Elamur Groupantsgparse-cli-training
The groupant beside gparse/src/main.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.
Facts
Implementation material. A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.
Components
| component | label | where |
|---|---|---|
| approximant | partial | approximant |
| ast | partial | ../syntax.json |
| cost | symbolic source derived | invariant |
| descriptant | present | descriptant |
| invariant | partial | invariant |
| tests | unexecuted source derived scope | tests |
Obernants
The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.
| id | type | label | where |
|---|---|---|---|
behavior_complement | behavior_complement | partial source derived model | behavior_complement |
reverse_reconstruction | reverse_reconstruction | partial source derived model | reverse_reconstruction |
schedule | schedule | partial source derived model | schedule |
sampling | sampling | partial source derived model | sampling |
checkpoint | checkpoint | partial source derived model | checkpoint |
partial_effect | partial_effect | partial source derived model | partial_effect |
Model text
descriptant
Source-bound description
Training composes filtered data, optional initialization, sampled plans, joint cross entropy and AdamW; saves strict best full-dev mean.
Epoch 2026-10-31
Prospective model was recorded before edits. Added tests use bounded synthetic fixtures; production algorithms unchanged. Coverage is partial, not proof. No trained corpus, live teacher or throughput experiment was executed.
approximant
Forward approximant
Training composes filtered data, optional initialization, sampled plans, joint cross entropy and AdamW; saves strict best full-dev mean.
Domain: valid source-specific dimensions and resources stated in invariant; no automatic production substitution. Numerical observations are finite checks, not an end-to-end relative bound. Executable Rust surrogate: unsupported gap; existing exact tests are not surrogates. Future metric-specific caps may be 1/2/5/10%, never above 10%.
invariant
Invariant and cost
Quick probes do not select checkpoints; output files can be partially updated on later failure.
Cost
Epochs times sampled batch network/backward/AdamW plus dev inference; O(parameters+corpus+batch graph) memory.
Allocation, finite arithmetic, platform dimensions and external failures are not universally certified.
models
Typed model catalog
behavior_complement
Quick-dev proxy cannot select checkpoint when dev sets exist.
reverse_reconstruction
Saved weights alone omit optimizer moments, sample RNG and checkpoint trajectory; antecedents are nonunique.
schedule
warm=min(est,100 init else2000); total=est*epochs; lr floors decay at0.02.
sampling
Always sentences included; others sample each epoch, first plan reused for epoch1.
checkpoint
Strict mean>best and full_dev/no-dev guard; last epoch always full.
partial_effect
Creates metadata before epochs, saves weights on improved epoch and rewrites log afterward; no transaction.
tests
Checks and limits
This exact fragment remains source-derived and unexecuted in this epoch. Parent/sibling selected evidence, not runtime coverage of this fragment: wave30::score_zip_and_plan_failure_precedence; train/eval/annotate external effects remain unexecuted
Selected commands run twice debug and twice release; exact receipts and source-to-test mapping retained in wave30 gparse external run directory. Tests do not certify allocation failure, all NaN/overflow inputs, trained-model accuracy or corpus timing. Compiled mutants cover selected boundaries; per-source coverage is recorded in the external map, not presumed.
This page in Markdown → https://md.groupants.elamur.ai/free/en/mova/gparse/src/main.rs.groupant/training.groupant/