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name: conversion description: "Routes OpenNMT-py checkpoint averaging, release, CTranslate2, external-family conversion, vocabulary or embedding extraction, and LoRA merge workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Conversion

Use this sub-skill when the task is about OpenNMT-py checkpoint maintenance or model-format conversion rather than training or translation.

Use this route when

• The request says average checkpoints, strip optimizer state, release a model, convert to CTranslate2, quantize for CT2, or inspect a saved checkpoint.
• The user needs to convert a LLaMA, Mistral, Mixtral, Phi, T5, MPT, Falcon, RedPajama, XGen, or Hugging Face-style checkpoint into OpenNMT-py format.
• The user needs to extract source/target vocabularies, inspect or export embeddings, convert pretrained embeddings to tensors, or check vocab availability.
• The user needs to merge or concatenate LoRA weights into a compatible base OpenNMT-py model.

Do not use this route when

• The main work is building corpora or tokenizers before training; use the data-preparation route.
• The main work is choosing architecture, optimizer, LoRA training options, or checkpoint continuation; use the training route.
• The main work is running inference or serving an already released model; use the inference route.

Start here

1. Read references/checkpoint-tools.md to identify the exact checkpoint family, expected files, command/API surface, validation checks, and output format.

2. Before changing a checkpoint, inspect the input with scripts/check_checkpoint_file.py:

   python scripts/check_checkpoint_file.py path/to/model.pt --json
   

   The helper maps tensors to CPU or fake tensors and reports top-level keys, option fields, vocab sides, tensor counts, and conversion-readiness hints.

3. Read references/troubleshooting.md before running weight-heavy conversion, CTranslate2 release, external/Hugging Face conversion, or LoRA merge.

4. Prefer package console commands for installed OpenNMT-py entry points: onmt_average_models and onmt_release_model. For utilities that were only source-tree tools, use the distilled contracts in references/checkpoint-tools.md and create a task-local reviewed helper instead of depending on any particular checkout path.

Primary workflows

• Average compatible checkpoints: check all inputs, confirm identical vocabulary and architecture options, then run onmt_average_models -models ... -output averaged.pt; add -fp32 when the release target must not preserve half precision.
• Release PyTorch or CTranslate2 models: use onmt_release_model --format pytorch to drop optimizer state, or --format ctranslate2 with an optional quantization mode to create a CT2 model directory.
• Convert external model families: use the family matrix and option contracts in references/checkpoint-tools.md; validate that the output checkpoint has model, generator, vocab, and opt sections before training or release.
• Extract vocabularies or embeddings: confirm vocab.src/vocab.tgt first, then use the reference recipes to write vocab text files or embedding tensors without relying on source checkout scripts.
• Merge LoRA weights: verify the base and LoRA checkpoints share architecture/vocab assumptions, merge for inference or concatenate for resumed training, then inspect the output and release it if needed.

Safety and validation rules

• Treat torch.load checkpoints as trusted pickle files; do not inspect arbitrary untrusted checkpoints without an isolation plan.
• Conversion and inspection should not place tensors on GPU. Use CPU/fake-tensor loading for checks, and only select GPU for downstream inference/training if the owning route requires it.
• Never average checkpoints from different vocabularies, architectures, model tasks, or incompatible LoRA/base states.
• For CT2, validate that the output is a directory with CT2 files such as model.bin, config.json, and vocabulary.json, not a .pt checkpoint.
• Keep real model paths, Hugging Face tokens, cache directories, and machine-specific paths out of skill notes and reusable scripts.

Bundled files

• references/checkpoint-tools.md — workflows, command/API contracts, external family matrix, vocab/embedding and LoRA guidance.
• references/troubleshooting.md — symptoms, likely causes, and recovery steps for conversion-specific failures.
• scripts/check_checkpoint_file.py — safe checkpoint inventory helper for OpenNMT-py .pt metadata and tensor summaries.