Agent skill · nvidia
codonfm-finetune
Fine-tune public CodonFM Encodon checkpoints on labeled coding-sequence or coding-variant data using LoRA, head-only, or full fine-tuning. Use when a user explicitly asks to fine-tune CodonFM or Encodon for regression or classification. Support generic public-v1 Encodon workflows only; reject Decodon, MissenseDataset, missense_synom_agg, and generation workflows.
What it needs
About 5k tokens when loaded.
What this skill does
Fine-tune public Encodon Use --pretrainedckptpath for public v1. Do not substitute --checkpointpath: the public runner does not forward that argument to the fine-tuning task. Instructions Resolve the target label, dataset, checkpoint, and output directory from the request and available files. Reuse existing data and weights. For training, check the project's ML dependencies and a compatible NVIDIA GPU before launch. If a required resource is unavailable, complete the available data preparation and return the command with the missing prerequisite clearly identified. When training is requested and the prerequisites are met, execute it and check the resulting checkpoints and metrics. A request for preparation ends with the validated inputs and command. Use the user's labeled dataset when provided. For a demonstration of sequence regression without a dataset, use the public human RiboNN translation-efficiency data below and state that choice. This is not a substitute for a user's intended assay or for labeled coding variants. If variant labels are missing, return the required schema and a command template promptly; do not search for labels or invent measured effects. Default demonstration checkpoint: nvidia/NV-CodonFM-Encodon-80M-v1, revision 399ca9fe17b57941a7bebc6788033919b417413c, file NV-CodonFM-Encodon-80M-v1.safetensors with sibling config.json. The public weights are about 307 MB. Download them when needed for the requested work; input preparation can record an intended checkpoint path. These are the original Encodon weights; the -TE- checkpoints use the separate TransformerEngine implementation. For an unsupported Decodon or missense-aggregation request, inspect the public parser/model configuration, explain the missing feature, and finish. Do not implement the missing model or search private repositories. Examples Prepare a small public-data example with the standard-library helper prepareribonn.py, running from the repository root. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-codonfm-finetune