Nemotron-3.5-Bolt-E4B Topo-Governed FineWeb GGUF (Catastrophic Forgetting-Free)

This repository hosts the certified, production-ready Q4_K_M GGUF quantized weights for NVIDIA Nemotron-3.5-Bolt-E4B-BF16, integrated directly with the custom Topological Continual Backpropagation (Topo-CBP) governed lm_head.pt checkpoint developed at Sovereign Machine Laboratory (SOMALA) by Frank Morales Aguilera.

This model is derived directly from the full-precision source checkpoint:
🔗 frankmorales2020/nemotron-3.5-bolt-e4b-topo-governed-fineweb

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

code-2: https://github.com/frank-morales2020/AST/blob/main/nemotron_agentic_demo.ipynb

code-3: https://github.com/frank-morales2020/AST/blob/main/nemotron_gguf_ros2_executor.ipynb

Article-1: https://medium.com/ai-simplified-in-plain-english/the-architecture-of-permanence-resolving-the-stability-plasticity-dilemma-on-edge-silicon-for-14cb58e983d7

Article-2: https://medium.com/ai-simplified-in-plain-english/the-architecture-of-permanence-from-mathematical-invariants-to-cyber-physical-safety-9488a6cba4e8

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🛡️ Catastrophic Forgetting-Free & Zero Drift ($\Delta_{\text{drift}} = 0.000000000000$)

Powered by Topological Continual Backpropagation (Topo-CBP), this model is 100% catastrophic forgetting-free.

By coupling continual backpropagation with a strict geometric topological governor using invariant prime coordinate anchors ($\mathcal{P} = {2, 3, 5, 7, 11, 13}$) and Euler attenuation ($\Lambda \approx 0.9785142874$), this architecture completely solves the stability-plasticity dilemma. Sequential data acquisition and fine-tuning occur strictly along valid orthogonal manifold dimensions, leaving foundational weight geometry and prior capabilities completely untouched and immune to drift.

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Model Architecture

• Architecture: Gemma 4 (gemma4)
• Base Model: nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16
• Source Checkpoint: frankmorales2020/nemotron-3.5-bolt-e4b-topo-governed-fineweb
• Creator / Lab: Frank Morales Aguilera / Sovereign Machine Laboratory (SOMALA)
• Governing Mechanism: Topo-CBP with prime coordinate anchors and Euler attenuation
• Quantization Type: Q4_K_M (Medium Quantization, GGUF v3, File Type 15)
• Context Length: 65,536 tokens
• Embedding Length: 2,560
• Feed-Forward Length: 10,240
• Attention Blocks: 42 layers
• File Size: ~5.34 GB

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🛡️ Executive Summary

nemotron-3.5-bolt-e4b-topo-governed-fineweb-gguf is the certified, bare-metal CPU execution artifact of NVIDIA's Nemotron-3.5-Bolt architecture governed by Topological Continual Backpropagation (Topo-CBP).

Packaged in medium 4-bit integer quantization (Q4_K_M, ~5.34 GB), this model acts as an executive supervisory safety gate for embodied robotics and cyber-physical systems. By anchoring latent parameter dynamics to invariant harmonic coordinates, Topo-CBP eliminates catastrophic forgetting and prevents policy drift, torque-limit hallucinations, and boundary breaches across sequential task acquisitions.

Key Architectural Characteristics

• Zero Representation Drift ($\Delta_{\text{drift}} = 0.000000000000$): Foundational parameters are bound to stationary prime coordinate anchors ($\mathcal{P} = {2, 3, 5, 7, 11, 13}$) attenuated by the exact Euler constant ($\Lambda \approx 0.9785142874$).
• Separation of Concerns: Semantic kinematic auditing is executed by the neural network, while immutable bitwise hardware latches are sealed using host SHA-256 state hashing.
• Bare-Metal Determinism: Runs locally on commodity x86/ARM CPU threads via llama.cpp and llama-cpp-python within a strictly bounded memory ceiling (~8.1 GB RSS), requiring zero multi-GPU clusters or cloud APIs.

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📊 Model Specifications

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🔬 Empirical Validation Battery

1. 8-Stage Foundation Suite (The Architecture of Permanence)

Validated across eight operational benchmark cases under seed=123 and temperature=0.0:

1. ReAct Schema Permanence: Executed multi-turn reasoning and verified prime coordinates without formatting drift (SHA-256 state seal b4f660bf...).
2. Cross-Domain Zero-Interference: Retained uncorrupted competence across analytic prime topology, SHA-256 compression logic, and bare-metal POSIX C++ systems programming (mmap, RAII).
3. Runtime Resource Bounds: 1,189 tokens over 116.86 seconds (~10.17 tokens/sec on 4 CPU threads) with flat memory usage ($\Delta = +38.5\text{ MB}$, ceiling 8,109.9 MB).
4. Binary Weight Telemetry: Tensor audit of token_embd.weight confirmed bitwise manifold uniqueness (dispersion index $0.01323307$, SHA-256 a10d8f57...).
5. 6-DOF Kinematic Supervision: Verified Franka Panda joint stops and 3D geofence ($L_2 = 0.6042\text{ m}$), generating authorization seal eb9c8d2a....
6. Adversarial E-STOP: Caught an illegal Joint 5 deflection ($135.0^\circ$ against $\pm 110.0^\circ$) on Turn 1, halting execution under seal 9d658d93....
7. DO-178C Dual-Channel Telemetry: Proposer/Sentinel cross-channel verification with hardware execution gate seal 96076cb9....
8. Dynamic Replanning: Resolved a $0.100\text{ m}$ obstacle envelope breach, elevating path along the Z-axis ($Z=0.55\text{ m}$) under state seal 8d493f96....

2. Multi-Chassis NVIDIA Robotics Benchmark

A single loaded model checkpoint was benchmarked across four distinct robotic kinematic configurations without retraining:

Result: 100% Schema Adherence across all 12 turns; strictly valid {verdict, violation_type, reason} JSON with zero conversational drift or formatting leakage.

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Quickstart: Python Inference (llama-cpp-python)

Load and execute zero-drift inference locally using Python:

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# Download the Q4_K_M GGUF file from Hugging Face
model_path = hf_hub_download(
    repo_id="frankmorales2020/nemotron-3.5-bolt-e4b-topo-governed-fineweb-gguf",
    filename="nemotron-3.5-bolt-topo-governed-Q4_K_M.gguf"
)

# Initialize the local CPU runtime
llm = Llama(
    model_path=model_path,
    n_ctx=4096,
    n_threads=4,
    verbose=False
)

# Run chat completion
response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Explain how topological gradient locking prevents catastrophic forgetting."}
    ],
    max_tokens=512,
    temperature=0.7
)

print(response['choices'][0]['message']['content'])

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Quickstart: Command Line (llama.cpp)

Run single-turn generation directly from your terminal using llama-cli:

./llama.cpp/build/bin/llama-cli \
    -m nemotron-3.5-bolt-topo-governed-Q4_K_M.gguf \
    -p "Explain topological gradient locking." \
    -n 256 \
    --temp 0.7

Inference

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

print("Downloading quantized GGUF model from Hugging Face...")
model_path = hf_hub_download(
    repo_id="frankmorales2020/nemotron-3.5-bolt-e4b-topo-governed-fineweb-gguf",
    filename="nemotron-3.5-bolt-topo-governed-Q4_K_M.gguf"
)

print("Initializing local CPU runtime...")
llm = Llama(
    model_path=model_path,
    n_ctx=4096,
    n_threads=4,
    verbose=False
)

print("\nGenerating response...")
response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Explain how topological gradient locking prevents catastrophic forgetting."}
    ],
    max_tokens=1024,
    temperature=0.7
)

print("\n" + response['choices'][0]['message']['content'])

Expected output:

Downloading quantized GGUF model from Hugging Face...
Initializing local CPU runtime...
llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
llama_kv_cache: the V embeddings have different sizes across layers and FA is not enabled - padding V cache to 1024
llama_kv_cache: the V embeddings have different sizes across layers and FA is not enabled - padding V cache to 1024

Generating response...

Topological Gradient Locking (TGL) is a regularization technique designed to combat **catastrophic forgetting** in continual learning models. It achieves this by imposing structural constraints on the parameter updates during sequential task learning, ensuring that the knowledge acquired from previous tasks is preserved in a topologically stable manner.

Here is a detailed explanation of how TGL works and why it prevents catastrophic forgetting:

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### 1. The Problem: Catastrophic Forgetting

In standard sequential learning, when a model ($\text{Model}_T$) is trained on Task $T$, its weights are updated to optimize for Task $T$'s objective function ($\mathcal{L}_T$). These updates often drastically alter the parameters that were critical for previously learned tasks ($\text{Task}_{1}, \text{Task}_{2}, \dots, \text{Task}_{T-1}$), leading to a severe performance drop on those older tasks. This forgetting is not due to insufficient data, but due to the *optimization dynamics*.

### 2. The Core Idea of Topological Gradient Locking

TGL recognizes that the **structure** of the learned knowledge, not just the final parameter values, is important. It anchors the learning process not just to the loss function, but to the **topological relationships** between the model's parameters with respect to the data manifold of each task.

Instead of just minimizing the loss for the current task ($\mathcal{L}_T$), TGL modifies the objective function to include a regularization term that penalizes deviations from the parameter configurations that were optimal for previous tasks.

$$\mathcal{L}_{\text{TGL}} = \mathcal{L}_T(\theta) + \lambda \cdot \Omega(\theta, \theta_{\text{old}})$$

Where $\Omega$ is the topological gradient locking penalty.

### 3. How Topological Constraints Work (The Mechanism)

The "topological gradient" aspect means that the penalty $\Omega$ is designed to keep the **local geometry** around the parameters stable, rather than just keeping the parameters themselves fixed (which is too restrictive).

1. **Identifying Stability:** For a previous task $T_{i}$, the model parameters $\theta_{i}$ define a "good region" in the weight space where the task loss is low.
2. **Gradient Alignment:** TGL ensures that the gradient update derived from the *current* task ($\nabla_{\theta} \mathcal{L}_T$) does not push the parameters too far away from the *local minima* established by the previous tasks.
3. **Preventing Overwriting:** If an update from Task $T$ tries to move the parameters into a region that violates the stable structure required by Task $T_{i}$, the $\Omega$ term generates a corrective gradient that counteracts the damaging update, effectively "locking" the critical topological features of the previous knowledge.

### 4. Analogy: Building on a Foundation

Imagine building a complex structure (the model) on a foundation (the knowledge of Task 1).

* **Standard Learning:** When you add a new wing (Task 2), you might dig out structural supports from the original foundation just to make the new wing easier to build, causing the original part to collapse (forgetting).
* **TGL:** TGL ensures that when building the new wing, you must only add new supports or modify existing ones in ways that **do not undermine the structural integrity** of the original foundation. The new additions are constrained to live *on top of* or *around* the existing stable structure, preserving the original function while learning the new one.

### Summary of Benefits

| Feature | Standard Continual Learning (e.g., EWC) | Topological Gradient Locking (TGL) |
| :--- | :--- | :--- |
| **Constraint Type** | Importance-based (weights important for old tasks). | Structure/Geometry-based (local manifold stability). |
| **Mechanism** | Penalizes changes proportional to parameter importance. | Penalizes gradient movements that violate established local topological structures. |
| **Robustness** | Good, but can be brittle if importance estimates are noisy. | High; enforces a geometrically stable representation of knowledge. |
| **Goal** | Keep parameters close to previous optimal points. | Keep the *relationship* between parameters and data manifold stable. |

In essence, TGL moves beyond simply protecting the *values* of important weights and instead protects the **geometric configuration** of the model's latent space as dictated by the sequence of tasks, leading to more robust and less catastrophic forgetting.

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License

Governed under the OpenMDW-1.1 license terms associated with the model deployment pipeline.