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name: distillation-and-post-training description: "MiniLLM, DPKD, and Tuna distillation and ranking-finetuning workflows for LMOps." disable-model-invocation: true metadata: disco-role: operating license: MIT

Distillation and Post-Training

Use this sub-skill for MiniLLM, DPKD, and Tuna workflows that distill or rank-finetune language models without entering VeRL-style experiential learning.

Route here when

• The task is MiniLLM SFT, KD, SeqKD, on-policy MiniLLM training, evaluation, exposure-bias analysis, data processing, model resource planning, or tensor parallel conversion.
• The task is DPKD training or evaluation, including the runner scripts, argument-group choices, DPO-style switches, and checkpoint/output planning.
• The task is Tuna probabilistic ranking or contextual ranking data preparation, GPT-4 provenance checks, or ranking-aware finetuning.

Route away

• GAD, OEL, OPCD, LLM-as-a-Coach, and OPO belong in ../rl-experiential-learning/SKILL.md.
• ResLoRA belongs in ../adaptation-and-training/SKILL.md.

Bundled helpers

• scripts/model_parallel_conversion_plan.py preflights checkpoint family, source/target MP sizes, and path layout before any conversion.
• scripts/check_tuna_ranking_data.py validates the minimal probabilistic and contextual ranking JSON shapes used by Tuna.

Read next

• references/minillm-workflows.md
• references/dpkd-workflows.md
• references/tuna-data-and-training.md
• references/troubleshooting.md

Working order

1. Decide whether the request is MiniLLM, DPKD, or Tuna.
2. Check the family reference and the bundled helper before any path-sensitive or schema-sensitive action.
3. Treat large model downloads, multi-node launches, and OpenAI-backed ranking generation as documented workflow steps, not cheap smoke tests.
4. If a failure looks like a shared LMOps issue, consult the parent skill's broad project index when available at ../../references/project-index.md.