kermt-add-cmim-pretrain

Convert a grover_base checkpoint (legacy original-GROVER grover.encoders.* or modern kermt.encoders.*, with or without vocab heads) into a fully-formed hybrid (cMIM + vocab) checkpoint, then continue pretraining on the user's corpus as hybrid.

This is a thin wrapper: upgrade_to_hybrid.py produces a new ckpt that classifies as model_type: hybrid via check_checkpoint.py, and the rest of the workflow is identical to kermt-continue-pretrain.

Status: experimental. This workflow is functional end-to-end but has not been benchmarked against the manuscript's from-scratch hybrid training (which produces the released checkpoint). Use as an experimental alternative to kermt-pretrain-scratch when you want to extend an existing grover_base checkpoint rather than restart from random init. Validate downstream performance on your own benchmark before relying on the upgraded ckpt for production work.

Skill and runtime paths

Set SKILL_DIR to the absolute path of this installed skill directory. Export KERMT_REPO as the absolute path to the KERMT checkout used for model execution. The bundled container helper mounts that checkout at /workspace and this skill at /skill (read-only). Commands inside the container use /skill/scripts/; defaults are bundled in config/.

Hardware requirements

Same as kermt-continue-pretrain (the cMIM decoder adds parameters but not substantially; VRAM headroom should be fine). The upgrade step itself is fast (~5 s) and CPU-only, only the subsequent continue-pretrain consumes GPU.

When to invoke

• User has a grover_base checkpoint (encoder-only or with vocab heads) and wants to extend it into a hybrid (vocab + cMIM contrastive) pretrain.
• Useful for adding the SMILES-reconstruction contrastive objective to a pretrained encoder without restarting pretraining from scratch (which kermt-pretrain-scratch would do at days-scale).