EvalHarness

History

sora 820a1dceac Auto-stream on huge INPUT too (longbench_v2 ReadTimeout fix)

The auto-stream trigger only looked at max_tokens (output budget);
longbench_v2 sends ~128k-token INPUTS with a small 8k output budget and
hit the gateway's whole-request buffering: 20-minute read timeouts
through all 6 retries. es survives the same bench because it streams
by default. Payloads over ~300k chars (~90k+ tokens) now take the
stream-aggregate path; verified live: a 350k-char prompt returns in
20s instead of hanging.

Co-Authored-By: Claude <noreply@anthropic.com>

2026-09-15 02:07:18 +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

Auto-stream on huge INPUT too (longbench_v2 ReadTimeout fix)

2026-09-15 02:07:18 +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

gen_profiles.py

gen_profiles.yaml moved to evalharness/config/ (search path added); /data2/hf_home deleted (8.6G freed)