Rename HF repo to SAAQ-Latent-Telemetry

README.md

@@ -68,9 +68,9 @@ dataset_info:
 ---
 
 [![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)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry)
 
-# Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing
+# SAAQ-Latent-Telemetry
 
 > 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.
 
@@ -81,7 +81,7 @@ dataset_info:
 - **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)
+- **Repository:** [huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry](https://huggingface.co/datasets/rmems/SAAQ-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)
@@ -147,7 +147,7 @@ The dataset documents the chronological progression from synthetic baselines to
 from datasets import load_dataset
 
 # Load the hardware telemetry dataset
-dataset = load_dataset("rmems/Metis-OLMoE-Latent-Telemetry", split="train")
+dataset = load_dataset("rmems/SAAQ-Latent-Telemetry", split="train")
 
 print(dataset.features)
 print(dataset[0])
@@ -237,16 +237,16 @@ This data behaves as "sensorimotor" stimulus for neural networks:
 ```bibtex
 @dataset{montoya_2026,
   author = {Raul Montoya Cardenas},
-  title = {Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing},
+  title = {SAAQ-Latent-Telemetry: Spikenaut SNN Routing},
   year = {2026},
   publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry}}
+  howpublished = {\url{https://huggingface.co/datasets/rmems/SAAQ-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
+Montoya Cardenas, R. (2026). SAAQ-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry
 
 ---
 

model_metadata_manifest.json

@@ -1,10 +1,10 @@
 {
-  "model_id": "Metis-OLMoE-Latent-Telemetry",
+  "model_id": "SAAQ-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": "apache-2.0 / mit",
-  "repository_url": "https://huggingface.co/datasets/rmems/Metis-OLMoE-Latent-Telemetry",
+  "repository_url": "https://huggingface.co/datasets/rmems/SAAQ-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",
@@ -175,8 +175,8 @@
   ],
 
   "citation": {
-    "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"
+    "bibtex": "@dataset{montoya_2026,\n  author = {Raul Montoya Cardenas},\n  title = {SAAQ-Latent-Telemetry: Spikenaut SNN Routing},\n  year = {2026},\n  publisher = {Hugging Face},\n  howpublished = {\\url{https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry}}\n}",
+    "apa": "Montoya Cardenas, R. (2026). SAAQ-Latent-Telemetry: Spikenaut SNN Routing [Dataset]. Hugging Face. https://huggingface.co/datasets/rmems/SAAQ-Latent-Telemetry"
   },
 
   "tags": [

origin_hardware_baselines/resident_evil_4/hf_dataset_card.md

@@ -44,7 +44,7 @@ from datasets import load_dataset
 import pyarrow.parquet as pq
 
 # Load the entire telemetry dataset as a single stream
-dataset = load_dataset("rmems/Metis-SMoE-Latent-Telemetry", split="train")
+dataset = load_dataset("rmems/SAAQ-Latent-Telemetry", split="train")
 
 print(dataset.features)
 print(dataset[0])