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codonfm-embed

NVIDIA4.0kstars

Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.

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1---2name: codonfm-embed3description: Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for…4---56# skills/bionemo-codonfm-embed/SKILL.md

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SKILL.md

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1Install this skill: https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-codonfm-embed

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skills/bionemo-codonfm-embed/SKILL.mdMarkdown · UTF-8

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File: skills/bionemo-codonfm-embed/SKILL.md
Publisher: NVIDIA
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SKILL.md

name: codonfm-embed
description: Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.
license: Apache-2.0
metadata: author: "NVIDIA BioNeMo <[email protected]>" tags: [biology, codonfm, embeddings]

Extract public Encodon embeddings

Purpose

Extract one frozen CLS vector per coding sequence with public Encodon v1. Support input validation, command preparation, extraction, and checkpoint selection for translation efficiency, expression, or mRNA stability modeling. Extraction does not automatically train a downstream regressor.

Prerequisites

Validation needs Python 3 standard library only; no GPU, weights, or API key.
Execution needs the public CodonFM checkout, its requirements.txt environment, a compatible NVIDIA GPU, and local checkpoint weights. A metadata JSON is not a checkpoint. A .safetensors file needs its sibling config.json; .ckpt checkpoints are also supported by the public loader.
Run python -m src.runner from the CodonFM repository root. In an isolated workspace, use supplied source artifacts; source paths below are relative to that checkout or source archive, not this skill directory.

Inputs

Input source precedence: explicit user prompt arguments, then supplied files/checkpoint metadata, then inspected public runner defaults. Resolve conflicting model names and checkpoint metadata before execution. Supplied 80M metadata is useful for preparing an 80M command; it does not restrict an open-ended recommendation to that size.

Required for validation: a CSV. Required for extraction: the CSV, checkpoint, matching model name, and output directory. Optional: context length and batch size overrides. Checkpoint-selection questions can be answered without a CSV.

Input	Requirement or default
Sequence CSV	Columns `id`, `ref_seq`, `value`, `split`; extra columns allowed
`id`	Nonblank, unique IDs for unambiguous output association
`ref_seq`	Coding sequence, uppercase DNA `A/C/G/T`, length divisible by three; public dataset converts uppercase `U` to `T`

Choose the requested workflow. For a checkpoint/performance question, read checkpoint selection and answer from public benchmark evidence. For the strongest published downstream results, prefer the public 1B random-mask checkpoint when resources allow; 80M is a demonstration or resource-constrained choice. A small labeled set alone does not establish that 80M frozen features are better. Do not download weights or inspect the entire source tree just to make a recommendation.
Inspect supplied source only where needed. Confirm runner/config, src/data/codon_bert_dataset.py, src/data/preprocess/codon_sequence.py, src/inference/encodon.py, or src/utils/pred_writer.py for the relevant behavior. Read ZIP members with zipfile.ZipFile.namelist() and .read(); source inspection does not need extraction. If a checkout is needed, use a new directory from tempfile.mkdtemp() or mktemp -d, without deleting or overwriting an existing directory. For Decodon support questions, inspect runner/config and model/inference modules, cite the inspected files, explain the missing public implementation, and finish there.
Validate the CSV before running extraction. Run the bundled checker below with the intended context length. Report per-row verdicts using CSV row numbers as well as IDs, since IDs can repeat. Separate excluded rows, invalid inputs, duplicate-ID warnings, and truncation. Propose fixes without silently rewriting supplied data. The checker is a preflight, not model execution or proof of biological CDS validity.
Deliver the requested preparation or execution. For preparation, return a complete command with resolved paths (or clearly identified prerequisites), the test-row count, validation findings, and the output contract below. Include all task/dataset/process flags in the final answer, even if already shown in a tool call. For extraction, reuse/download the chosen checkpoint when needed, execute once resources are ready, and verify the saved arrays. If resources are missing, finish preparation and state what is missing.

python "$CODONFM_SKILL_DIR/scripts/validate_inputs.py" "$CODONFM_DATA_PATH" \
    --context-length 2048

python -m src.runner eval \
    --task_type embedding_prediction \
    --process_item codon_sequence \
    --dataset_name CodonBertDataset \
    --exp_name embed_extract \
    --model_name "$CODONFM_MODEL_NAME" \
    --checkpoint_path "$CODONFM_CHECKPOINT_PATH" \
    --data_path "$CODONFM_DATA_PATH" \
    --context_length 2048 \
    --num_nodes 1 \
    --num_gpus 1 \
    --num_workers 0 \
    --val_batch_size 2 \
    --out_dir "$CODONFM_RUN_DIR" \
    --predictions_output_dir "$CODONFM_RUN_DIR/predictions"

Symptom	Cause and action
Missing `split` column	Eval requests the test split despite the dataset docstring calling this column optional; add an explicit split column to a corrected copy
Fewer output rows	Blank/non-`test` split values are silently filtered; set intended extraction rows to exact `test` in a corrected copy
Repeated output IDs	Duplicate input IDs are not rejected; assign unique IDs while preserving a mapping to the original rows
Oversized sequence	Preprocessing truncates at `context_length - 2` codons; report retained/lost lengths and agree on a sequence-handling strategy
Missing weights or dependencies	Complete validation/command preparation; metadata and `--dryrun` do not substitute for weights
Merge failure on a repeated run	The writer scans `.npy` files; use a fresh predictions directory to avoid stale shards or merged arrays

codonfm-embed

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SKILL.md

Extract public Encodon embeddings

Purpose

Prerequisites

Inputs

Instructions

Available Scripts

Output Format

Examples

Outputs

Limitations

Troubleshooting