Kermt Add Cmim Pretrain
NVIDIAOfficialPublished by NVIDIA
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This workflow converts an existing grover_base checkpoint (encoder-only or with vocab heads) into a hybrid model by adding a randomly-initialized cMIM decoder and latent distribution. It then proceeds to continue pretraining on the user's provided corpus. This process allows users to incorporate contrastive objectives into their models without the computational cost of restarting pretraining from scratch.
What it does
- Validates the input checkpoint to ensure it is suitable for hybrid conversion.
- Prepares the training data and builds a fresh vocabulary from the user's corpus.
- Upgrades the checkpoint by adding a cMIM decoder and latent distribution.
- Launches a detached pretraining run using the newly created hybrid checkpoint.
- Provides audit logs and provenance for the upgraded checkpoint.
When to use it
- When you have a grover_base checkpoint and want to add SMILES-reconstruction contrastive objectives.
- When you want to extend an existing model's capabilities without performing a full from-scratch pretraining run.
- When you need to adapt a pretrained encoder to a new corpus while maintaining hybrid training objectives.
Advantages
- Saves significant time compared to restarting pretraining from scratch.
- Enables the use of contrastive learning objectives on existing encoder-only models.
- Maintains provenance by capturing upgrade summaries and input manifests.
- Ensures data integrity through automated validation of checkpoints and corpora.
Usage examples
- “Upgrade my grover_base checkpoint at ./models/base.pt to a hybrid model using my new dataset at ./data/corpus.csv.”
- The agent validates the checkpoint, prepares the data, creates an upgraded hybrid checkpoint, and initiates the continue-pretraining process.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/kermt-add-cmim-pretrain/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip kermt-add-cmim-pretrain.zip -d .agents/skills
Documentation by NVIDIA
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-scratchwhen 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-scratchwould do at days-scale).
For continuing an existing hybrid or cmim ckpt: use kermt-continue-pretrain
directly. For training a fresh model on a custom corpus: use
kermt-pretrain-scratch.
Inputs
Required:
--ckpt <path>— grover_base ckpt to upgrade. Validated viacheck_checkpoint.py --mode upgrade_to_hybrid; rejected if the ckpt already has a contrast head or task FFN.--csv <path>— pretrain corpus CSV. Same shape askermt-continue-pretrain's--csvinput.
Optional (same as kermt-continue-pretrain):
--val-csv <path>— separate validation CSV. Without it, prepare_data auto-splits by--val-frac 0.1.Training-hyperparameter overrides (
--epochs N,--batch-size N, lr triple,--warmup-epochs F, etc.).--vocab-loss-weight F/--latent-dim N/--contrastive-temperature F.--wandb-project NAME/--wandb-run-name NAME— optional Weights & Biases logging (run name honored only alongside a project). Off by default.--gpus 0,2.
Workflow
Let $KERMT_REPO be the path to your kermt repo checkout.
Pre-flight: check_system (same as
kermt-continue-pretrainstep 1).Compute run directory:
RUN_DIR=$KERMT_REPO/runs/add-cmim-pretrain_$(date -u +%Y-%m-%dT%H-%M-%SZ)Validate the input ckpt with
check_checkpoint --mode upgrade_to_hybrid. Abort onok: false. The validator rejects ckpts that already have contrast head (suggestkermt-continue-pretrain) or task FFN heads (the ckpt has been finetuned; suggest using the original pretrain checkpoint).Validate the corpus via
check_data --mode pretrain. Abort onok: false.Prepare the data with
--mode pretrain— without--vocab-dir. The upgrade builds fresh vocab heads sized to the corpus's vocab, so we wantprepare_datato produce a new vocab from the corpus rather than passing through the ckpt's old vocab (which may not even exist for encoder-only legacy grover_base ckpts):"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \ "python /skill/scripts/prepare_data.py --mode pretrain \\ --csv /data/<basename> --out /runs/data \\ [--val-csv /data/<val-basename>] [--val-frac 0.1] [--seed 0]"The output manifest has
vocab_source: "built_fresh"and includes asmiles_vocab(built from the corpus, needed for the new decoder).Upgrade the ckpt.
"$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> --run-dir $RUN_DIR -- \ "python /skill/scripts/upgrade_to_hybrid.py \\ --ckpt /ckpt \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs/upgraded.pt"Surface the JSON summary to the user — especially
warnings[], which includes any encoder-arch drift notes (e.g. legacy GROVER had two extraact_func_*keys that modern KERMTEmbedding doesn't) and the pretrain_ddp.py--backboneargparse-restriction note if the upgraded ckpt's backbone is anything other thangtrans.Estimate runtime + confirm with the user. Same heuristic as
kermt-continue-pretrain(corpus size × epochs × GPU count → wall time).Launch the runner detached.
"$SKILL_DIR/scripts/kermt_container.sh" run_detached \\ --name kermt-add-cmim-pretrain-<ts> \\ --run-dir $RUN_DIR -- \\ "python /skill/scripts/run_pretrain_local.py \\ --ckpt /runs/upgraded.pt \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs \\ [--epochs N --batch-size N ...]"The runner sees the upgraded ckpt as
model_type: hybrid, so it auto-dispatches--pretrain_mode hybrid --vocab_loss_weight 1.0with smiles_vocab plumbed through.Report to the user with the upgraded ckpt path + the same run.json pointer / log path / tensorboard URL pattern as
kermt-continue-pretrain.
Hard rules
Never modify the user's input ckpt. The upgrade writes a new file at
<run_dir>/upgraded.pt; the source ckpt stays untouched.Vocab heads are always fresh. Even if the input grover_base has vocab heads, they're discarded and rebuilt sized to the new corpus's vocab. Continue-pretraining the upgraded ckpt will train those new heads alongside the decoder.
Don't auto-relax
--backbonechoices. If the upgrade warning fires because the input ckpt's backbone isn'tgtrans(e.g. legacydualtrans), surface the warning and ask the user. Do NOT silently modify parsing.py to add the legacy backbone to the choices list.
Common errors
check_checkpoint rejected the ckptwith model_type=hybrid or cmim → user's ckpt already has a contrast head. Redirect tokermt-continue-pretrain.check_checkpoint rejected the ckptwith task_ffn=true → the ckpt has been finetuned. The upgrade workflow only supports pretrain checkpoints.prepare manifest missing smiles_vocab→ prepare_data was invoked with--skip-vocabor some equivalent that omitted the smiles vocab. Re-run prepare without those flags.unexpected key(s) in encoder loadwarning → legacy GROVER architectures saved a couple ofact_func_*weights that modern KERMTEmbedding doesn't use. Benign; the rest of the encoder loaded correctly.
What's in run.json after a successful run
Same reproducibility fields as kermt-continue-pretrain, plus the upgrade step's
summary.json is captured under the inputs.upgrade_summary path so the
provenance of the upgraded ckpt is auditable.
Replayability
Same as kermt-continue-pretrain: cmd_replay rebuilds the
run_pretrain_local.py --ckpt <upgraded.pt> ... invocation. To redo the
full add-cmim flow end-to-end, the user also needs the input grover_base
ckpt and the corpus — both are captured in the prepare_data and upgrade
manifests by absolute path.
Files
- ATTRIBUTION.mdAdded by AutoGineer853 B
- BENCHMARK.md7.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md8.4 KiB
- config/defaults_pretrain.json2.6 KiB
- evals/evals.json5.6 KiB
- scripts/_utils.pyScript14.1 KiB
- scripts/check_checkpoint.pyScript20.1 KiB
- scripts/check_data.pyScript11.9 KiB
- scripts/kermt_container.shScript18.9 KiB
- scripts/prepare_data.pyScript36.5 KiB
- scripts/run_pretrain_local.pyScript34.8 KiB
- scripts/upgrade_to_hybrid.pyScript18.2 KiB
- skill-card.md4.4 KiB
- skill.oms.sig6.3 KiB
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Requirements and compatibility
Written for: Claude Code, OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
The author says: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
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Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/bionemo-kermt-add-cmim-pretrain · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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