CondonFM Skills

.github/workflows/request-nvskills-ci.yml

@@ -0,0 +1,26 @@
+name: Request NVSkills CI
+
+on:
+  issue_comment:
+    types: [created]
+  pull_request:
+    types: [opened, reopened, synchronize, ready_for_review]
+  push:
+
+jobs:
+  request:
+    if: >
+      github.event_name == 'pull_request' ||
+      (github.event_name == 'issue_comment' &&
+        github.event.issue.pull_request &&
+        startsWith(github.event.comment.body, '/nvskills-ci')) ||
+      (github.event_name == 'push' &&
+        github.actor == (vars.NVSKILLS_SIGNATURE_PUSH_ACTOR || 'nv-skills-ci[bot]') &&
+        startsWith(github.event.head_commit.message, vars.NVSKILLS_SIGNATURE_COMMIT_TITLE || 'Attach NVSkills validation signatures'))
+    permissions:
+      contents: read
+      pull-requests: read
+      statuses: read
+    uses: NVIDIA/skills/.github/workflows/team-request.yml@main
+    secrets:
+      NVSKILLS_CI_DISPATCH_TOKEN: ${{ secrets.NVSKILLS_CI_DISPATCH_TOKEN }}

requirements.txt

@@ -1,3 +1,6 @@
+# xFormers CUDA wheels are published on the PyTorch index.
+--extra-index-url https://download.pytorch.org/whl/cu124
+
 # --------- pytorch --------- #
 torch==2.5.1
 torchvision==0.20.1
@@ -22,9 +25,10 @@ pre-commit==4.0.1      # hooks for applying linters on commit
 rich==13.9.4            # beautiful text formatting in terminal
 pytest==8.1.1  # tests
 sh==2.2.2  # for running bash commands in some tests (linux/macos only)
+python-dotenv==1.0.1
 transformers==4.54.1
 polars==1.12.0
-xformers==0.0.28.post3 --index-url https://download.pytorch.org/whl/cu124
+xformers==0.0.28.post3
 ninja==1.11.1.1
 einops==0.8.0
 ipython-autotime==0.3.2

skills/codonfm-embed/SKILL.md

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+---
+name: codonfm-embed
+description: Extract frozen CLS embeddings from public CodonFM Encodon checkpoints for coding-sequence property modeling. Use when a user explicitly asks for CodonFM or Encodon embeddings, or wants Encodon features for translation-efficiency, expression, or mRNA-stability modeling. Support Encodon embedding_prediction only; do not claim Decodon embedding support in public CodonFM v1.
+metadata:
+  author: "NVIDIA BioNeMo <bionemofeedback@nvidia.com>"
+---
+
+# Extract public Encodon embeddings
+
+Extract one frozen CLS vector per coding sequence. This workflow writes
+embeddings only; it does not automatically train a downstream regressor.
+
+## Instructions
+
+Resolve the sequence CSV, checkpoint, and output directory from the request and
+available files. Validate inputs before extraction. Execution requires the
+project's ML dependencies and a compatible NVIDIA GPU. If a required resource
+is unavailable, complete the available preparation and return the command with
+that prerequisite identified. When extraction is requested and resources are
+ready, execute and verify the embedding arrays. A request for preparation ends
+with the inputs and command. If no sequences were supplied, report the required
+inputs. For Decodon, inspect the [public parser](../../src/runner.py) and
+[model configuration](../../src/config.py), explain the missing implementation,
+and finish without attempting installation or model development.
+
+For a demonstration use `nvidia/NV-CodonFM-Encodon-80M-v1`, revision
+`399ca9fe17b57941a7bebc6788033919b417413c`, file
+`NV-CodonFM-Encodon-80M-v1.safetensors` with sibling `config.json`.
+Reuse an existing checkpoint or download it when needed for the requested work.
+Preserve a user's explicit checkpoint choice.
+
+## Preflight and inputs
+
+1. Confirm `src/runner.py`, `src/data/codon_bert_dataset.py`, and
+   `src/inference/encodon.py` exist.
+2. Accept only `encodon_80m`, `encodon_600m`, or `encodon_1b`.
+3. For execution require a `.ckpt`, or `.safetensors` with sibling `config.json`;
+   input preparation can use a planned path.
+4. Require CSV columns `id`, `ref_seq`, `value`, and `split`.
+
+`ref_seq` must be a coding sequence. For extraction-only data, set `value` to
+`0.0` and `split` to `test` on every row. Although the public dataset labels
+`split` optional, its evaluation path calls the test split and fails without
+that column. Normalize sequences to uppercase DNA (`A/C/G/T`) and require
+lengths divisible by three. Sequences longer than `--context_length - 2`
+codons are truncated rather than embedded in full.
+
+## Examples
+
+Set `CODONFM_DATA_PATH` to the sequence CSV, `CODONFM_CHECKPOINT_PATH` to the
+checkpoint, and `CODONFM_RUN_DIR` to your chosen output directory:
+
+```bash
+python -m src.runner eval \
+    --exp_name embed_extract \
+    --model_name encodon_80m \
+    --checkpoint_path "$CODONFM_CHECKPOINT_PATH" \
+    --data_path "$CODONFM_DATA_PATH" \
+    --process_item codon_sequence \
+    --dataset_name CodonBertDataset \
+    --task_type embedding_prediction \
+    --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"
+```
+
+For preparation requests, inspect the CSV directly against the input schema
+above and report the test-row count and sequence checks. Extra columns are
+allowed; extraction does not require a measured target. This does not require
+the ML runtime. The command above performs extraction when resources are ready.
+
+The existing `--dryrun` optionally builds runtime configuration and skips
+execution. It requires the ML dependencies, can create the prediction directory,
+and does not read the CSV or load weights. Do not use it as evidence that inputs,
+checkpoint compatibility, or embedding quality have been validated.
+
+## Outputs
+
+- `embeddings_merged.npy`: shape `(number_of_rows, hidden_size)`.
+- `ids_merged.npy`: IDs aligned with the embedding rows.
+
+Use the checked-in Encodon notebooks as downstream-model references:
+
+- `notebooks/4-EnCodon-Downstream-Task-riboNN.ipynb`
+- `notebooks/5-EnCodon-Downstream-Task-mRFP-expression.ipynb`
+- `notebooks/6-EnCodon-Downstream-Task-mRNA-stability.ipynb`
+
+Do not reference `notebooks/te_predictor.py`, `notebooks/mfe_predictor.py`, or
+Decodon notebooks because they are absent from public v1.
+
+## Boundaries
+
+- Do not use for Decodon; the public repository has no Decodon model or
+  inference class.
+- Do not claim a benchmark-trained regressor generalizes to a new organism,
+  cell type, or assay without new labeled validation data.
+- Do not invoke this skill for a generic expression-prediction request that
+  does not mention CodonFM or Encodon.

skills/codonfm-embed/agents/openai.yaml

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+interface:
+  display_name: "CodonFM Embeddings"
+  short_description: "Extract public Encodon sequence embeddings"
+  default_prompt: "Use $codonfm-embed to extract Encodon embeddings from my coding-sequence CSV."

skills/codonfm-embed/evals/evals.json

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+{
+  "skill_name": "codonfm-embed",
+  "evals": [
+    {
+      "id": "codonfm-embed-001",
+      "prompt": "Validate the supplied sequences.csv and prepare a public Encodon embedding-extraction command. Explain which rows will be processed and how to associate the output embeddings with sequence IDs. Use the supplied public source and checkpoint metadata.",
+      "files": [
+        "files/codonfm_source.zip",
+        "files/encodon_checkpoint.json",
+        "files/sequences.csv"
+      ],
+      "expected_output": "Two validated test rows and a public embedding_prediction command with the correct embedding/ID output contract.",
+      "assertions": [
+        "The command uses embedding_prediction, codon_sequence, and CodonBertDataset",
+        "The command specifies --checkpoint_path and output/prediction paths appropriate to the chosen working directory",
+        "The agent validates both sequence rows, including value and split=test, and explains that evaluation processes the test split",
+        "The response identifies embeddings_merged.npy and ids_merged.npy and explains their row alignment without fabricating embeddings"
+      ],
+      "expected_skill": "codonfm-embed",
+      "expected_script": null
+    },
+    {
+      "id": "codonfm-embed-002",
+      "prompt": "Does the public CodonFM implementation support extracting Decodon embeddings? Check the supplied source and explain the limitation, if any.",
+      "files": [
+        "files/codonfm_source.zip",
+        "files/encodon_checkpoint.json"
+      ],
+      "expected_output": "The agent identifies the absence of a public Decodon model and inference implementation.",
+      "assertions": [
+        "The agent explains that the supplied public source has no Decodon model/inference implementation and cites inspected files",
+        "The agent does not invent a Decodon command or attempt to implement the missing model"
+      ],
+      "expected_skill": "codonfm-embed",
+      "expected_script": null
+    }
+  ]
+}

skills/codonfm-embed/evals/files/encodon_checkpoint.json

@@ -0,0 +1,33 @@
+{
+  "repo_id": "nvidia/NV-CodonFM-Encodon-80M-v1",
+  "revision": "399ca9fe17b57941a7bebc6788033919b417413c",
+  "model_name": "encodon_80m",
+  "filename": "NV-CodonFM-Encodon-80M-v1.safetensors",
+  "size_bytes": 307351588,
+  "config_filename": "config.json",
+  "config": {
+    "vocab_size": 69,
+    "hidden_size": 1024,
+    "num_hidden_layers": 6,
+    "num_attention_heads": 8,
+    "intermediate_size": 4096,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "attention_probs_dropout_prob": 0.1,
+    "initializer_range": 0.02,
+    "layer_norm_eps": 1e-12,
+    "pad_token_id": 3,
+    "position_embedding_type": "rotary",
+    "classifier_dropout": 0.1,
+    "rotary_theta": 10000.0,
+    "ignore_index": -100,
+    "loss_type": "cross_entropy",
+    "lora": false,
+    "lora_alpha": 32.0,
+    "lora_r": 16,
+    "lora_dropout": 0.1,
+    "finetune_strategy": "full"
+  },
+  "source_url": "https://huggingface.co/nvidia/NV-CodonFM-Encodon-80M-v1/tree/399ca9fe17b57941a7bebc6788033919b417413c",
+  "weights_included": false
+}

skills/codonfm-embed/evals/files/sequences.csv

@@ -0,0 +1,3 @@
+id,ref_seq,value,split
+example_1,ATGGCTGAATTTCCGTAA,0.0,test
+example_2,ATGGCAGAATTTCCGTAA,0.0,test