sora
ea93602dfa
Unified, nicer result tables + conda env setup in README
...
- cli: rich Run Summary table for multi-benchmark runs (green/red rows,
fallback to aligned plain text); unified _fmt_score (fractions render
as percentages everywhere -- was 1.0 in summary vs 100.0% in detail);
fix the stray "summary csv -> None/viz/..." print without --out-dir;
summary.md upgraded to a proper table with model/timestamp/ok-count
header -- one table for a whole N-benchmark run
- text renderer: single-bench headline deduped (dataset==recipe) and
compacted to one facts line; adaptive metric-name column (long names
no longer break alignment)
- md_compare: auto-switches to one-row-per-benchmark when comparing
different benchmarks with different metrics; same-bench model
comparison gains baseline delta markers (+/- percentage points)
- README: conda create/activate in the install block
Co-Authored-By: Claude <noreply@anthropic.com>
2026-09-10 06:30:22 +00:00
sora
27cf8b3c7e
Usability round: progress plugin, CLI provider flags, top-level run(), vendored BFCL checker
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- progress/: Rich per-sample terminal progress plugin (Run Plan panel,
in-flight/rate/ETA bar); shared console + log-through-live to avoid
interleaved writes, rollback() pairs begin_sample on the retry path,
begin moved inside the semaphore (in-flight = actually generating),
graceful degradation when rich is absent
- cli.py: --provider/--api-url/--model composition (openai-chat |
openai-pool), --disable-thinking/--perf/--textools as first-class
flags, per-bench phase lines and done/failed result lines
- __init__: top-level run()/arun() entries (event-loop safe for notebooks)
- third_party/bfcl: vendored official BFCL ast_checker + type mappings
(Apache-2.0, provenance in __init__.py); imports rerouted locally,
underscore_to_dot parameterized; verified bit-identical with the
bfcl-eval package on 100 real rows -- removes the heavy extra
(pinned numpy + cloud SDK wall) from the install path
- runner: progress/status hooks through generate+evaluate, checkpoint
key scheme fix (empty-store falsy bug), tiered retry backoff,
multi-segment pool {range} expansion fix, adapter-instance passthrough
- pyproject: tree_sitter family joins core deps; [bfcl] extra retired
- README: rewritten (zh) -- install/quickstart/flags reference/bench
table/reliability/extension/architecture/validation
Co-Authored-By: Claude <noreply@anthropic.com>
2026-09-10 05:46:45 +00:00
sora
46bef7d3dd
dp4-flash 28-bench alignment: renderer plugin layer, exec_workers, gen_profiles, AIMD pool, BCB/LCB/bfcl/gfc judge fixes
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Co-Authored-By: Claude <noreply@anthropic.com>
2026-09-08 05:55:04 +00:00
89e721414f
Simplify install: light deps (datasets/pyarrow/sympy/pylatexenc/numpy/scipy) are DEFAULT; single [bfcl] extra for the heavy official checker
2026-08-25 02:51:55 +00:00
b2e7133b20
Add sandbox layer (docker exec hard-isolation + serve envs, refcounted acquire/release, atexit teardown, bind-mount sharing) and agent evaluation driver (message pump drive(), Trajectory, bfcl_mock env with official call-sequence scoring, mock:fc oracle); Deployer delegates to sandbox.serve_env; code_any extractor fixes indentation-stripping; CLI --env
2026-08-24 07:11:03 +00:00
6b3bb330c7
Add model layer: async ModelAdapter (openai_compatible + mock) returning structured ModelOutput(text,tool_calls,usage), Deployer registry (vllm/sglang docker-pinned via models.yaml, external), async run_eval generate->score, CLI --model, agent-ready SampleResult.trajectory/env_state, tests
2026-08-24 06:28:00 +00:00
4a15f80897
Add evaluation layer + visualization: extract/score/aggregate plugins, 28 recipes, official-aligned scorers (PRM800K math, DROP Hungarian EM/F1, SimpleQA A/B/C judge), report artifacts, console renderers, CLI eval/viz, regression tests
2026-08-24 06:09:38 +00:00
f8cd15fea1
EvalHarness data layer: 28 dataset plugins, lazy materialize cache (raw/ + samples.jsonl + meta.json), ModelScope native loader, CLI list/fetch/unload/stats/show
2026-08-24 03:35:11 +00:00
