feat(hle): add hle_250 — fixed 250-question subset, 10 epochs, OpenRouter judge default - #6
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Two changes to the DeepInfra provider, both needed to benchmark models on DeepInfra's own infrastructure. **service_tier via DEEPINFRA_SERVICE_TIER.** DeepInfra accepts an optional service_tier on chat completions; "flex" rides spare capacity, which is what lets a benchmark harness run without competing with production traffic. Read from the environment so a harness can set it per-run without touching eval code, and an explicit extra_body setting still wins. **An explicit per-request timeout.** The OpenAI SDK defaults to read=600s and nothing upstream raises it: openai_compatible.py builds AsyncOpenAI without a timeout, and inspect spends GenerateConfig.timeout on tenacity's stop_after_delay, which bounds retrying and never reaches the socket. So a generation legitimately running past ten minutes is cut off client-side. That was measured, not theoretical: benchmarking a 9B model on mbpp (which leaves max_tokens unset), 19-24% of requests died at exactly 599-602s while the API had admitted every one of them in about a second and refused none. Successful requests in the same window tailed to 479s, so the cut lands inside the live part of the latency distribution -- and it removes the *slowest* samples, biasing the score rather than merely shrinking it. A later attempt to set 1200s instead still produced deaths at 1199s, because the server's own budget can exceed that before generation even starts. An hour is a backstop rather than a target; a run that needs it has a problem worth seeing. Deliberately not taken from config.timeout, which inspect already spends as the retry budget -- reusing one number for both lets a single attempt consume the budget and leaves nothing to retry, which is the original failure. The value is an httpx.Timeout rather than a bare float so only the wait for tokens gets the long budget: connect stays at 30s, or an unreachable endpoint would hang for the full hour instead of failing fast. Also pins inspect-ai to 0.3.142; 0.3.141 was never published to PyPI, so the declared dependency does not install. Tests cover the timeout default, the short connect phase, independence from config.timeout, an explicit override, and all three service-tier paths.
# Conflicts: # src/openbench/model/_providers/deepinfra.py # tests/test_deepinfra_provider.py
…uter judge default (#3) * feat(hle): add hle_250, a fixed 250-question subset with epochs and a judge default Mirrors the shape of Artificial Analysis's Endpoint Accuracy Index HLE-250 (their exact subset is private): 250 text-only questions stratified proportionally over the 8 HLE categories (seed 20260807, largest-remainder rounding), committed as data in hle_250_ids.py so the subset can never drift. Runs 10 epochs averaged at temperature=1.0/top_p=0.95, per the reasoning-eval sampling convention, and defaults the grader to GPT-5.6 Luna (medium) via OpenRouter (OPENROUTER_API_KEY). get_dataset grows ids= and name= filters that refuse silently-missing ids; hle_scorer grows a reasoning_effort argument for graders that support one. Verified against the gated cais/hle dataset: 250 unique text-only samples, epochs=10, and one real judge call through inspect's openrouter provider. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * chore: update benchmark docs [skip ci] --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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Ships hle_250 (merged into
deepinfra-providervia #3) tomain. PR #3 landed ondeepinfra-providera few hours after #5 had already merged that branch intomain, so the feature never reachedmain— this PR closes that gap. The diff is exactly the hle_250 delta: 6 files, +364/−8.What
hle_250task: fixed 250-question text-only HLE subset,Epochs(10, "mean"),temperature=1.0/top_p=0.95, grader defaults toopenrouter/openai/gpt-5.6-lunawithreasoning_effort=medium(needsOPENROUTER_API_KEY).hle_250_ids.py: the subset as committed data — proportional stratification over the 8 HLE categories (2,158 text-only questions, May 2025 revision), seed 20260807, largest-remainder rounding. Committed as ids, not a seed, so dataset reordering can never move the subset.get_dataset(ids=, name=): subset filter that raises on missing ids rather than silently shrinking.hle_scorer(reasoning_effort=): grader effort pass-through.Why not AA's actual HLE-250
Artificial Analysis's subset is private and difficulty-overweighted; this is an equivalent-shape instrument (same size, repeats, sampling convention, judge family), not a replica. Scores are not comparable to AA's published numbers — that is documented on the task.
See #3 for the original review context and validation notes.
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