Individual skill
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.
skills/bionemo-codonfm-finetune/SKILL.md
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- engineering Derived category
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BENCHMARK.mddoc · 7,464 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesSKILL.mdcore · 9,666 bytesagents/openai.yamlasset · 233 bytesevals/evals.jsonasset · 4,522 bytesevals/files/codonfm_source.zipasset · 81,758 bytesevals/files/encodon_checkpoint.jsonasset · 1,025 bytesevals/files/ribonn_smoke.provenance.jsonasset · 606 bytesevals/files/ribonn_smoke.tsvasset · 31,525 bytesevals/files/variants_labeled.csvasset · 913 bytesevals/files/variants_labeled.provenance.jsonasset · 313 bytesscripts/prepare_ribonn.pyruns · 6,283 bytesskill-card.mddoc · 4,770 bytesskill.oms.sigasset · 6,557 bytes
Read excerpt
# Fine-tune public Encodon
Use `--pretrained_ckpt_path` for public v1. Do not substitute
`--checkpoint_path`: 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