--- language: - en license: mit pretty_name: Klein-Code-Bench task_categories: - text-generation tags: - code - benchmark - project-generation - small-language-models - local-models - python size_categories: - n<1K configs: - config_name: default default: true data_files: - split: test path: klein_code_bench/data/*.jsonl - config_name: easy data_files: - split: test path: klein_code_bench/data/easy.jsonl - config_name: intermediate data_files: - split: test path: klein_code_bench/data/intermediate.jsonl - config_name: hard data_files: - split: test path: klein_code_bench/data/hard.jsonl - config_name: max data_files: - split: test path: klein_code_bench/data/max.jsonl ---

KleinML

# Klein-Code-Bench Can a model build a working program from scratch? Klein-Code-Bench gives a coding model a specification and an empty directory, and checks whether the finished Python project passes every acceptance test. - **260 tasks** in four tiers of 65 - **Pass or fail per project:** a task counts only if all of its tests pass - **Runs locally:** standard-library Python, no Docker, no cloud model, no extra dependencies | Tier | Intended model size | What the model builds | Tasks | | --- | --- | --- | ---: | | easy | 2–8B | A JSONL command-line tool with a domain algorithm and error recovery | 65 | | intermediate | 8–16B | A stateful document service: CRUD, listing, domain evaluation | 65 | | hard | 16–32B | A service with atomic batches, revision checks and nested transactions | 65 | | max | 60–200B | A durable service that survives being force-killed, with idempotency, history and pagination | 65 | The model sizes are design targets. Version 0.1.0 has not been calibrated against real models yet, and there is no leaderboard. Any model can be run on any tier. ## How a task works 1. The model gets `PROMPT.md`: the full specification plus two examples. 2. It writes `main.py` and any other files into an empty `project/` directory. There's no starter code and no existing repository to fix. 3. The evaluator runs the project as a subprocess, sends JSONL requests and checks every response and state change. In the max tier, the process is force-killed right after its last acknowledged response, restarted on the same state directory, and must continue correctly. The 65 tasks per tier come from the same 65 domain families: text processing, CSV/JSON tools, calendars, numerical utilities, graphs, scheduling, ledgers, geometry, parsing, simulation and more. Each tier adds service behavior on top of the one below. Full list: [task catalog](docs/CATALOG.md). Because the tiers share families, results across tiers are correlated. Report each tier separately, and don't train on one tier and then present another as an uncontaminated evaluation. ## Quick start Requires Python 3.11+ on Linux or macOS. ```bash git clone https://huggingface.co/datasets/klein-ml/klein-code-bench cd klein-code-bench python -m pip install -e . klein-bench list --subset easy # Create a workspace: PROMPT.md, plus an empty project/ directory for the model klein-bench prepare kcb-easy-001-word-frequency --output workspaces/first # Give workspaces/first/PROMPT.md to your model and let it write into workspaces/first/project/, then: klein-bench evaluate kcb-easy-001-word-frequency \ --solution workspaces/first/project --output results/first.json klein-bench score results/first.json ``` - `python -m klein_code_bench` works the same as `klein-bench`. - Exit code 1 means a failed submission, 2 an invalid invocation. - `--public-only` runs just the two examples as a smoke test. Those results can't be scored. Bring your own agent or local API endpoint, or use the [banana-harness integration](docs/BANANA_HARNESS.md). Record the model's inference settings with your results. ## Loading the dataset ```python from datasets import load_dataset easy = load_dataset("klein-ml/klein-code-bench", "easy", split="test") # 65 tasks all_tasks = load_dataset("klein-ml/klein-code-bench", split="test") # 260 tasks ``` Each tier is its own config with a single `test` split; the default config contains all four. There is no training split, and no loading script or `trust_remote_code` is needed. Pin `revision` to a commit when you report results. | Field | Content | | --- | --- | | `task_id` | Stable unique ID | | `subset`, `family`, `category`, `title` | Tier and task description | | `target_model_parameters` | Intended model-size band (not calibrated) | | `language`, `entrypoint`, `protocol_version` | Python, `main.py`, protocol version | | `prompt` | The full specification the model sees | | `public_tests` | Example requests and responses (JSON string) | | `acceptance_tests` | Evaluator scenarios: sessions, requests, expected responses (JSON string) | | `num_scenarios`, `num_assertions`, `timeout_seconds` | Test counts and the per-session time limit | The test fields are stored as JSON strings so that all tasks share one schema; decode them with `json.loads`. **Never put `acceptance_tests`, the reference implementations or the evaluator source into the model's prompt, context or workspace.** They're public for reproducibility, so the benchmark depends on you keeping them away from the model. ## Scoring The metric is **resolve@1**: one project per task, and it counts only if every test passes. - **Per tier:** resolved tasks / 65. **Overall:** resolved tasks / 260. - Missing submissions count as failures. The report also shows the rate among submitted tasks and flags incomplete runs. - Partial assertion success is reported for diagnosis, but it never counts as resolved. Each scenario starts from a fresh copy of the submission and an empty state directory; sessions within a max-tier scenario share that directory. JSON comparison keeps array order and tells booleans apart from numbers. Floats use an absolute and relative tolerance of 1e-9; integers are compared exactly. Default limits are 10 seconds per session and 1 MiB of stdout. Details: [evaluation protocol](docs/EVALUATION.md). | Tier | Assertions | | --- | ---: | | easy | 3,075 | | intermediate | 5,415 | | hard | 9,705 | | max | 15,490 | | **total** | **33,685** | The tests cover: - 65 hand-written golden outputs - 24 generated inputs per family (seed 1729, shared across tiers) - boundary cases and malformed requests - failure atomicity, nested rollback and revision conflicts - retry and restart sequences All expected outputs are computed before grading, never inside the candidate's process. ### Verifying the benchmark itself ```bash python -m unittest discover -s tests -v # unit and property tests for the benchmark and runner klein-bench build # regenerate data and manifest from source klein-bench validate --output results/validation.json # every reference must pass, every empty project must fail ``` The reference implementations exist to maintain the evaluator. Export one for auditing with `klein-bench reference TASK_ID --output DIR`. The published `validation-summary.json` documents the dataset manifest and validation totals; it is not a model score. ## Security The runner is **not a sandbox**. It does the following: - removes inherited credentials from the environment - runs each submission from a temporary copy - rejects symlinks - limits time and output - kills the whole process group when it's done Generated code can still reach your filesystem and network. Run it under a separate unprivileged user, on a machine you can afford to lose. Timing depends on the host, so record your hardware with your results. ## Provenance and limitations - **Origin:** every task was written for this benchmark. None were copied from SWE-bench, LiveCodeBench or other benchmarks. That covers how the tasks were authored; it doesn't guarantee that standard algorithms in the easy tier never appear in a model's training data. - **Scope:** Python only. The tests measure behavioral correctness, not code quality, architecture or UI. They don't cover concurrent writers, disk corruption or power loss during a write. - **Coverage:** the reference implementations and independent golden checks make the dataset internally consistent, but the tests can't prove that no specification or expected output contains an error. - **License:** MIT for prompts, test data and reference implementations. ## Contributing A new task needs: - a contract (the specification) - meaningful boundary tests - an independent check of the expected outputs - a reference implementation that passes Changes to a contract or expected behavior require a new dataset version. Task IDs stay the same only for backward-compatible fixes.