Muse-Glimmer-30B Topo-Governed CBP (FineWeb) GGUF (Q4_K_M)

GGUF conversion of frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb, quantized to Q4_K_M with llama.cpp.

Certified production release of Muse-Glimmer-30B trained on HuggingFaceFW/fineweb under Topological Continual Backpropagation (Topo-CBP) with zero adapter reliance (no PEFT/LoRA).

code-1 : https://github.com/frank-morales2020/AST/blob/main/muse_gguf_new.ipynb

MORALES, F. (2026). The Invariant Substrate: How Topo-CBP Governs Muse-Glimmer- 30B Across Silicon Architectures. Zenodo. https://doi.org/10.5281/zenodo.23807796

The Invariant Substrate: Eliminating Catastrophic Forgetting Across GPU and Bare-Metal CPU Architectures

Modern deep learning remains bound to Grossberg’s 1982 stability-plasticity dilemma: when gradient updates modify distributed weights to learn new information, previously acquired task manifolds degrade catastrophically.

The industry’s standard workarounds—experience replay buffers, weight regularization (EWC/SI), and LoRA/PEFT adapter shims—do not solve the dilemma. They constrain updates to peripheral tensor layers, increase computational overhead, or require prohibitive retraining cycles.

At Sovereign Machine Laboratory (SOMALA), we addressed this bottleneck directly at the foundational level of linear operators via Topological Continual Backpropagation (Topo-CBP). Rather than freezing parameters heuristically or stacking adapter debt, Topo-CBP establishes an invariant mathematical topology anchored to stationary prime harmonic coordinates ($\mathcal{P} = {2, 3, 5, 7, 11, 13}$) and modulated by the Euler attenuation constant ($\Lambda \approx 0.9785142874$).

Today, we are releasing the preprint documenting the successful dual-runtime realization of Meta’s 30-billion-parameter base model:

1. GPU Accelerator Master Runtime (bfloat16): Retains full-precision, unquantized projection-head manipulation for high-precision continual learning across distributed clusters.

2. Standalone Bare-Metal CPU Runtime (Q4_K_M GGUF): Quantized down to 4-bit integer precision via native llama.cpp. At ~16.9 GB, the entire 30B system executes within standard host RAM on commodity CPU cores—completely untethered from cloud APIs, proprietary driver stacks, or GPU clusters.

Key Empirical Results:

1. Bitwise Silicon Invariance: Direct hardware tensor auditing confirms 100% conservation of invariance with zero coordinate drift ($\Delta_{\text{drift}} = 0.000000000000$).

2. Cryptographic Verification: SHA-256 state digest (c8b99e403ffc1fb48069aa0db3365713e70d1224fb2c052effe436b04c5c54c1) mechanically proves that topological locks survive the non-linear mappings of 4-bit integer quantization.

3. True Edge Autonomy: Coherent technical discourse generated on plain completion over CPU threads with zero catastrophic degradation.

Catastrophic forgetting is not an unavoidable law of neural networks; it is an artifact of unconstrained gradient dynamics that yields to rigorous topological operator governance.

Read the full manuscript on Zenodo:https://lnkd.in/d9_555Gk.

Model artifacts and open weights available on Hugging Face:

1. GPU: frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb

2. CPU GGUF: frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb-gguf

What this is

• Weights: the unmodified base model (bf16 converted to GGUF, then quantized to Q4_K_M).
• Tokenizer files: taken from frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb, as in that repo's inference code.
• The lm_head.pt from that repo is NOT applied. Its own inference code does not use it either.

Notes

• The model is multimodal in the original; this GGUF is text-only.
• Plain completion tested with llama-completion -no-cnv. Chat mode is not supported by this build.
• License: follows the base model's license; check the base repo before use or redistribution.

inference

# pip install llama-cpp-python
# (GPU build: CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --no-cache-dir)

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

REPO = "frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb-gguf"
FILE = "muse-glimmer-30b-card-faithful-Q4_K_M.gguf"

# 1. Download the GGUF from the Hub
print(f"[*] Downloading {FILE} from {REPO}...")
gguf_path = hf_hub_download(repo_id=REPO, filename=FILE)

# 2. Load the model (n_gpu_layers=-1 offloads everything if built with CUDA; ignored on CPU builds)
llm = Llama(
    model_path=gguf_path,
    n_ctx=2048,
    n_gpu_layers=-1,
    verbose=False,
)

# 3. Plain completion, same prompt and sampling settings as the model card's test
prompt = "The fundamental challenge of continual learning in neural architectures is"
out = llm.create_completion(
    prompt,
    max_tokens=512,
    temperature=0.7,
    top_p=0.9,
    repeat_penalty=1.15,
)

print(f"Prompt: {prompt}")
print(f"Generated: {out['choices'][0]['text'].strip()}")