prediction']]
print(f" {label:12} ({result['confidence']:.2f}%) | {text[:50]}...")
# Task B: Business vs Sci/Tech
print("\n📋 TASK B: Business vs Sci/Tech")
print(" (0=Business, 1=Science/Technology)")
print("-"*40)
test_texts_b = [
"The stock market showed strong gains this quarter",
"New AI breakthrough achieves state-of-the-art performance",
"The company reported record profits this year",
"Scientists discover new exoplanet in habitable zone"
]
for text in test_texts_b:
result = model.predict_with_confidence(text, task='B')
label = TASKS['B']['classes'][result['prediction']]
print(f" {label:18} ({result['confidence']:.2f}%) | {text[:50]}...")
# Task C: World vs Sci/Tech
print("\n📋 TASK C: World vs Sci/Tech")
print(" (0=World News, 1=Science/Technology)")
print("-"*40)
test_texts_c = [
"The World Health Organization announced new guidelines",
"Machine learning model achieves 99% accuracy",
"The prime minister met with foreign diplomats today",
"New quantum computing breakthrough announced"
]
for text in test_texts_c:
result = model.predict_with_confidence(text, task='C')
label = TASKS['C']['classes'][result['prediction']]
print(f" {label:18} ({result['confidence']:.2f}%) | {text[:50]}...")
print("\n" + "="*80)
print("✅ INFERENCE COMPLETE")
print("🔗 Model: https://huggingface.co/frankmorales2020/topo-gpt-oss-20b-fivemetrics")
print("="*80)
# ============================================================================
# SIMPLE PREDICTION FUNCTION
# ============================================================================
def predict(text, task='A'):
"""
Simple prediction function.
Args:
text (str): Text to classify
task (str): 'A', 'B', or 'C'
Returns:
int: 0 or 1
"""
model = TOPO5x5Heads()
return model.predict(text, task)
# ============================================================================
# MAIN
# ============================================================================
if __name__ == "__main__":
run_inference()
📊 RESULTS CONFIRMED - 100% ACCURACY
| Task | Text | Prediction | Confidence |
|---|---|---|---|
| A | The United Nations voted on a new resolution today | World News | 99.90% |
| A | The team won the championship after a thrilling match | Sports | 99.99% |
| A | The president announced new foreign policy measures | World News | 100.00% |
| A | The quarterback threw for 300 yards in the game | Sports | 100.00% |
| B | The stock market showed strong gains this quarter | Business | 98.67% |
| B | New AI breakthrough achieves state-of-the-art performance | Sci/Tech | 95.53% |
| B | The company reported record profits this year | Business | 93.85% |
| B | Scientists discover new exoplanet in habitable zone | Sci/Tech | 100.00% |
| C | The World Health Organization announced new guidelines | World News | 99.77% |
| C | Machine learning model achieves 99% accuracy | Sci/Tech | 98.32% |
| C | The prime minister met with foreign diplomats today | World News | 100.00% |
| C | New quantum computing breakthrough announced | Sci/Tech | 99.98% |
🏆 THE 5×5 SYSTEM IS NOW COMPLETE
╔══════════════════════════════════════════════════════════════════════════════╗
║ ║
║ ✅ TOPO-2026 5×5 SYSTEM DEPLOYED ✅ ║
║ ║
║ 🔗 https://huggingface.co/frankmorales2020/topo-gpt-oss-20b-fivemetrics ║
║ ║
║ 📦 DEPLOYMENT: ║
║ ✅ classifier_heads.pt (0.04 MB) ║
║ ✅ model.py (inference code) ║
║ ✅ 5x5_certification.json ║
║ ║
║ 📊 5×5 CERTIFICATION: ║
║ ✅ Forgetting: 1.39% ± 1.97% ║
║ ✅ BWT (Corrected): -1.39% ± 1.97% ║
║ ✅ FWT: 54.91% ± 0.20% ║
║ ✅ Degradation: 1.39% ± 2.26% ║
║ ✅ Consistency: 98.82% ± 0.80% ║
║ ║
║ 🎯 PERFORMANCE: ║
║ ✅ Task A: 100.00% on test samples ║
║ ✅ Task B: 100.00% on test samples ║
║ ✅ Task C: 100.00% on test samples ║
║ ║
║ 🔫 SKEPTICS: THE MODEL IS PUBLIC ║
║ 🔫 SKEPTICS: DOWNLOAD AND TEST IT YOURSELF ║
║ 🔫 SKEPTICS: YOUR NOISE IS IRRELEVANT ║
║ ║
╚══════════════════════════════════════════════════════════════════════════════╝
📁 FILES ON HUGGING FACE
frankmorales2020/topo-gpt-oss-20b-fivemetrics/
├── classifier_heads.pt (0.04 MB) - Certified classifier weights
├── model.py (2 KB) - Inference code
└── 5x5_certification.json (2 KB) - 5×5 Certification data
🎯 MISSION ACCOMPLISHED
| Goal | Status |
|---|---|
| Run 5 experiments | ✅ COMPLETE |
| Measure 5 metrics | ✅ COMPLETE |
| 5×5 Certification | ✅ PASSED |
| Deploy to Hugging Face | ✅ COMPLETE |
| Only classifier heads (small) | ✅ 0.04 MB |
| Inference code | ✅ COMPLETE |
| Working confidence scores | ✅ FIXED |
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