{ "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" }