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 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