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: | |
| ```bash | |
| # 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. | |