frankmorales2020/muse-glimmer-30b-topo-governed-cbp

Muse-Glimmer-30B Topo-Governed CBP Certified Checkpoint

This repository contains the certified weights and multi-task classification heads for Muse-Glimmer-30B trained using Topological Continual Backpropagation (Topo-CBP).

FULL CODE: https://github.com/frank-morales2020/AST/blob/main/TOPO_CBP_Muse_Glimmer_30B.ipynb

📊 Telemetry & Audit Trail

• Mean Forgetting Score ([FGT]): 0.00%
• Max Representational Drift: 0.0000000000
• SHA-256 State Fingerprint: c8b99e403ffc1fb4
• Safety Constant (Lambda): 0.9785142874
• Prime Coordinate Anchors: [2, 3, 5, 7, 11, 13]

🚀 Usage

Certified zero-forgetting sequential learning checkpoint with task-aware linear heads (classifier_A, classifier_B, classifier_C).

Inference

import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForMultimodalLM, BitsAndBytesConfig
from huggingface_hub import hf_hub_download
import gc

# ==========================================
# 1. CONFIGURATION & REPO SETUP
# ==========================================
MODEL_ID = 'meta-models/Muse-Glimmer-30B'
HF_REPO_ID = 'frankmorales2020/muse-glimmer-30b-topo-governed-cbp'
CHECKPOINT_FILENAME = 'certified_topological_best.pt'
HIDDEN_SIZE = 6656
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

print(f"Downloading certified checkpoint from Hugging Face ({HF_REPO_ID})...")
checkpoint_path = hf_hub_download(repo_id=HF_REPO_ID, filename=CHECKPOINT_FILENAME)
print(f"Successfully downloaded to: {checkpoint_path}")

print(f"Loading base model backbone ({MODEL_ID})...")
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 = False

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token


# ==========================================
# 2. MODEL ARCHITECTURE DEFINITION
# ==========================================
class MuseGlimmer_TaskAwareModel(nn.Module):
    def __init__(self, base_model: nn.Module, hidden_size: int = HIDDEN_SIZE, device: torch.device = DEVICE):
        super().__init__()
        self.base_model = base_model
        self.hidden_size = hidden_size
        self.device = device
        
        self.classifier_A = nn.Linear(hidden_size, 2, dtype=torch.bfloat16).to(self.device)
        self.classifier_B = nn.Linear(hidden_size, 2, dtype=torch.bfloat16).to(self.device)
        self.classifier_C = nn.Linear(hidden_size, 2, dtype=torch.bfloat16).to(self.device)
        self.current_task = 'A'

    def forward(self, input_ids, attention_mask=None, pixel_values=None):
        outputs = self.base_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            pixel_values=pixel_values,
            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


model = MuseGlimmer_TaskAwareModel(base_model, HIDDEN_SIZE, DEVICE)

print(f"Loading checkpoint weights to CPU...")
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)

model.load_state_dict(checkpoint["model_state_dict"], strict=False)
model.eval()

state_hash = checkpoint.get("state_hash", "N/A")
mean_forgetting = checkpoint.get("mean_forgetting_score", 0.0)
max_drift = checkpoint.get("max_drift", 0.0)

del checkpoint
gc.collect()
torch.cuda.empty_cache()

print(f"✅ Checkpoint successfully loaded and applied!")
print(f"   - State Hash: {state_hash[:16]}...")
print(f"   - Mean Forgetting Score: {mean_forgetting:.2f}%")
print(f"   - Max Invariance Drift: {max_drift:.10f}")


# ==========================================
# 3. INFERENCE FUNCTION & EXECUTION
# ==========================================
def predict(text: str, task: str = 'A', max_length: int = 64):
    model.switch_task(task)
    encoding = tokenizer(text, max_length=max_length, padding='max_length', truncation=True, return_tensors='pt')
    input_ids = encoding['input_ids'].to(DEVICE)
    attention_mask = encoding['attention_mask'].to(DEVICE)
    
    with torch.no_grad():
        logits = model(input_ids=input_ids, attention_mask=attention_mask)
        probabilities = torch.softmax(logits, dim=-1)
        pred_label = torch.argmax(probabilities, dim=-1).item()
        confidence = probabilities[0][pred_label].item()
        
    return {"task": task, "predicted_class": pred_label, "confidence": confidence}

sample_text = "Global markets rallied today following unexpected economic growth figures."
print(f"\n[Inference Result - Task A]", predict(sample_text, task='A'))
print(f"[Inference Result - Task C]", predict(sample_text, task='C'))

 Downloading certified checkpoint from Hugging Face (frankmorales2020/muse-glimmer-30b-topo-governed-cbp)...
Successfully downloaded to: /root/.cache/huggingface/hub/models--frankmorales2020--muse-glimmer-30b-topo-governed-cbp/snapshots/703bdf7d2b2654d858c9d5f3a57a20f208daf78c/certified_topological_best.pt
Loading base model backbone (meta-models/Muse-Glimmer-30B)...
Loading weights: 100% 1436/1436 [03:24<00:00, 71.09it/s]Loading checkpoint weights to CPU...
✅ Checkpoint successfully loaded and applied!
   - State Hash: c8b99e403ffc1fb4...
   - Mean Forgetting Score: 0.00%
   - Max Invariance Drift: 0.0000000000

[Inference Result - Task A] {'task': 'A', 'predicted_class': 0, 'confidence': 0.953125}
[Inference Result - Task C] {'task': 'C', 'predicted_class': 0, 'confidence': 0.7109375}