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生産性・共同作業 · SKILL.md

post-training-rl-and-multimodal

post-training-rl-and-multimodal

Route optional Megatron ModelOpt, RL/GRPO, VLM, and multimodal/MIMO workflows with explicit dependency and hardware limits.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: post-training-rl-and-multimodal description: "Route optional Megatron ModelOpt, RL/GRPO, VLM, and multimodal/MIMO workflows with explicit dependency and hardware limits." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

post-training-rl-and-multimodal

Use this sub-skill for ModelOpt quantization/pruning/distillation/export, Megatron RL/GRPO, VLM, audio/video, and MIMO workflows. These surfaces are optional and usually need more than the minimal Megatron Core install.

Read first

Route by task

TaskRoute
Quantize/prune/export a modelConfirm ModelOpt install, checkpoint/model compatibility, and target artifact before running.
Run GRPO/RL post-trainingConfirm reward/environment configs, rollout backend, packed sequence schema, and GPU topology.
Train a VLM/MIMO modelConfirm media manifest/schema, encoder/model-provider, tokenizer, and heterogeneous topology.
Only run a standard GPT pretrainRoute to ../training-cli-and-data/SKILL.md.
Missing ModelOpt/vision/audio dependencyRoute to ../install-and-environment/SKILL.md for a minimal extra install; do not install all dev deps blindly.

Safety

  • Do not start long RL training, model conversion, or server processes as a probe.
  • Use --help, import checks, and tiny mock fixtures first.
  • Treat external reward services, HF tokens, media datasets, and model checkpoints as user-provided resources; never embed credentials.
  • Expect H100/Blackwell and large-memory assumptions for many current examples; document unverified hardware rather than substituting a CPU check.

Boundaries

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