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

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
15/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
huggingface, kotlin
Domain
backend, machine-learning

Research direction

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.

Written by the indexing model from the issue text.

Description

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package becomes deprecated aliases) · compatibility/migration policy (three-release deprecation of the superseded identity switches and of the SKaiNET-transformers legacy tokenizer stack) · runtime integration (the resolver is what model loaders, Java callers and cartridge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
Dominant language
Kotlin
Stars
52
Forks
15
Avg merge
1d 15h
Merged PRs (30d)
36

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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