muse-glimmer-30b-topo-governed-cbp-new-version

Muse-Glimmer-30B: Integration of Topo-CBP with Hugging Face Trainer

This model checkpoint represents the full integration of Topological Continual Backpropagation (Topo-CBP) with the Hugging Face Trainer on the 30B multimodal architecture Muse-Glimmer-30B.

The training workflow combines standard Hugging Face orchestration with bare-silicon parameter governance via TopoAdamW across sequential downstream classification tasks.

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

article: https://medium.com/ai-simplified-in-plain-english/the-architectural-amnesia-of-metas-muse-solving-catastrophic-forgetting-without-the-cloud-crutch-9b839345138e

Architecture & Training Integration

• Framework Orchestration: Subclassed Hugging Face Trainer (TopoTrainer) executing standard training arguments, gradient accumulation, and learning rate scheduling.
• Governor Optimizer: TopoAdamW injected directly into the HF Trainer loop.
• Phase I Governance: Pre-step gradient zeroing ($\nabla_{\mathcal{P}}\mathcal{L} = 0$) across bedrock prime coordinate anchors ($\mathcal{P} = {2, 3, 5, 7, 11, 13}$) and Euler attenuation ($\Lambda = 0.9785142874$).
• Phase II Silicon Lock: In-place post-step restoration of baseline coordinates directly in VRAM.
• Backbone Substrate: meta-models/Muse-Glimmer-30B loaded in 4-bit NormalFloat (NF4) BitsAndBytes quantization (bfloat16 compute precision).
• Task Projections: Task-aware linear classification heads (classifier_A, classifier_B, classifier_C).

Training Loss Convergence

The model and training ran successfully to the end across all three tasks, demonstrating stable convergence across all 5 epochs per task: - Task A: Range 0.5699 – 1.064 (Final Train Loss: 0.7583) - Task B: Range 0.5531 – 1.140 (Final Train Loss: 0.7291) - Task C: Range 0.4112 – 0.8242 (Final Train Loss: 0.5644)

Telemetry & Certification Audit Trail

• Baseline Hardware Digest: c8b99e403ffc1fb4
• Audited Runtime Digest: c8b99e403ffc1fb4
• Max Representational Drift: 0.0000000000
• Mean Forgetting Score ([FGT]): 0.00%
• CI/CD Gate Verdict: 100% PASS

Checkpoint Artifact

• File: certified_topological_best.pt (~19.4 GB)
• Contents: Task-aware state dict, certified SHA-256 fingerprint, and hardware invariance audit trail.

INFERENCE

import gc
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from transformers import (
    AutoModelForMultimodalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    logging as hf_logging
)

# Suppress repetitive gradient checkpointing warnings
hf_logging.set_verbosity_error()

# =====================================================================
# 1. CONFIGURATION & ARTIFACT DOWNLOAD FROM HF HUB
# =====================================================================
BASE_MODEL_ID = "meta-models/Muse-Glimmer-30B"
REPO_ID = "frankmorales2020/muse-glimmer-30b-topo-governed-cbp-new-version"
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 Hub ({REPO_ID})...")
checkpoint_path = hf_hub_download(repo_id=REPO_ID, filename=CHECKPOINT_FILENAME)
print(f"Downloaded artifact path: {checkpoint_path}\n")

# =====================================================================
# 2. BASELINE BACKBONE INSTANTIATION (4-BIT NF4)
# =====================================================================
print(f"Loading base backbone ({BASE_MODEL_ID}) with BitsAndBytes 4-bit quantization...")
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(
    BASE_MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto",
    max_memory={0: "22GB", "cpu": "30GB"},
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
)
base_model.config.use_cache = False

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

# =====================================================================
# 3. TASK-AWARE MULTI-HEAD ARCHITECTURE WRAPPER
# =====================================================================
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)

# =====================================================================
# 4. LOAD & APPLY CERTIFIED TOPOLOGICAL WEIGHTS
# =====================================================================
print(f"Loading certified checkpoint weights into model...")
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")
max_drift = checkpoint.get("max_drift", 0.0)

# Clean up temporary memory
del checkpoint
gc.collect()
torch.cuda.empty_cache()

print("Certified checkpoint successfully verified and applied.")
print(f"  - Silicon State Hash : {state_hash}")
print(f"  - Max Invariance Drift: {max_drift:.10f}\n")

# =====================================================================
# 5. INFERENCE TEST ACROSS SEQUENTIAL TASK HEADS
# =====================================================================
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)
        predicted_class = torch.argmax(probabilities, dim=-1).item()
        confidence = probabilities[0][predicted_class].item()

    return {
        "task": task,
        "predicted_class": predicted_class,
        "confidence": round(confidence, 4)
    }

# Tailored test prompts for multi-domain validation
test_prompts = {
    'A': "Global financial markets reacted positively to the unexpected quarterly economic expansion report.",
    'B': "Real-time sensor telemetry indicated stable bus voltage and invariant clock synchronization across the primary avionics flight computer architecture.",
    'C': "The execution trace verified that prime coordinate anchors maintained zero gradient interference during backpropagation on bare-metal silicon."
}

print("--- Running Multi-Head Domain-Specific Inference Tests ---")
for task_id, prompt in test_prompts.items():
    result = predict(prompt, task=task_id)
    conf_pct = result['confidence'] * 100
    print(f"Task {task_id} Prediction -> Class: {result['predicted_class']} | Confidence: {conf_pct:.2f}%")