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name: training-basics description: "Enables future agents to prepare MiniMind training data and operate tokenizer, pretraining, SFT, LoRA, checkpoint, DDP, and tiny validation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MiniMind Training Basics

Use this sub-skill when the task is to prepare MiniMind training data, choose core model/training knobs, start or resume pretraining, run full supervised fine-tuning, run LoRA fine-tuning, validate tokenizer/data compatibility, or perform a safe tiny training smoke.

Load order

• For JSONL schemas, tokenizer special tokens, chat-template behavior, tool-call fields, and reasoning fields, read references/data-formats.md.
• For tokenizer, pretraining, full SFT, LoRA, DDP, checkpoint resume, logging, mixed-precision, gradient accumulation, and compile command routes, read references/workflows.md.
• For MiniMindConfig, MiniMindForCausalLM, model-size knobs, RoPE/YaRN context, output weight names, checkpoint state, and LoRA merge semantics, read references/model-and-checkpoints.md.
• For missing files, invalid JSONL, tokenizer mismatch, missing weights, LoRA compile incompatibility, device choice, resume, logging, and output-directory failures, read references/troubleshooting.md.

Bundled safe helpers

• Validate pretrain/SFT JSONL and optional local tokenizer compatibility with scripts/validate_minimind_jsonl.py.
• Run a bounded random-tensor model forward/backward/generate smoke with scripts/tiny_training_smoke.py.

Both helpers are deterministic by default, perform no downloads, do not require credentials, and do not launch full training.

Boundaries

This sub-skill owns the core MiniMind training path: tokenizer validation/training settings, pretrain JSONL, SFT conversation JSONL, pretraining, full SFT, LoRA SFT, checkpoint resume, DDP launch, data/model configuration, and tiny validation.

Route API servers, web UI, local chat, tool-call evaluation, model export, and merged-weight serving to the inference-serving sub-skill. Route DPO, distillation, PPO, GRPO/CISPO, and Agentic RL to the rlhf-agentic sub-skill.