B8 multilingual specialist (Indic-focus first)

status: not-started (blocked-by A7)
owner: unassigned
created: 2026-06-13
parent_plan: docs/PLAN.md §3 Tier B (B8)
related_prds: A1-first-specialist-tool-caller.md (A1’s recipe template), docs/research/indic_evals.md (eval surface)

PRD — Multilingual specialist on top of Sarvam-Edge / Airavata base

Goal

Train a multilingual SFT/LoRA specialist on a Sarvam-Edge or Airavata base (both are Indic-LLM bases optimized for English + 10+ Indian languages) for a Mac-runnable Indic-capable agent. Ship gate: beats the 0-shot base on the MILU eval (posttrainllm eval-indic) by ≥ 3pp average across at least 3 Indian languages.

Different from A1/B1: A1/B1 are domain specialists; B8 is a language-coverage specialist. Same recipe shape; different acceptance axis.

Why now

• Indic evals are wired (posttrainllm eval-indic, smoke-validated per PLAN.md). The eval surface exists; the trained-for-Indic specialist doesn’t.
• The Indian-language LLM ecosystem (Airavata, OpenHathi, Sarvam) has shipped solid open bases. Specializing on top of them is the cheapest way to deliver a usable Indic agent without a multi-month pretrain.
• Distinct user need from the English specialists: a Mac-running Hindi/Tamil/Telugu agent has no good open competitor at the ≤ 4B size class.

Scope — in

• Base candidates: Sarvam-Edge (newest), Airavata (well-tested), or OpenHathi as fallback. V1 picks one based on Mac MLX compatibility; document in decision_log.md.
• Training data: Indic SFT corpora (Aya from CohereForAI, IndicSUPERB, MILU’s training split). All open.
• Recipe: mirror A1’s recipe shape (scripts/recipes/b8-indic.sh).
• Eval: existing posttrainllm eval-indic extended to report per- language scores in MILU + IndicGenBench-XQuAD.
• Ship gate: average across Hindi/Tamil/Telugu MILU ≥ base
  • 3pp under B23 K=3 protocol.

Scope — out

• Full multilingual (50+ languages, mBERT-style). V1 is Indic-focused.
• Cross-lingual transfer experiments. Train and eval same language family for V1.
• TTS / Indic speech. Distinct PRD if needed (consumes 5.6).

Files to touch

Acceptance criteria

• Hindi MILU ≥ base + 3pp, Tamil + Telugu similarly.
• No regression on English BFCL ≥ -2pp (specialist shouldn’t break English capability).
• B8 row appears on the SLM leaderboard alongside A1 / B1.

Reference patterns

• A1’s recipe.
• Airavata paper — base characteristics + Indic SFT recipe references.
• docs/research/indic_evals.md — eval landscape.

Open questions

• Sarvam-Edge vs Airavata. Recommendation: pick at training- time based on MLX compatibility check; both are valid V1 bases.