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[SKEEP-006]: Input front ends and model identity

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
15/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
huggingface, kotlin
Ambito
backend, machine-learning

Direzione di ricerca

Read the draft proposal at docs/modules/skeep/pages/006-input-frontends-and-model-identity.adoc and its registration in docs/modules/skeep/nav.adoc. The proposal is still a draft, and implementation is already underway in a fork; wait for the maintainer to accept it and review the linked implementation work before taking on any sub-issue. Done means the proposal’s status and follow-up issues reflect the agreed scope.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

skeep tracking

Proposal document: docs/modules/skeep/pages/006-input-frontends-and-model-identity.adoc — prepared in a personal fork: https://github.com/michalharakal/SKaiNET-core-upstream/blob/feature/skeep-006-input-frontends/docs/modules/skeep/pages/006-input-frontends-and-model-identity.adoc
Status: Draft
Branch: feature/skeep-006-input-frontends (fork; the upstream pull request follows once the #1321 good-first-issue lanes have landed, so the contributors working them are not disturbed)

Trigger

Public Kotlin API (new modules skainet-model-identity, skainet-frontend-core, skainet-frontend-text; the sk.ainet.io.tokenizer package becomes deprecated aliases) · compatibility/migration policy (three-release deprecation of the superseded identity switches and of the SKaiNET-transformers legacy tokenizer stack) · runtime integration (the resolver is what model loaders, Java callers and cartridge facades use).

Summary

A checkpoint is weights, an identity and the recipe that turns raw input into the tensors the weights expect. Today the recipe is re-derived downstream: SKaiNET-transformers carries a second tokenizer stack that disagrees with the engine's, model identity is split three ways in the engine and a fourth way downstream, and audio/image preprocessing has no home at all. The proposal introduces FrontEnd<I, O> with Tokenizer as its first implementor, a string-based ModelIdentity with an immutable ModelFamilyRegistry that weight mapping and front ends both key on, and FrontEndResolver as the single entry point. Algorithms live in the engine, golden-tested against the Hugging Face tokenizers library; families (downstream) contribute policy only. Audio and image features follow on the same contract in later lanes.

Related DARC features

  • #1321 — tokenizer.json parity for BPE encoders (the lane fixes #1323–#1326 are folded into the moved implementations; the lanes stay open for their contributors and are closed by the upstream PR of this proposal or by their own PRs, whichever lands first)
  • SKaiNET-developers/SKaiNET-transformers#471 — legacy tokenizer defects and parity gate (its lanes become the migration gate)
  • SKaiNET-developers/SKaiNET-transformers#330 — two GGUF tokenizer paths disagree (answered by ModelFamilyDescriptor + FrontEndResolver)

Sub-issues

Filed once the proposal is Accepted: engine F2 (contract, identity, resolver — implemented in the fork branch), F4 log-mel front end, F5 image front ends; SKaiNET-transformers F3 (consume engine tokenizers — implemented in the fork branch feature/skeep-006-frontend-consumer of https://github.com/michalharakal/SKaiNET-transformers), F6 audio families.

Status upkeep

  • Proposal registered in docs/modules/skeep/nav.adoc and the "Current Proposals" table (fork branch)
  • Maintainer moved status to Accepted
  • Implementation PR(s) linked here and flipped the proposal's Status: to Implemented
Lingua principale
Kotlin
Stelle
52
Fork
15
Merge medio
1g 8h
PR unite (30g)
37

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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