Added metadata manifest to actually represent Model lineage.

model_metadata_manifest.json

@@ -0,0 +1,199 @@
+{
+  "model_id": "Metis-OLMoE-Latent-Telemetry",
+  "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",
+  "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",
+  "primary_discovery": "Semantic Attractor Clustering - SNN physically routes different semantic concepts into distinct, repeatable biological pathways",
+  
+  "model_lineage": {
+    "origin": {
+      "model": "OLMoE-1B-7B-0125-Instruct",
+      "organization": "AllenAI",
+      "architecture": "Mixture of Experts (MoE)",
+      "parameters": "1B active / 7B total",
+      "quantization": "GGUF",
+      "link": "https://huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct-GGUF"
+    },
+    "derivation_pipeline": {
+      "name": "corinth-canal",
+      "purpose": "SNN quantization pipeline",
+      "repository": "https://github.com/Limen-Neural/corinth-canal",
+      "technique": "SAAQ (Semantic Attractor Architecture Quantization)"
+    },
+    "analysis_tools": {
+      "visualization": {
+        "name": "Surrogate_Viz.jl",
+        "repository": "https://github.com/Spikenaut/Surrogate_Viz.jl",
+        "purpose": "Symbolic regression and latent space visualization"
+      }
+    }
+  },
+
+  "experiment_chronology": [
+    {
+      "phase": 1,
+      "name": "Synthetic Baseline (Smoke Test)",
+      "directory": "origin_hardware_baselines/",
+      "input": "Synthetic sine wave",
+      "result": "Verified GPU temporal loop (10,000 ticks) and basic biological fatigue without crashing CUDA context",
+      "hardware_baseline": "Resident Evil 4 Remake path tracing telemetry",
+      "files": ["RE4_path_tracing_telemetry.csv"],
+      "discovery": "Gaming workloads create dynamic 'heartbeat' vs static crypto-mining data"
+    },
+    {
+      "phase": 2,
+      "name": "The F16 Magnitude Collapse (Unbounded)",
+      "directory": "first-day-testing-real-weights/first-test-falied/",
+      "input": "Real LLM embeddings (OLMoE)",
+      "issue": "Misconfigured CUDA kernels searching for F32 instead of F16",
+      "result": "Routing collapse - unbounded electrical pressure caused single walker (~620) to become a 'blackhole'",
+      "files": ["latent_space_exploration_first_real_attempt.png"],
+      "discovery": "Raw F16-to-F32 extraction without scaling causes routing collapse"
+    },
+    {
+      "phase": 3,
+      "name": "Attractor Discovery (L2 Normalization)",
+      "directory": "first-day-testing-real-weights/second-test/",
+      "input": "Teaching OMLoE the language of SNN",
+      "fix": "L2 Normalization applied to voltage",
+      "result": "Energy settled into Walker 2000 with secondary echoes at Walkers 700 and 1450",
+      "files": ["map_olmoe_english_logic.png"],
+      "discovery": "L2 Normalization prevents Winner-Take-All collapse by bounding semantic pressure to unit sphere"
+    },
+    {
+      "phase": 4,
+      "name": "Rust Syntax (The True Victory)",
+      "directory": "first-day-testing-real-weights/third-test/",
+      "input": "fn main () { println!(); }",
+      "result": "Routing changed completely from English prompt - code syntax routed to different biological neighborhood",
+      "files": [
+        "map_olmoe_rust_syntax_logic.png",
+        "map_olmoe_rust_syntax_logic.txt",
+        "snn_latent_telemetry.csv"
+      ],
+      "discovery": "L2 Normalization forces hardware to dynamically adapt to data type - different semantic concepts = different pathways",
+      "walker_range": "600-800 frequency band",
+      "key_metrics": {
+        "ticks": 10000,
+        "best_walker_range": "129-816",
+        "elapsed_us_range": "206-4445"
+      }
+    },
+    {
+      "phase": 5,
+      "name": "Math Logic Clustering",
+      "directory": "first-day-testing-real-weights/fourth-test/",
+      "input": "The derivative of a constant is mathematically zero.",
+      "result": "Mathematical logic routed to exact same 600-800 frequency band as Rust syntax",
+      "files": [
+        "map_olmoe_math_logic.png",
+        "telemetry_olmoe_math_logic.txt"
+      ],
+      "discovery": "Semantic Attractor Clustering - SNN maps highly structured logic tasks (math and code) to adjacent biological neighborhoods to conserve energy"
+    }
+  ],
+
+  "key_concepts": {
+    "walker": {
+      "definition": "Pulse of electrical energy (spike) that physically explores network to find path of least resistance",
+      "analogy": "Electrical impulses in biological brain"
+    },
+    "l2_normalization": {
+      "purpose": "Prevents any single neuron from becoming dominant",
+      "effect": "Mimics biological brain's energy distribution",
+      "mathematical_result": "Bounds semantic pressure to unit sphere"
+    },
+    "semantic_attractor_clustering": {
+      "definition": "SNN physically maps different semantic concepts to distinct, repeatable biological pathways",
+      "example": "Abstract philosophy (2000-route) vs rigid code syntax (600-800 band)"
+    },
+    "fatigue_mechanics": {
+      "definition": "Neurons that fire too much become less responsive",
+      "purpose": "Prevents energy overload and enables network adaptation"
+    }
+  },
+
+  "hardware_environment": {
+    "workstation": "Ship of Theseus",
+    "gpu": "ASUS ProArt GeForce RTX 5080 (16GB VRAM)",
+    "cpu": "AMD Ryzen 9 9950X",
+    "os": "Fedora 43",
+    "implementation": "Custom Rust/CUDA corinth-canal"
+  },
+
+  "data_structure": {
+    "routing": "CSV files containing routing and latent telemetry data",
+    "experiments": "Test configurations and variants",
+    "results": {
+      "plots": "Visualization of SNN routing paths and firing density",
+      "raw_telemetry": "Original tick-by-tick log files"
+    }
+  },
+
+  "file_manifest": {
+    "first-day-testing-real-weights/first-test-falied/": {
+      "latent_space_exploration_first_real_attempt.png": "Visualization of routing collapse (blackhole at walker ~620)"
+    },
+    "first-day-testing-real-weights/second-test/": {
+      "map_olmoe_english_logic.png": "English text semantic routing through Walker 2000"
+    },
+    "first-day-testing-real-weights/third-test/": {
+      "map_olmoe_rust_syntax_logic.png": "Rust code syntax routing visualization (600-800 band)",
+      "map_olmoe_rust_syntax_logic.txt": "Raw tick data for Rust syntax test (10,000 ticks)",
+      "snn_latent_telemetry.csv": "CSV telemetry export for Rust syntax test"
+    },
+    "first-day-testing-real-weights/fourth-test/": {
+      "map_olmoe_math_logic.png": "Mathematical logic routing visualization (600-800 band)",
+      "telemetry_olmoe_math_logic.txt": "Raw tick data for math logic test (10,000 ticks)"
+    },
+    "origin_hardware_baselines/": {
+      "RE4_path_tracing_telemetry.csv": "Resident Evil 4 thermal telemetry that inspired SAAQ equations"
+    }
+  },
+
+  "usage": {
+    "primary": "Feed SymbolicRegression.jl to discover new equations for SNN quantization",
+    "secondary": "Train pure native Spikenaut SNN",
+    "visualization": "Study spiking behavior, routing stability, and adaptive quantization"
+  },
+
+  "related_repositories": [
+    {
+      "name": "corinth-canal",
+      "url": "https://github.com/Limen-Neural/corinth-canal",
+      "purpose": "SNN quantization pipeline"
+    },
+    {
+      "name": "Surrogate_Viz.jl",
+      "url": "https://github.com/Spikenaut/Surrogate_Viz.jl",
+      "purpose": "Symbolic regression and visualization"
+    }
+  ],
+
+  "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"
+  },
+
+  "tags": [
+    "snn",
+    "spiking-neural-network",
+    "olmoe",
+    "mixture-of-experts",
+    "quantization",
+    "neuromorphic",
+    "telemetry",
+    "semantic-routing",
+    "latent-space",
+    "cuda",
+    "rust",
+    "gguf"
+  ],
+
+  "created_at": "2025-04-16",
+  "updated_at": "2025-04-16"
+}