Add YAML frontmatter and resolve merge conflicts in README

.huggingface.yaml

@@ -0,0 +1,81 @@
+---
+# Hugging Face Dataset Metadata
+# Repository: rmems/Metis-OLMoE-Latent-Telemetry
+
+license: gpl-3.0
+
+# Display and Discovery
+pretty_name: "Metis-OLMoE Latent Telemetry: Spikenaut SNN Routing"
+language:
+  - en
+  - code
+tags:
+  - snn
+  - spiking-neural-network
+  - olmoe
+  - mixture-of-experts
+  - quantization
+  - neuromorphic
+  - telemetry
+  - semantic-routing
+  - latent-space
+  - cuda
+  - rust
+  - gguf
+  - saaq
+  - attractor-clustering
+  
+# Model Lineage
+datasets:
+  - allenai/OLMoE-1B-7B-0125-Instruct-GGUF
+
+# Task and Size
+task_categories:
+  - text-generation
+  - feature-extraction
+  - text-classification
+size_categories:
+  - n<1K
+
+# Dataset Information
+dataset_info:
+  - config_name: default
+    features:
+      - name: tick
+        dtype: int64
+      - name: best_walker
+        dtype: int64
+      - name: elapsed_us
+        dtype: int64
+    splits:
+      - name: full
+        num_bytes: 119634
+        num_examples: 10000
+
+# Model Card Data
+model-index:
+  - name: Metis-OLMoE-Latent-Telemetry
+    results: []
+
+# Additional Metadata
+metadata:
+  version: "1.0.0"
+  base_model: "OLMoE-1B-7B-0125-Instruct-GGUF"
+  base_model_source: "allenai/OLMoE-1B-7B-0125-Instruct-GGUF"
+  architecture: "Mixture of Experts (MoE)"
+  parameters: "1B active / 7B total"
+  quantization: "GGUF"
+  research_purpose: "Map physical routing of LLM embeddings via biologically-inspired neuronal fatigue mechanics"
+  primary_discovery: "Semantic Attractor Clustering"
+  derivation_pipeline: "corinth-canal"
+  technique: "SAAQ (Semantic Attractor Architecture Quantization)"
+  visualization_tool: "Surrogate_Viz.jl"
+  hardware:
+    gpu: "ASUS ProArt GeForce RTX 5080 (16GB VRAM)"
+    cpu: "AMD Ryzen 9 9950X"
+    os: "Fedora 43"
+    workstation: "Ship of Theseus"
+  related_repos:
+    - https://github.com/Limen-Neural/corinth-canal
+    - https://github.com/Spikenaut/Surrogate_Viz.jl
+---

README.md

@@ -1,27 +1,43 @@
 ---
 license: gpl-3.0
+pretty_name: "Metis-OLMoE Latent Telemetry: Spikenaut SNN Routing"
+language:
+  - en
+  - code
+tags:
+  - snn
+  - spiking-neural-network
+  - olmoe
+  - mixture-of-experts
+  - quantization
+  - neuromorphic
+  - telemetry
+  - semantic-routing
+  - latent-space
+  - cuda
+  - rust
+  - gguf
+  - saaq
+datasets:
+  - allenai/OLMoE-1B-7B-0125-Instruct-GGUF
+task_categories:
+  - text-generation
+  - feature-extraction
 ---
+
 # Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing
 
-<<<<<<< HEAD
-## 1. The Origin of my Neuromorphic journey and this project
-=======
 ## 1. The Origin of Spikenaut
+
 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?
 
-<<<<<<< HEAD
 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, the Spikenaut architecture was born. The true origin of this project is deeply personal—it stems from my switch from Computer Science to Electrical Engineering, the struggle to learn on my own terms, and the drive to build an architecture that adapts dynamically. Now, I am dropping these new SNN quantization models and datasets on Hugging Face to prove the math works and to keep the research completely open and transparent.
 
 ## 2. The Science: Semantic Attractor Clustering
-This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Spikenaut** SNN architecture. 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.
-=======
-When I pitched this idea, most people didn't believe it would be good AI training data (spike data conversion)would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, the Spikenaut architecture was born. The true origin of this project is deeply personal—it stems from my switch from starting Economics, Pre-med, Finance, Computer Science major switch to Electrical Engineering, the struggle to learn on my own terms, and the drive to build an architecture that adapts dynamically. Now, I am dropping these new SNN quantization models and datasets on Hugging Face to prove the math works and to keep the research completely open and transparent.
 
-## 2. The Science: Semantic Attractor Clustering
-This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Spikenaut** SNN architecture. The objective is to map the physical routing of LLM embeddings (specifically from the OLMoE-0125 Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.
->>>>>>> ec02242 (Added all neccessary data files.)>>>>>>> ec02242 (Added all neccessary data files.)
+This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Spikenaut** SNN architecture. 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 primary discovery documented here is **Semantic Attractor Clustering**. By applying L2 Normalization to F16-to-F32 casted embeddings, the SNN bounds the semantic pressure to a unit sphere. This prevents "Winner-Take-All" routing collapse and forces the network to organically balance the load. The resulting telemetry proves that the SNN physically routes different semantic concepts (e.g., abstract philosophy vs. rigid code syntax) into distinct, repeatable biological pathways.