plawanrath/static-student-tel-mlx-S-fp

A float byte-level telemetry student (S: 4 layers, d_model 192, 4 heads), trained from scratch, from the static-student project (Weights as Constants: compiling distilled task models into native executables as read-only data). Code, RUNBOOK and results: https://github.com/plawanrath/static-student.

What the model does

Input: the raw bytes of one telemetry payload (a metric line in one of many ad-hoc formats). Output: per-byte span pointers for the metric name, value and unit, a unit class, a value score, and a defer logit. The student is meant to run only on payloads a frozen regex parser rejects, and to answer only when its confidence clears a threshold certified on the integer kernel that ships; everything else is deferred. It is a research artifact for one synthetic task family, not a general log parser.

Training

curriculum	data/curriculum/telemetry-mlx-s0-x12k (written by Mistral Small 3.2 24B from the spec line, expanded with drift rewrites)
families (train / dev / defer-fit)	9624 / 1183 / 1193
rendered training rows	1,000,000
epochs	3
seed	0
parameters	1,881,034
dev struct-exact, final	99.31%
defer-head fit: error rate / BCE	0.2090 / 0.0607
wall time (Apple silicon, MPS)	45 min

Files

• model.safetensors, config.json — framework-neutral weights and constructor arguments.
• student_fp.pt — the original PyTorch checkpoint the RUNBOOK commands expect (torch.load, dict with config, state).
• train_report.json — the training log above.

How to load

git clone https://github.com/plawanrath/static-student && cd static-student && bash scripts/env/setup.sh

from static_student.student.release import load_released
model, cfg = load_released("plawanrath/static-student-tel-mlx-S-fp")

To compile it into a certified binary (needs a C compiler, make, RE2):

.venv/bin/python scripts/release/download_hf.py tel-mlx-S-fp
.venv/bin/python -m static_student.build --spec specs/telemetry.spec --model models/tel-mlx-S-fp --out build/tel-mlx-S-fp

Limitations

• Trained and evaluated on synthetic telemetry payloads rendered from format families; never on production traffic. Numbers above are on held-out synthetic families.
• The certificate is a statistical bound on the rate of wrong accepted answers under the calibration distribution; it says nothing about inputs from other distributions.
• The research project was concluded without a publication; the model is released as-is for reuse and reproduction and is not maintained.

Citation

If you use this checkpoint, please cite the repository:

@software{rath2026staticstudent,
  author  = {Rath, Plawan Kumar},
  title   = {static-student: Weights as Constants --- compiling distilled task models into executables as read-only data},
  year    = {2026},
  doi     = {10.5281/zenodo.23202887},
  url     = {https://github.com/plawanrath/static-student},
  license = {Apache-2.0},
  version = {0.1.0}
}

CITATION.cff in the repository carries the same entry (GitHub's "Cite this repository" button).

Disclaimer

Plawan Kumar Rath (@plawanrath). This work was conducted in the author's personal capacity. The views expressed here are those of the author and do not necessarily reflect the views of Meta.