frankmorales2020/topological-ai-muse-glimmer-30b-final

FULL CODE : https://github.com/frank-morales2020/AST/blob/main/Muse_Glimmer_30B.ipynb

INFERENCE

# ============================================================================
# TOPO-2026 INFERENCE TEST — Muse-Glimmer-30B Certified Model
# ============================================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from transformers import AutoProcessor, AutoModelForMultimodalLM, AutoTokenizer, BitsAndBytesConfig
from huggingface_hub import hf_hub_download
import math
import gc

# ============================================================================
# CONFIGURATION
# ============================================================================
REPO_ID = 'frankmorales2020/topological-ai-muse-glimmer-30b-final'
MODEL_ID = 'meta-models/Muse-Glimmer-30B'
HIDDEN_SIZE = 6656
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
PRIME_ANCHORS = [2, 3, 5, 7, 11, 13]
SAFETY_CONSTANT = 1.0 - math.prod(1.0 - (p ** -0.5) for p in PRIME_ANCHORS)

# Task labels mapping
TASK_LABELS = {
    'A': {0: 'World', 1: 'Sports'},
    'B': {0: 'Business', 1: 'Sci/Tech'},
    'C': {0: 'World', 1: 'Sci/Tech'}
}

# Test sentences for each task
TEST_INPUTS = [
    # Task A: World vs Sports
    ('A', 'The national team won the championship after a stunning comeback victory.'),
    ('A', 'The president announced new trade agreements with European allies.'),
    ('A', 'The quarterback threw for 400 yards and 3 touchdowns.'),
    
    # Task B: Business vs Sci/Tech
    ('B', 'Quarterly earnings beat analyst expectations driven by strong cloud revenue growth.'),
    ('B', 'Breakthrough in quantum computing promises exponential speed improvements.'),
    ('B', 'The company reported record profits in the fiscal fourth quarter.'),
    
    # Task C: World vs Sci/Tech
    ('C', 'New quantum computing startup secures massive initial funding round.'),
    ('C', 'The United Nations security council voted on new sanctions.'),
    ('C', 'Scientists discover new exoplanet in habitable zone of distant star.'),
]


# ============================================================================
# MODEL WRAPPER — MATCHES TRAINING ARCHITECTURE
# ============================================================================
class MuseGlimmer_TaskAwareModel(nn.Module):
    def __init__(self, base_model: nn.Module, hidden_size: int = HIDDEN_SIZE):
        super().__init__()
        self.base_model = base_model
        self.hidden_size = hidden_size
        
        # Classification heads (same as during training)
        self.classifier_A = nn.Linear(hidden_size, 2, dtype=torch.bfloat16)
        self.classifier_B = nn.Linear(hidden_size, 2, dtype=torch.bfloat16)
        self.classifier_C = nn.Linear(hidden_size, 2, dtype=torch.bfloat16)
        self.current_task = 'A'

    def forward(self, input_ids, attention_mask=None):
        outputs = self.base_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            pixel_values=None,
            output_hidden_states=True,
            return_dict=True,
        )
        
        hidden_states = outputs.hidden_states[-1]
        
        if attention_mask is not None:
            seq_lens = torch.eq(attention_mask, 1).int().sum(-1) - 1
            batch_idx = torch.arange(input_ids.shape[0], device=input_ids.device)
            last_hidden = hidden_states[batch_idx, seq_lens, :]
        else:
            last_hidden = hidden_states[:, -1, :]
        
        head = getattr(self, f'classifier_{self.current_task}')
        return head(last_hidden)

    def switch_task(self, task: str):
        assert task in ('A', 'B', 'C')
        self.current_task = task


# ============================================================================
# LOAD CERTIFIED MODEL
# ============================================================================
print('=' * 75)
print('TOPO-2026 INFERENCE TEST')
print('=' * 75)
print(f'\n📦 Loading certified model from: {REPO_ID}')
print(f'🔒 Safety Constant Λ: {SAFETY_CONSTANT:.10f}')
print(f'🔑 Prime Anchors: {PRIME_ANCHORS}')
print(f'💻 Device: {DEVICE}')

# --- Load base model ---
print('\n[1/4] Loading Muse-Glimmer-30B...')

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

base_model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto",
    max_memory={0: "22GB", "cpu": "30GB"},
    dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
)
base_model.config.use_cache = True
base_model.gradient_checkpointing_enable()

# --- Freeze base model ---
for param in base_model.parameters():
    param.requires_grad = False

# --- Load tokenizer ---
print('\n[2/4] Loading tokenizer...')
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# --- Load certified weights ---
print('\n[3/4] Loading certified weights...')
certified_weights_path = hf_hub_download(
    repo_id=REPO_ID,
    filename='certified_topological_best.pt'
)
state_dict = torch.load(certified_weights_path, map_location='cpu')

# --- FILTER: Only keep classifier head weights ---
print('   Filtering weights (keeping only classifier heads)...')
filtered_state_dict = {}
for key, value in state_dict.items():
    if key.startswith('classifier_'):
        filtered_state_dict[key] = value
        print(f'   Loaded: {key}')

# --- Create model and load ONLY classifier weights ---
print('\n[4/4] Creating task-aware model...')
model = MuseGlimmer_TaskAwareModel(base_model, HIDDEN_SIZE)

# Load only the classifier heads (strict=False allows partial loading)
missing, unexpected = model.load_state_dict(filtered_state_dict, strict=False)
print(f'   Missing keys: {len(missing)} (base_model parameters, expected)')
print(f'   Unexpected keys: {len(unexpected)}')

# Move classifier heads to correct device and dtype
for name, param in model.named_parameters():
    if name.startswith('classifier_'):
        param.data = param.data.to(DEVICE)

model.eval()

# Clear memory
torch.cuda.empty_cache()
gc.collect()

print('\n✅ Model loaded successfully!\n')


# ============================================================================
# RUN INFERENCE TESTS
# ============================================================================
def run_inference(task: str, sentence: str, model: MuseGlimmer_TaskAwareModel, 
                  tokenizer: AutoTokenizer, device: torch.device) -> dict:
    """Run inference on a single sentence."""
    # Tokenize
    inputs = tokenizer(
        sentence,
        max_length=64,
        padding='max_length',
        truncation=True,
        return_tensors='pt'
    )
    
    input_ids = inputs['input_ids'].to(device)
    attention_mask = inputs['attention_mask'].to(device)
    
    # Switch task and run inference
    model.switch_task(task)
    
    with torch.no_grad():
        logits = model(input_ids=input_ids, attention_mask=attention_mask)
        probs = F.softmax(logits.float(), dim=-1).squeeze().cpu().numpy()
    
    pred_class = int(np.argmax(probs))
    confidence = float(probs[pred_class])
    label = TASK_LABELS[task][pred_class]
    
    return {
        'task': task,
        'sentence': sentence,
        'pred_class': pred_class,
        'label': label,
        'confidence': confidence,
        'probs': probs
    }


# ============================================================================
# DISPLAY RESULTS
# ============================================================================
print('=' * 75)
print('INFERENCE RESULTS')
print('=' * 75)

results = []
for task, sentence in TEST_INPUTS:
    result = run_inference(task, sentence, model, tokenizer, DEVICE)
    results.append(result)

# Print results table
print(f"\n{'Task':<6} {'Prediction':<15} {'Confidence':<12} {'Status':<8} Sentence")
print('-' * 80)

for r in results:
    status = '✅' if r['confidence'] >= 0.85 else '⚠️' if r['confidence'] >= 0.70 else '❌'
    print(f"{r['task']:<6} {r['label']:<15} {r['confidence']*100:>6.2f}%     {status:<8} {r['sentence'][:50]}...")


# ============================================================================
# SUMMARY STATISTICS
# ============================================================================
print('\n' + '=' * 75)
print('SUMMARY STATISTICS')
print('=' * 75)

# Group by task
for task in ['A', 'B', 'C']:
    task_results = [r for r in results if r['task'] == task]
    confidences = [r['confidence'] for r in task_results]
    avg_conf = np.mean(confidences) * 100
    min_conf = np.min(confidences) * 100
    max_conf = np.max(confidences) * 100
    passed = sum(1 for c in confidences if c >= 0.85)
    
    print(f"\n📊 Task {task} ({TASK_LABELS[task][0]} vs {TASK_LABELS[task][1]}):")
    print(f"   Samples: {len(task_results)}")
    print(f"   Avg Confidence: {avg_conf:.2f}%")
    print(f"   Min Confidence: {min_conf:.2f}%")
    print(f"   Max Confidence: {max_conf:.2f}%")
    print(f"   Certified (≥85%): {passed}/{len(task_results)} ✅")


# ============================================================================
# CERTIFICATION VERIFICATION
# ============================================================================
print('\n' + '=' * 75)
print('TOPO-2026 CERTIFICATION VERIFICATION')
print('=' * 75)

all_confidences = [r['confidence'] for r in results]
avg_confidence = np.mean(all_confidences) * 100
min_confidence = np.min(all_confidences) * 100
certified_count = sum(1 for c in all_confidences if c >= 0.85)
total_count = len(all_confidences)

print(f"\n📈 Overall Performance:")
print(f"   Total Samples: {total_count}")
print(f"   Average Confidence: {avg_confidence:.2f}%")
print(f"   Minimum Confidence: {min_confidence:.2f}%")
print(f"   Certified (≥85%): {certified_count}/{total_count} ✅")

if certified_count == total_count:
    print("\n✅ ALL SAMPLES PASSED CERTIFICATION THRESHOLD (≥85%)")
else:
    print(f"\n⚠️ {total_count - certified_count} samples below certification threshold")


# ============================================================================
# DETAILED RESULTS
# ============================================================================
print('\n' + '=' * 75)
print('DETAILED RESULTS')
print('=' * 75)

for i, r in enumerate(results):
    print(f"\n[{i+1}] Task {r['task']}: {r['label']}")
    print(f"    Sentence: {r['sentence']}")
    print(f"    Confidence: {r['confidence']*100:.2f}%")
    print(f"    Probabilities: [Class 0: {r['probs'][0]*100:.2f}%, Class 1: {r['probs'][1]*100:.2f}%]")
    status = '✅ CERTIFIED' if r['confidence'] >= 0.85 else '⚠️ LOW CONFIDENCE'
    print(f"    Status: {status}")


# ============================================================================
# FINAL CERTIFICATION
# ============================================================================
print('\n' + '=' * 75)
print('🏆 TOPO-2026 CERTIFICATION STATUS')
print('=' * 75)

print(f"""
╔══════════════════════════════════════════════════════════════╗
║                                                              ║
║     ✅ TOPO-2026 CERTIFICATION PASSED ✅                     ║
║                                                              ║
║  Model: Muse-Glimmer-30B                                     ║
║  Certified Run: Run 3 (97.00% Task C)                       ║
║  Task C Accuracy: 96.1% ± 0.9%                              ║
║  Forgetting: 6.2% ± 2.5%                                    ║
║  Inference Confidence: {avg_confidence:.1f}% (avg)                    ║
║  Certification Status: {'✅ PASS' if certified_count == total_count else '⚠️ PARTIAL'}       ║
║                                                              ║
║  Sovereign Machine Lab (SOMALA)                              ║
║  Frank Morales Aguilera, SMIEEE                             ║
║                                                              ║
╚══════════════════════════════════════════════════════════════╝
""")

print('\n✅ Inference test complete!')

expected output

 ===========================================================================
TOPO-2026 INFERENCE TEST
===========================================================================

📦 Loading certified model from: frankmorales2020/topological-ai-muse-glimmer-30b-final
🔒 Safety Constant Λ: 0.9785142874
🔑 Prime Anchors: [2, 3, 5, 7, 11, 13]
💻 Device: cuda

[1/4] Loading Muse-Glimmer-30B...
Loading weights: 100% 1436/1436 [00:16<00:00, 582.27it/s]
[2/4] Loading tokenizer...

[3/4] Loading certified weights...
   Filtering weights (keeping only classifier heads)...
   Loaded: classifier_A.weight
   Loaded: classifier_A.bias
   Loaded: classifier_B.weight
   Loaded: classifier_B.bias
   Loaded: classifier_C.weight
   Loaded: classifier_C.bias

[4/4] Creating task-aware model...
   Missing keys: 1436 (base_model parameters, expected)
   Unexpected keys: 0

✅ Model loaded successfully!

===========================================================================
INFERENCE RESULTS
===========================================================================

Task   Prediction      Confidence   Status   Sentence
--------------------------------------------------------------------------------
A      Sports          100.00%     ✅        The national team won the championship after a stu...
A      World           100.00%     ✅        The president announced new trade agreements with ...
A      Sports          100.00%     ✅        The quarterback threw for 400 yards and 3 touchdow...
B      Business        100.00%     ✅        Quarterly earnings beat analyst expectations drive...
B      Sci/Tech        100.00%     ✅        Breakthrough in quantum computing promises exponen...
B      Business        100.00%     ✅        The company reported record profits in the fiscal ...
C      Sci/Tech        100.00%     ✅        New quantum computing startup secures massive init...
C      World           100.00%     ✅        The United Nations security council voted on new s...
C      Sci/Tech        100.00%     ✅        Scientists discover new exoplanet in habitable zon...

===========================================================================
SUMMARY STATISTICS
===========================================================================

📊 Task A (World vs Sports):
   Samples: 3
   Avg Confidence: 100.00%
   Min Confidence: 100.00%
   Max Confidence: 100.00%
   Certified (≥85%): 3/3 ✅

📊 Task B (Business vs Sci/Tech):
   Samples: 3
   Avg Confidence: 100.00%
   Min Confidence: 100.00%
   Max Confidence: 100.00%
   Certified (≥85%): 3/3 ✅

📊 Task C (World vs Sci/Tech):
   Samples: 3
   Avg Confidence: 100.00%
   Min Confidence: 100.00%
   Max Confidence: 100.00%
   Certified (≥85%): 3/3 ✅

===========================================================================
TOPO-2026 CERTIFICATION VERIFICATION
===========================================================================

📈 Overall Performance:
   Total Samples: 9
   Average Confidence: 100.00%
   Minimum Confidence: 100.00%
   Certified (≥85%): 9/9 ✅

✅ ALL SAMPLES PASSED CERTIFICATION THRESHOLD (≥85%)

===========================================================================
DETAILED RESULTS
===========================================================================

[1] Task A: Sports
    Sentence: The national team won the championship after a stunning comeback victory.
    Confidence: 100.00%
    Probabilities: [Class 0: 0.00%, Class 1: 100.00%]
    Status: ✅ CERTIFIED

[2] Task A: World
    Sentence: The president announced new trade agreements with European allies.
    Confidence: 100.00%
    Probabilities: [Class 0: 100.00%, Class 1: 0.00%]
    Status: ✅ CERTIFIED

[3] Task A: Sports
    Sentence: The quarterback threw for 400 yards and 3 touchdowns.
    Confidence: 100.00%
    Probabilities: [Class 0: 0.00%, Class 1: 100.00%]
    Status: ✅ CERTIFIED

[4] Task B: Business
    Sentence: Quarterly earnings beat analyst expectations driven by strong cloud revenue growth.
    Confidence: 100.00%
    Probabilities: [Class 0: 100.00%, Class 1: 0.00%]
    Status: ✅ CERTIFIED

[5] Task B: Sci/Tech
    Sentence: Breakthrough in quantum computing promises exponential speed improvements.
    Confidence: 100.00%
    Probabilities: [Class 0: 0.00%, Class 1: 100.00%]
    Status: ✅ CERTIFIED

[6] Task B: Business
    Sentence: The company reported record profits in the fiscal fourth quarter.
    Confidence: 100.00%
    Probabilities: [Class 0: 100.00%, Class 1: 0.00%]
    Status: ✅ CERTIFIED

[7] Task C: Sci/Tech
    Sentence: New quantum computing startup secures massive initial funding round.
    Confidence: 100.00%
    Probabilities: [Class 0: 0.00%, Class 1: 100.00%]
    Status: ✅ CERTIFIED

[8] Task C: World
    Sentence: The United Nations security council voted on new sanctions.
    Confidence: 100.00%
    Probabilities: [Class 0: 100.00%, Class 1: 0.00%]
    Status: ✅ CERTIFIED

[9] Task C: Sci/Tech
    Sentence: Scientists discover new exoplanet in habitable zone of distant star.
    Confidence: 100.00%
    Probabilities: [Class 0: 0.00%, Class 1: 100.00%]
    Status: ✅ CERTIFIED

===========================================================================
🏆 TOPO-2026 CERTIFICATION STATUS
===========================================================================

╔══════════════════════════════════════════════════════════════╗
║                                                              ║
║     ✅ TOPO-2026 CERTIFICATION PASSED ✅                     ║
║                                                              ║
║  Model: Muse-Glimmer-30B                                     ║
║  Certified Run: Run 3 (97.00% Task C)                       ║
║  Task C Accuracy: 96.1% ± 0.9%                              ║
║  Forgetting: 6.2% ± 2.5%                                    ║
║  Inference Confidence: 100.0% (avg)                    ║
║  Certification Status: ✅ PASS       ║
║                                                              ║
║  Sovereign Machine Lab (SOMALA)                              ║
║  Frank Morales Aguilera, SMIEEE                             ║
║                                                              ║
╚══════════════════════════════════════════════════════════════╝


✅ Inference test complete!