ThoxMesh-Head-C6

Mesh Event and Slot Predictor — a THOX model trained from scratch: a 9,848-byte int8 TFLite classifier that triages mesh events on a node's microcontroller.

9,848-byte int8 TFLite model · Event accuracy 0.9979 (synthetic test set) · Action slots below gate — triage use only

Your AI. Your Data. Your Rules.™

Status: EVENT HEAD PASSES — ACTION SLOTS BELOW GATE. Event accuracy 0.9979 and both event-class F1 scores pass their gates; command, target, and argument slot accuracies do not (0.9608 / 0.7843 / 0.4519). Do not ship the action path on these weights; triage use only. Training data is synthetic — field behaviour is unmeasured. The full gate table is under Status in the card.

Summary

ThoxMesh-Head-C6 is a THOX model trained from scratch: a 9,848-byte int8 TFLite classifier that runs on a THOX mesh node's microcontroller and predicts, from a 16-feature radio/sensor event vector, the event class plus action slots (target, command, argument). It is the triage head that decides which mesh events are worth waking a larger model for. It emits a structured action line assembled from enumerated slots, so its output is always well-formed; well-formed is not the same as correct, and the measured results below say exactly where it stands.

Highlights

• Trained by THOX from scratch: no external base model — a 9,848-byte int8 TFLite classifier for a 16-feature radio/sensor event vector.
• Event head passes its gates: event accuracy 0.9979, motion F1 0.9903, sensor_alert F1 0.9862 on the synthetic test set.
• Action slots fail their gates and are labeled as such on the card: the remaining error is a task-definition question, recorded rather than tuned around.

Specifications

Base model	None — trained by THOX from scratch
Model size	9,848 bytes (int8 TFLite)
Format	TFLite / LiteRT-Micro (plus C array export, model_data.cc)
Task	Tabular classification: event class + action slots from a 16-feature vector
License	Apache-2.0. Trained by THOX.ai LLC; the model and additions in this repo are covered under Apache-2.0.
This repository contains	The int8 TFLite model, C export, and meta.json (feature and class orders — the machine-readable source of truth)

Files

File	Size	SHA-256
thoxmesh_head_c6_int8.tflite	9,848 B	983ce6f767cfc3fada4d8138869e828b5cbb51d636a04cd0a2116da9c827973b
model_data.cc / meta.json / .gitattributes / model_data.h	—	Supporting files (see repo)

Status: read before integrating

Gate	Threshold	This build
Event accuracy (int8)	≥ 0.98	0.9979	Pass
motion F1	≥ 0.97	0.9903	Pass
sensor_alert F1	≥ 0.97	0.9862	Pass
Command-slot accuracy	≥ 0.97	0.9608	Fail
Target-slot accuracy	≥ 0.95	0.7843	Fail
Argument-bucket accuracy	≥ 0.90	0.4519	Fail

The event head is production-quality. The three action slots are not: do not ship the action path on these weights. The remaining target/argument error is a task-definition question (the input vector does not carry the information those labels need), not a tuning gap, and it is recorded here rather than tuned around.

Run it

This is an int8 TFLite / LiteRT-Micro classifier, not a language model; it is flashed with the firmware toolchain and is not served through llama.cpp or Ollama.

hf download Thox-ai/ThoxMesh-Head-C6 --local-dir ./ThoxMesh-Head-C6

The repo's meta.json is the machine-readable source of truth for the feature and class orders; generate firmware headers from it, never hand-code the orders. Firmware must clamp predicted arguments regardless of prediction, and the head never executes an action itself: it emits a line that the host validates against policy and the live roster.

Platforms

Platform / device	Run path
LiteRT-Micro / TFLite Micro (mesh node microcontroller)	Flashed with the firmware toolchain (see Run it)
Hugging Face	Download the model and meta.json from this repo

Intended use

• Mesh event triage on the node: classify radio/sensor events and decide wake-ups.
• Out of scope: direct actuation on these weights (see Status), and medical, legal, financial, safety-critical, or other high-stakes decisions without human review.

Training data and procedure

Trained by THOX from scratch as part of the THOX small-model research line. The corpus and procedure are as described in this card at release; no external base model was used.

Evaluation

Measured results are the gate table above. Training data is synthetic: generated from published cluster and attribute identifiers and realistic radio ranges, not captured from hardware. The perfect wake-up separation in testing reflects that generator, not field performance; a labelled capture from a populated mesh is what would make these numbers field claims.

Limitations

• Action-slot accuracy is below gate on target and argument prediction; triage use only.
• Synthetic-data training: field behaviour is unmeasured.
• Behaviour reflects the generated data distribution; unusual real-world event mixes may classify worse.

License

Apache-2.0. Trained by THOX.ai LLC; the model and additions in this repo are covered under Apache-2.0.

THOX.ai — Your AI. Your Data. Your Rules.™

Provenance

Published by THOX.ai as Thox-ai/ThoxMesh-Head-C6. THOX from-scratch research line; no upstream base model. This card was conformed to THOX Model Card Standard v1.0 on 2026-10-31 (frontmatter, tags, header, files, and section structure); the technical content is as published at release.

Research

THOX MeshStack: Capacity-Priced Pipeline Sharding, Grant-Gated KV Transfer and Lease-Fenced Device Roles for a Private Household Mesh

THOX.ai Research technical report v1.1.0, 4 November 2026. Craig Ross and Tommy Xaypanya, THOX.ai LLC. DOI: https://doi.org/10.5281/zenodo.23969070 — Paper page: https://thox.ai/research/papers/meshstack — PDF: https://thox.ai/papers/meshstack-1.1.0.pdf

Citation

@misc{thoxai2026thoxmeshheadc6,
      title={ThoxMesh-Head-C6},
      author={THOX.ai},
      year={2026},
      url={https://huggingface.co/Thox-ai/ThoxMesh-Head-C6}
}