Add Benchmark

Adding a benchmark

Don't skip step 1. If NS doesn't have the benchmark, this becomes a heavier add-metrics-type task instead, not this skill.

1. Verify upstream coverage

SHA=$(python -c "import yaml; print(yaml.safe_load(open('sgl_eval/_vendored/nemo_skills/SOURCES.yaml'))['synced_from_sha'])")
gh api "repos/NVIDIA/NeMo-Skills/contents/nemo_skills/dataset/<name>?ref=$SHA" \
  --jq '.[] | .name'
gh api "repos/NVIDIA/NeMo-Skills/contents/nemo_skills/dataset/<name>/__init__.py?ref=$SHA" \
  --jq '.content' | base64 -d

Confirm:

• The directory exists.
• METRICS_TYPE is one of math, multichoice (the wired-up types). Otherwise stop and ask the user; this skill cannot proceed.
• GENERATION_ARGS contains a ++prompt_config=... (the prompt yaml basename will be auto-derived).

2. Add source files to SOURCES.yaml

Always append the dataset __init__.py:

- src: nemo_skills/dataset/<name>/__init__.py
  dst: dataset/<name>/__init__.py

Then exactly one data-source entry, depending on what the upstream benchmark ships:

• If upstream has dataset/<name>/prepare.py (benchmark downloads from HuggingFace / a URL at prepare time):

  - src: nemo_skills/dataset/<name>/prepare.py
    dst: dataset/<name>/prepare.py
  

• Else if upstream has dataset/<name>/test.txt (questions bundled as jsonl-in-txt):

  - src: nemo_skills/dataset/<name>/test.txt
    dst: dataset/<name>/test.txt
    binary: true
  

• Else stop -- this skill only covers the two loaders sgl-eval wires.

If the prompt yaml referenced by GENERATION_ARGS is one we already vendor (math.yaml, mcq-4choices.yaml, mcq-4choices-boxed.yaml), no further yaml entry is needed. Otherwise, also add the prompt:

- src: nemo_skills/prompt/config/<path>.yaml
  dst: prompts/<basename>.yaml

3. Run sync

python scripts/sync_vendored.py

4. Register in _registry.py:_TABLE

Append a row. Only set fields that are non-default for this benchmark; metrics_type + prompt yaml basename are auto-derived from the upstream __init__.py:GENERATION_ARGS, so do not hardcode them.

Two minimal shapes -- pick the one matching step 2:

Bundled (vendored test.txt):

{
    "name": "<name>",
    "loader": "bundled",
    "thinking": True,                # True for reasoning, False for knowledge
    "default_n_repeats": 16,         # 1 single-shot, 8/16 stochastic
    "description": "<one-liner>",
},

Prepare (vendored prepare.py):

{
    "name": "<name>",
    "loader": "prepare",
    "save_args": ("test",),          # see invariant below
    "save_kwargs": {"random_seed": 42},  # only if upstream save_data takes non-default kwargs
    "thinking": False,
    "default_n_repeats": 1,
    "description": "<one-liner>",
},

save_args[0] invariant. _loader.py uses output_basename = f"{save_args[0]}.jsonl" to find the file save_data wrote. So save_args[0] must equal the basename (sans .jsonl) of upstream's output. e.g. gpqa.save_data("diamond") writes diamond.jsonl -> save_args=("diamond",). If upstream's signature differs, the loader can't be reused as-is.

5. Verify

pytest                                    # vendor audit + NS tests still pass
sgl-eval list -v                          # new benchmark appears with correct defaults

For a smoke test against a real server (only if user has one running):

sgl-eval run <name> --base-url http://localhost:30000/v1 --num-examples 3

6. Commit

One commit per benchmark. Title: add <name> benchmark.

The commit should contain:

• SOURCES.yaml change (manifest entries)
• new files under sgl_eval/_vendored/nemo_skills/dataset/<name>/
• the _TABLE row in _registry.py

Nothing else. If the sync also produced unrelated changes (e.g., the upstream commit has been bumped meanwhile), separate that into its own vendor-update commit first.