Overhaul HF dataset card and update license

README.md

@@ -1,9 +1,10 @@
 ---
-license: gpl-3.0
-pretty_name: "Metis-OLMoE Latent Telemetry: Spikenaut SNN Routing"
 language:
   - en
-  - code
+license:
+  - apache-2.0
+  - mit
+pretty_name: "Metis-OLMoE Latent Telemetry: Spikenaut SNN Routing"
 tags:
   - snn
   - spiking-neural-network
@@ -18,30 +19,94 @@ tags:
   - rust
   - gguf
   - saaq
-datasets:
-  - allenai/OLMoE-1B-7B-0125-Instruct-GGUF
+  - tabular
+  - timeseries
 task_categories:
   - text-generation
   - feature-extraction
+annotations_creators:
+  - machine-generated
+language_creators:
+  - other
+size_categories:
+  - n<1K
+source_datasets:
+  - allenai/OLMoE-1B-7B-0125-Instruct-GGUF
+multilinguality:
+  - monolingual
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "origin_hardware_baselines/resident_evil_4/system_telemetry_v1_batch_*.parquet"
+dataset_info:
+  features:
+    - name: timestamp_ms
+      dtype: int64
+    - name: power_usage_mw
+      dtype: uint32
+    - name: temperature_c
+      dtype: float32
+    - name: pcie_rx_kbps
+      dtype: uint32
+    - name: pcie_tx_kbps
+      dtype: uint32
+    - name: encoder_util_perc
+      dtype: float32
+    - name: decoder_util_perc
+      dtype: float32
+    - name: mangohud_active
+      dtype: bool
+    - name: cpu_tctl_c
+      dtype: float32
+    - name: cpu_ccd1_c
+      dtype: float32
+    - name: cpu_ccd2_c
+      dtype: float32
+    - name: throttle_reasons_bitmask
+      dtype: int64
 ---
 
+[![License: Apache 2.0 / MIT](https://img.shields.io/badge/License-Apache%202.0%20%2F%20MIT-blue.svg)](https://opensource.org/licenses)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry)
+
 # Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing
 
-## 1. The Origins of Metis
+> Bare-metal hardware telemetry and SNN latent space routing data for neuromorphic quantization research. This dataset documents the discovery of **Semantic Attractor Clustering** — that a Spiking Neural Network physically routes different semantic concepts (abstract language vs code syntax vs math logic) into distinct, repeatable biological pathways when L2 Normalization is applied to LLM embeddings.
+
+---
+
+## Dataset Details
+
+- **Curated by:** Raul Montoya Cardenas
+- **Language(s):** English, Code
+- **License:** Apache 2.0 / MIT (dual-licensed)
+- **Repository:** [huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry](https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry)
+- **Base Model:** [allenai/OLMoE-1B-7B-0125-Instruct-GGUF](https://huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF)
+- **Implementation:** [corinth-canal](https://github.com/Limen-Neural/corinth-canal) (SNN quantization pipeline)
+- **Analysis:** [Surrogate_Viz.jl](https://github.com/Spikenaut/Surrogate_Viz.jl) (symbolic regression)
+
+---
+
+## Dataset Description
+
+### Origins of Metis
 
 Before this was a formal dataset, it was an attempt to solve a bare-metal problem. I had been experimenting with mining telemetry, HFT bots, and sync node data to train a spiking neural network (SNN), but the data kept returning dead zeros in value after being used for training.
 
-The breakthrough came entirely by accident. I was running heavy mods—DLSS 4.0 and path tracing—on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: *What if I used raw gaming telemetry data for neuromorphic spike data conversion?* What if I could use this intense hardware stress to create an artificial heartbeat for AI?
+The breakthrough came entirely by accident. I was running heavy mods — DLSS 4.0 and path tracing — on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: *What if I used raw gaming telemetry data for neuromorphic spike data conversion?* What if I could use this intense hardware stress to create an artificial heartbeat for AI?
 
-When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, **Metis** was born—a MoE-based SNN quantizaton model and dataset architecture for exploring SNN quantization. 
+When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, **Metis** was born — a MoE-based SNN quantization model and dataset architecture for exploring SNN quantization.
 
 ### Relationship to Spikenaut
 
-**Spikenaut** is my pure SNN model, built from scratch as a native spiking neural network. **Metis** (this repository) serves as the architect and teacher—exploring SNN quantization techniques through the OLMoE Mixture-of-Experts model. The discoveries, equations, and architecture frameworks developed here feed directly into Spikenaut's training and evolution. Metis proves the math; Spikenaut implements it natively.
+**Spikenaut** is my pure SNN model, built from scratch as a native spiking neural network. **Metis** (this repository) serves as the architect and teacher — exploring SNN quantization techniques through the OLMoE Mixture-of-Experts model. The discoveries, equations, and architecture frameworks developed here feed directly into Spikenaut's training and evolution. Metis proves the math; Spikenaut implements it natively.
+
+---
 
-## 2. The Science: Semantic Attractor Clustering
+## The Science: Semantic Attractor Clustering
 
-This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the routing encoder. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics. These insights directly inform the training of **Spikenaut**, my pure native SNN.
+This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the routing encoder. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.
 
 ### The Discovery: Physical Neighborhood Mapping
 
@@ -49,61 +114,154 @@ The primary breakthrough documented in this dataset is the organic, physical sep
 
 Telemetry visualizations prove that the Spike-based routing physically routes different cognitive tasks into isolated biological neighborhoods:
 
-#### Abstract Language Routing (The 2000-Route)
+| Abstract Language Routing (2000-Route) | Structured Logic Routing (600-800 Band) |
+|:---:|:---:|
+| ![English Logic Routing](first-day-testing-real-weights/second-test/map_olmoe_english_logic.png) | ![Rust Syntax Routing](first-day-testing-real-weights/third-test/map_olmoe_rust_syntax_logic.png) |
+| Abstract English logic establishes a dominant attractor basin at the **2000-index walker route** | Rust syntax and math logic collapse into the same **600-800 frequency band** |
+
 When fed abstract English logic, the network distributes energy across multiple nodes, establishing a dominant attractor basin at the **2000-index walker route**, with secondary echoes in Walkers 700 and 1450.
-![English Logic Routing](first-day-testing-real-weights/second-test/map_olmoe_english_logic.png)
 
-#### Structured Logic Routing (The 600-800 Band)
 When fed rigid mathematical statements or raw Rust syntax, the network completely abandons the 2000-route. The tokens experience mathematical pushback in abstract centers and organically collapse into the exact same **600-800 frequency band**. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods to conserve energy.
-![Math Logic Routing](first-day-testing-real-weights/fourth-test/map_olmoe_math_logic.png)
-![Rust Syntax Routing](first-day-testing-real-weights/third-test/map_olmoe_rust_syntax_logic.png)
 
-## 3. Experiment Progression
+---
+
+## Experiment Progression
+
 The dataset documents the chronological progression from synthetic baselines to actual semantic routing:
 
-* **Phase 1: Synthetic Baseline (Smoke Test)**
-  * **Input:** Synthetic sine wave.
-  * **Result:** Verified the GPU temporal loop (10,000 ticks) and basic biological fatigue without crashing the CUDA context.
-* **Phase 2: The F16 Magnitude Collapse (Unbounded)**
-  * **Input:** Real LLM embeddings (OLMoE).
-  * **Result:** Unscaled F16-to-F32 extraction resulted in raw, unbounded electrical pressure. A single expert neuron (Walker ~620) was overwhelmed, causing a routing collapse where one walker took the entire load for the full temporal loop.
-* **Phase 3: L2 Normalization & Philosophy Attractors**
-  * **Input:** `"Let's teach this MoE model..."` (Abstract English).
-  * **Result:** L2 Normalization successfully shattered the routing collapse. Energy dynamically settled into high-register attractor bands, predominantly isolating into the **2000-route**.
-* **Phase 4: Semantic Clustering (Code & Math Logic)**
-  * **Input A:** `fn main() { println!("Hello, World!"); }` (Rust Syntax)
-  * **Input B:** `"The derivative of a constant is mathematically zero."` (Math Logic)
-  * **Result:** The SNN abandoned the 2000-route completely. Both raw Rust syntax and mathematical logic organically fell into the exact same **600-800 frequency band**. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods.
+| Phase | Name | Input | Key Result |
+|:---:|------|-------|------------|
+| 1 | Synthetic Baseline | Synthetic sine wave | Verified GPU temporal loop (10,000 ticks) and basic biological fatigue |
+| 2 | F16 Magnitude Collapse | Real LLM embeddings (OLMoE) | Unscaled F16-to-F32 extraction caused routing collapse (single walker overwhelmed) |
+| 3 | Attractor Discovery | "Let's teach this MoE model..." | L2 Normalization shattered the collapse; energy settled into Walker 2000 |
+| 4 | Rust Syntax | `fn main() { println!(); }` | Code syntax routed to completely different biological neighborhood (600-800 band) |
+| 5 | Math Logic Clustering | "The derivative of a constant is mathematically zero." | Math logic routed to the same 600-800 band as Rust syntax — confirming Semantic Attractor Clustering |
+
+---
 
-**Dataset for Spikenaut SNN Research**
+## Usage
 
-This dataset contains latent telemetry and routing data generated from the SNN-quantized version of AllenAI’s OLMoE-1B-7B-0125-Instruct model using the `corinth-canal` pipeline. The heavy mathethical analysis is documented and implemented in the 'surrogate_viz.jl' repository.
+### Quick Start (Python)
 
+```python
+from datasets import load_dataset
 
+# Load the hardware telemetry dataset
+dataset = load_dataset("rmems/Metis-OLMoE-Latent-Telemetry", split="train")
 
-### Purpose
-These files are used to study spiking behavior, routing stability, and adaptive quantization (SAAQ) in SNN-converted MoE models. The data feeds SymbolicRegression.jl to discover new equations for improved SNN quantization and ultimately trains the pure native **Spikenaut** SNN.
+print(dataset.features)
+print(dataset[0])
+```
 
-### Folder Structure
+### Data Schema (Hardware Telemetry — Parquet)
 
-- **routing/** — CSV files containing routing and latent telemetry data
-- **first-day/** — Early experimental runs (optional)
-- **SAAQ 3.0/** — Future runs with new algorithm versions
-- **experiments/** — Additional test configurations and variants
-- **results/**
-  - **plots/** — Visualization of SNN routing paths and firing density
-  - **raw_telemetry/** — Original tick-by-tick log files
+The primary data is provided as Parquet files captured at 5ms intervals using NVML:
 
-  ### Origin Hardware Baselines (`/origin_hardware_baselines/`)
-This directory contains the foundational, bare-metal hardware telemetry that inspired the Spikenaut SAAQ thermal equations. 
+| Feature | Type | Description |
+|:---|:---|:---|
+| `timestamp_ms` | `int64` | UNIX timestamp in milliseconds (5ms interval) |
+| `power_usage_mw` | `uint32` | Total GPU power usage in milliwatts |
+| `temperature_c` | `float32` | GPU core temperature in Celsius |
+| `pcie_rx_kbps` | `uint32` | Incoming PCIe throughput in KB/s (excitatory signal) |
+| `pcie_tx_kbps` | `uint32` | Outgoing PCIe throughput in KB/s |
+| `encoder_util_perc` | `float32` | NVIDIA Encoder (NVENC) utilization % |
+| `decoder_util_perc` | `float32` | NVIDIA Decoder (NVDEC) utilization % |
+| `mangohud_active` | `bool` | Whether MangoHud overlay was active |
+| `cpu_tctl_c` | `float32` | CPU package temperature (Tctl) |
+| `cpu_ccd1_c` | `float32` | CPU Core Complex Die 1 temperature |
+| `cpu_ccd2_c` | `float32` | CPU Core Complex Die 2 temperature |
+| `throttle_reasons_bitmask` | `int64` | Hardware throttling events bitmask (inhibitory signal) |
 
-* **`RE4_path_tracing_telemetry.csv`**: This file captures the extreme hardware stress (GPU/CPU temps, package power, clock speeds) generated by running the *Resident Evil 4 Remake* with heavy modifications (path tracing, DLSS 4.0) on a high-performance workstation PC. 
-* **Purpose:** This specific dataset proved that intense gaming workloads create a highly dynamic, non-zero biological "heartbeat" compared to static crypto-mining telemetry. These exact thermal fluctuations were used to derive the baseline fatigue limits for the SNN's `tick_gpu_temporal` loop.
+### Neuromorphic Mapping
 
-  ### Related Repos
-- [corinth-canal](https://github.com/Limen-Neural/corinth-canal) — SNN quantization pipeline
-- [Surrogate_Viz.jl](https://github.com/Spikenaut/Surrogate_Viz.jl) — Symbolic regression and visualization
+This data behaves as "sensorimotor" stimulus for neural networks:
 
+- **Excitatory Inputs:** High surges in `pcie_rx_kbps` indicate asset floods (e.g., BVH structure updates for path tracing), mimicking sensory signals
+- **Action Potentials:** `encoder_util_perc`, `decoder_util_perc`, and `power_usage_mw` transients represent internal activity and firing rates
+- **Inhibitory Inputs:** Non-zero `throttle_reasons_bitmask` signals act as inhibitory governors, dynamically suppressing activity
+- **State/Momentum:** Slow-moving temperatures (`cpu_tctl_c`, `temperature_c`) and memory capacity
+
+---
 
+## Dataset Structure
+
+```
+├── origin_hardware_baselines/
+│   └── resident_evil_4/
+│       ├── system_telemetry_v1_batch_*.parquet   (48 files)
+│       ├── RE4_path_tracing_telemetry.csv
+│       └── README.md
+├── first-day-testing-real-weights/
+│   ├── first-test-falied/        # Routing collapse visualization
+│   ├── second-test/              # English logic routing (2000-route)
+│   ├── third-test/               # Rust syntax routing (600-800 band)
+│   └── fourth-test/              # Math logic routing (600-800 band)
+├── experiments/                  # Smoke test visualizations
+├── SAAQ 1.0/                     # Foundational SAAQ equation
+├── New symbolic regression equation/   # SAAQ 1.5 delta-Q adaptation
+├── Research notes/               # Informal research notes
+├── routing/                      # (placeholder — future routing CSVs)
+├── results/
+│   ├── plots/                    # (placeholder — visualizations)
+│   └── raw_telemetry/            # (placeholder — tick logs)
+└── model_metadata_manifest.json  # Structured project metadata
+```
+
+---
+
+## Hardware Environment
+
+| Component | Spec |
+|-----------|------|
+| Workstation | Ship of Theseus |
+| GPU | ASUS ProArt GeForce RTX 5080 (16GB VRAM) |
+| CPU | AMD Ryzen 9 9950X |
+| OS | Fedora 43 |
+| Implementation | Custom Rust/CUDA via corinth-canal |
+
+---
+
+## Bias, Risks, and Limitations
+
+- This dataset is generated from a single hardware configuration (RTX 5080 + Ryzen 9 9950X). Routing patterns may differ on other GPU architectures.
+- The telemetry captures are from gaming workloads (Resident Evil 4 Remake with path tracing). Other GPU stress patterns may produce different "heartbeat" signatures.
+- The SNN routing visualizations represent a specific quantization approach (SAAQ) applied to a specific model (OLMoE). Results may not generalize to other SNN architectures or MoE models.
+- The dataset is small (48 parquet batches) and is intended for research and equation discovery, not large-scale training.
+
+---
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@dataset{montoya_2026,
+  author = {Raul Montoya Cardenas},
+  title = {Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry}}
+}
+```
+
+**APA:**
+
+Montoya Cardenas, R. (2026). Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry
+
+---
+
+## Glossary
+
+| Term | Definition |
+|------|------------|
+| **Walker** | A pulse of electrical energy (spike) that physically explores the network to find the path of least resistance. Analogous to electrical impulses in a biological brain. |
+| **L2 Normalization** | Bounds semantic pressure to the unit sphere, preventing any single neuron from becoming dominant. Mimics biological brain energy distribution. |
+| **Semantic Attractor Clustering** | The phenomenon where an SNN physically maps different semantic concepts (abstract language vs code syntax) to distinct, repeatable biological pathways. |
+| **Fatigue Mechanics** | Neurons that fire too much become less responsive, preventing energy overload and enabling network adaptation. |
+| **SAAQ** | Semantic Attractor Architecture Quantization — the quantization technique developed through this research. |
+
+---
 
+## License
 
+This dataset is dual-licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) and [MIT](https://opensource.org/licenses/MIT).

model_metadata_manifest.json

@@ -3,7 +3,7 @@
   "base_model": "OLMoE-1B-7B-0125-Instruct-GGUF",
   "base_model_source": "allenai/OLMoE-1B-7B-0125-Instruct-GGUF",
   "version": "1.0.0",
-  "license": "gpl-3.0",
+  "license": "apache-2.0 / mit",
   "repository_url": "https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry",
   "description": "Spikenaut SNN Routing dataset containing raw bare-metal telemetry logs and latent space visualizations for SNN-quantized OLMoE MoE model",
   "research_purpose": "Map physical routing of LLM embeddings as processed by biologically-inspired neuronal fatigue mechanics",
@@ -175,8 +175,8 @@
   ],
 
   "citation": {
-    "bibtex": "@dataset{metis_olmoe_2025,\n  author = {Spikenaut},\n  title = {Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing},\n  year = {2025},\n  publisher = {Hugging Face},\n  howpublished = {\\url{https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry}}\n}",
-    "apa": "Spikenaut. (2025). Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry"
+    "bibtex": "@dataset{montoya_2026,\n  author = {Raul Montoya Cardenas},\n  title = {Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing},\n  year = {2026},\n  publisher = {Hugging Face},\n  howpublished = {\\url{https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry}}\n}",
+    "apa": "Montoya Cardenas, R. (2026). Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry"
   },
 
   "tags": [
@@ -195,5 +195,5 @@
   ],
 
   "created_at": "2025-04-16",
-  "updated_at": "2025-04-16"
+  "updated_at": "2026-07-10"
 }