---

name: training description: "Routes OpenNMT-py training, checkpoint continuation, distributed layout, alignment, LoRA, and quantized fine-tuning workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training

Use this sub-skill when the task is to run or debug OpenNMT-py model training with onmt_train, including model architecture choices, optimizer schedules, GPU layout, checkpoint continuation, vocabulary update, alignment training, pretrained embeddings, LoRA, gradient checkpointing, or 4/8-bit fine-tuning.

Use this route when

• The request mentions onmt_train, a training YAML, train_steps, valid_steps, save_model, train_from, reset_optim, or update_vocab.
• The user needs to choose or debug RNN, Transformer, language-model, summarization, or fine-tuning options.
• The task involves world_size, gpu_ranks, CUDA_VISIBLE_DEVICES, multi-node rank slices, or training throughput.
• The task involves pretrained embeddings, frozen word vectors, supervised alignment loss, copy-attention summarization, LoRA adapters, gradient checkpointing, or quantized layers.

Do not use this route when

• The task is only corpus discovery, tokenizer transforms, or vocabulary building before training; use ../data-preparation/ first.
• The task is translation, scoring, server deployment, decoding, or CTranslate2 inference; use ../inference/.
• The task is checkpoint averaging, release conversion, external checkpoint conversion, or LoRA merge after training; use ../conversion/.

Start here

1. Read references/training-configs.md to select the smallest valid training pattern and understand the parser constraints.

2. Run the bundled inspector before launching a long job:

   python scripts/inspect_train_config.py train.yaml
   

3. For real training, run:

   onmt_train -config train.yaml
   

4. If the inspector reports data or vocabulary failures, fix them with ../data-preparation/ before retrying training.

5. If the training job fails after launch, use references/troubleshooting.md to map the failure to config validation, distributed runtime, checkpoint, alignment, embedding, LoRA, quantization, or memory causes.

Operating rules

• Treat YAML config checks as a safety gate, not as proof that the model will fit memory or converge.
• Prefer explicit YAML over long command lines for training because configargparse merges nested data, vocab, model, optimizer, and distributed options in one place.
• When continuing from a checkpoint, decide whether the goal is exact resume, new optimizer states, changed model options, or vocabulary update before setting reset_optim and override_opts.
• Do not claim 4/8-bit or LoRA viability without the required optional packages and GPU memory checks; CPU-only checks only validate config shape.
• Keep source-data paths, checkpoint paths, and private environment details in the user's config or logs, not in reusable instructions.