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kermt-add-cmim-pretrain
Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a one-time ckpt-conversion step prepended.
skills/bionemo-kermt-add-cmim-pretrain/SKILL.md
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BENCHMARK.mddoc · 7,784 bytesSKILL.mdcore · 8,631 bytesSKILL.mdcore · 8,631 bytesSKILL.mdcore · 8,631 bytesSKILL.mdcore · 8,631 bytesSKILL.mdcore · 8,631 bytesconfig/defaults_pretrain.jsonasset · 2,699 bytesevals/evals.jsonasset · 5,784 bytesscripts/_utils.pyruns · 14,483 bytesscripts/check_checkpoint.pyruns · 20,605 bytesscripts/check_data.pyruns · 12,185 bytesscripts/kermt_container.shruns · 19,370 bytesscripts/prepare_data.pyruns · 37,383 bytesscripts/run_pretrain_local.pyruns · 35,597 bytesscripts/upgrade_to_hybrid.pyruns · 18,647 bytesskill-card.mddoc · 4,474 bytesskill.oms.sigasset · 6,497 bytes
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# 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