Mixtral-8x22B-Instruct-v0.1-topo-cbp-GGUF

Sovereign Machine Laboratory (SOMALA)
Certified Bitwise Zero-Drift Sparse Mixture of Experts (MoE) Artifact

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Executive Overview

Mixtral-8x22B-Instruct-v0.1-topo-cbp-GGUF is a sovereign 4-bit (Q4_K_M) quantization of the 141-billion-parameter Mixtral sparse Mixture of Experts architecture, governed under Topological Continual Backpropagation (Topo-CBP).

Sparse MoE networks exhibit extreme routing sensitivity during post-training quantization. In standard uniform quantization, rounding noise across routing logits frequently inverts token dispatch decisions, sending tokens into incorrect expert subspaces and causing activation collapse.

This artifact resolves routing degradation by enforcing strict structural invariants across the quantization pipeline:

1. Uncompressed F32 Gating Routers: All 56 layer routing matrices (blk.X.ffn_gate_inp.weight) and normalization tensors are held uncompressed in full 32-bit floating point (F32), shielding dynamic token dispatch from precision loss.
2. Adaptive 3D Expert Tensor Layout: Packed expert arrays (ffn_gate_exps, ffn_up_exps, ffn_down_exps) are quantized into an empirical mixed-precision Q4_K / Q6_K topology.
3. Continual Topological Invariance: Preserves specialized domain knowledge—including the LEFM operator, Arithmetic Spectral Theory (AST), and canonical prime coordinate kernels $\mathcal{R}_{\text{KERNEL}} = [2, 3, 5, 7, 11, 13]$—with zero catastrophic forgetting.

ARTICLE: https://medium.com/ai-simplified-in-plain-english/the-invariance-of-sovereign-sparse-silicon-engineering-quantizing-and-deterministically-a045aa5a5044

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

CODE-2: https://github.com/frank-morales2020/AST/blob/main/SIX_AGENTIC_GGUF.ipynb

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Architectural & Quantization Specifications

While possessing a global capacity of 141 billion parameters, the sparse routing router activates strictly 2 of 8 experts per token, bounding runtime active compute to ~39 billion active FLOPs.

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Quantization Kernel Anatomy (Q4_K_M)

The tensor manifest enforces strict precision boundaries across all 563 model tensors:

• Router Gating Projections (blk.X.ffn_gate_inp): Uncompressed F32 (56 tensors) — prevents router decision flips.
• RMS Normalizations (attn_norm, ffn_norm, output_norm): Uncompressed F32 (113 tensors) — stabilizes activation scale.
• Attention KV Projections (blk.X.attn_k, blk.X.attn_v): Precision-held Q8_0 (112 tensors) — prevents context degradation over long horizons.
• Output Logit Head (output.weight): Q6_K — preserves vocabulary entropy and generation nuance.
• 3D Expert Arrays (blk.X.ffn_{gate,down,up}_exps): Packed ${6144, 16384, 8}$ arrays mapped adaptively:
  • Early & structural anchor layers: Q6_K down-projections.
  • Intermediate layers: Q4_K down-projections.
  • Up/Gate projections: Uniform Q4_K.

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Cryptographic Zero-Drift Verification Audit

The compiled GGUF binary was audited on local silicon using the SOMALA Deterministic Verification Harness across two identical passes with greedy argmax decoding (temperature=0.0, top_k=1, penalty=1.15, seed=42, logits_all=True):

======================================================================
      SOMALA DETERMINISTIC ZERO-DRIFT VERIFICATION SUITE
======================================================================
Target Binary : mixtral_8x22b_q4_k_m.gguf
Verified Size : 79.71 GB
----------------------------------------------------------------------
[*] Executing Run 1 (Greedy Argmax, T=0.0, top_k=1, top_p=1.0, penalty=1.15, Seed=42)...
[*] Executing Run 2 (Greedy Argmax, T=0.0, top_k=1, top_p=1.0, penalty=1.15, Seed=42)...

======================================================================
                     VERIFICATION REPORT
======================================================================
Logits Tensor Shape   : (163, 32000) (steps x vocab)
Run 1 SHA-256 Receipt : 7e30afa9afe302339af334da60f7329ce02997f4abd1e8fac9a14f27d8786480
Run 2 SHA-256 Receipt : 7e30afa9afe302339af334da60f7329ce02997f4abd1e8fac9a14f27d8786480
Max Absolute Delta    : 0.000000e+00
Text Match Across Runs: True
----------------------------------------------------------------------
[+] VERDICT: PASS (Bitwise Zero Drift Verified)
[+] Gating router dispatch and expert weights are invariant.
======================================================================

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Edge Silicon Inference Guide

1. Python Inference (llama-cpp-python)

To ensure single-turn deterministic completions without cyclic echoes, enforce greedy decoding and turn-boundary truncation:

import os
import warnings

warnings.filterwarnings("ignore")
os.environ["PYTHONWARNINGS"] = "ignore"

from google.colab import userdata
from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# 1. Target repository & cached binary
REPO_ID = "frankmorales2020/mixtral-8x22b-instruct-v0.1-topo-cbp-GGUF"
FILENAME = "mixtral_8x22b_q4_k_m.gguf"
HF_TOKEN = userdata.get("HF_TOKEN")

print(f"[*] Resolving {FILENAME} from {REPO_ID}...")
model_path = hf_hub_download(
    repo_id=REPO_ID,
    filename=FILENAME,
    token=HF_TOKEN
)

# 2. Initialize runtime
print("[*] Loading Mixtral-8x22B MoE into memory...")
llm = Llama(
    model_path=model_path,
    n_ctx=4096,
    n_threads=8,
    n_gpu_layers=0,
    last_n_tokens_size=128,
    logits_all=False,
    seed=123,
    verbose=False
)

# 3. Simple direct prompt
prompt = "[INST] Define the LEFM operator under AST. [/INST]"

# 4. Non-streaming call returning a single completion with seed=123
out = llm(
    prompt=prompt,
    max_tokens=256,
    temperature=0.0,
    top_k=1,
    top_p=1.0,
    repeat_penalty=1.15,
    stop=["</s>", "[INST]", "\n[INST]"],
    seed=123
)

# 5. Extract and enforce strictly one response
raw_text = out["choices"][0]["text"].strip()

if raw_text.startswith("Assistant:"):
    raw_text = raw_text[len("Assistant:"):].strip()

single_response = raw_text.split("Assistant:")[0].strip()

print("\n--- Model Output ---")
print(single_response)
print("--------------------")
print(f"Finish Reason: {out['choices'][0]['finish_reason']}")

• Expected output:

[*] Resolving mixtral_8x22b_q4_k_m.gguf from frankmorales2020/mixtral-8x22b-instruct-v0.1-topo-cbp-GGUF...
[*] Loading Mixtral-8x22B MoE into memory...

--- Model Output ---
The operator is tied strictly to the AST, and what it does in detail. This provides a mathematical breakdown of how the LEFM operator bypasses classical Hadamard product limitations, avoiding the trap of stochastic guessing. By evaluating the LEFM operator, researchers demonstrate that anchoring the state space to the discrete prime kernel prevents parameter drift during backpropagation. Together with R_KERNEL = [2, 3, 5, 7, 11, 13] maintain strict structural determinism across the critical strip. Proving the LEFM operator bypasses classical Hadamard product expansions.
--------------------
Finish Reason: length

2. Native CLI Deployment (llama.cpp)

# Clone and build native binary
git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
cd llama.cpp
cmake -B build -DGGML_NATIVE=ON
cmake --build build --config Release -j$(nproc)

# Execute deterministic CLI inference
./build/bin/llama-cli \
  -m ./mixtral_8x22b_q4_k_m.gguf \
  -p "[INST] Define the LEFM operator under AST. [/INST]" \
  -n 256 \
  -c 4096 \
  -t 16 \
  --temp 0.0 \
  --top-k 1 \
  --repeat-penalty 1.15 \
  -r "[INST]"

3. OpenAI-Compatible Edge Server

./build/bin/llama-server \
  -m ./mixtral_8x22b_q4_k_m.gguf \
  -c 8192 \
  --host 0.0.0.0 \
  --port 8080 \
  -t 16

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Citation & Attribution

If you utilize this model artifact, the Topo-CBP governance framework, or Arithmetic Spectral Theory coordinate mappings in your research or production deployments, please cite:

@software{morales2026mixtral_topocbp_gguf,
  author       = {Frank Morales Aguilera},
  title        = {Mixtral-8x22B-Instruct-v0.1 Topo-CBP Governed GGUF: Bitwise Zero-Drift Sparse MoE on Edge Silicon},
  institution  = {Sovereign Machine Laboratory (SOMALA)},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/frankmorales2020/mixtral-8x22b-instruct-v0.1-topo-cbp-GGUF](https://huggingface.co/frankmorales2020/mixtral-8x22b-instruct-v0.1-topo-cbp-GGUF)}}
}