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EvalHarness
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EvalHarness
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evalharness
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model
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sora
85dd193bcf
Execution-bench fixes verified on real model: BCB standalone-module + unittest semantics (100%), LCB base64+zlib+pickle private cases + line-normalized runner (100%), code_any def-start heuristic, general_fc should-call-tool semantics (80%), bfcl real-model 51 samples/17 categories (45.1%, multi_turn needs stateful env - known)
2026-08-25 10:48:20 +00:00
..
__init__.py
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
adapter.py
Drop logprob scoring path entirely; MCQ = strict-letter generation (evalscope parity 'ANSWER: X'), few-shot defaults stay; fix output.py duplication
2026-08-25 02:26:26 +00:00
deployer.py
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
output.py
Drop logprob scoring path entirely; MCQ = strict-letter generation (evalscope parity 'ANSWER: X'), few-shot defaults stay; fix output.py duplication
2026-08-25 02:26:26 +00:00
runner.py
Execution-bench fixes verified on real model: BCB standalone-module + unittest semantics (100%), LCB base64+zlib+pickle private cases + line-normalized runner (100%), code_any def-start heuristic, general_fc should-call-tool semantics (80%), bfcl real-model 51 samples/17 categories (45.1%, multi_turn needs stateful env - known)
2026-08-25 10:48:20 +00:00