Datasets:
banana-harness integration
The companion banana-harness adapter uses benchmark name klein-code-bench, creates an empty native workspace, and keeps evaluator scenarios in Task.grading, outside Task.public() and the model prompt. It grades the complete generated project using this package's evaluator. It requires Python 3.11+ and POSIX. No Docker/rootfs backend is used.
From a banana-harness checkout with the Klein adapter:
# Both checkouts are local; the benchmark evaluator has no runtime dependencies.
python -m pip install -e /path/to/klein-code-bench
python -m pip install -e '/path/to/banana-harness[models,datasets]'
# Endpoint options use the harness's --model-options JSON file.
printf '%s\n' '{"api_base":"http://127.0.0.1:8000/v1","api_key":"local","temperature":0}' > local-model.json
banana run klein-code-bench --allow-downloads --dataset-config easy \
--backend native --model openai/your-local-model \
--model-options local-model.json --output jobs/klein-easy
Use the CLI's --help to confirm endpoint/model options for the installed harness. The source-loading path also accepts a single subset JSONL file from klein_code_bench/data/ through --source, avoiding dataset downloads. Use --dataset-config intermediate, hard, max, or default for the other subsets or all tasks. Pin --revision to the HF commit for remote loading. Record generation settings and use one attempt per task.
Generation uses the harness's existing agent and local-model providers. Grading imports klein_code_bench.runner lazily; installing or importing banana-harness alone does not download the benchmark. A missing evaluator gives an actionable install error. The optional klein dependency installs a wheel pinned to a benchmark dataset commit.