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Prime (via albok-dev): understand why h2o failed and re-fit the judge. One vLLM on GPU 3 behind two fronts: :8034 with the shim's yes/no floor at 0.801 exactly as the cutover ran (same judge stamp), :8035 with SHIM_NOUL_FLOOR=0 and a distinct stamp (h2o-lightning-4b+noul-floor-0). Both refuse image URLs; the vLLM --allowed-media-domains backstop is now verified (400, no fetch).
33 lines
2.4 KiB
Markdown
33 lines
2.4 KiB
Markdown
# h2o-eval (TEMPORARY) — fv-ml1 GPU 3, :8034
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> **UP again since 2026-10-12 1428 PT, round 2** (Prime 1420, via albok-dev): "I'd like to understand why h2o failed, so
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> stand it up on gpu3 and refit it and let's get an understanding." albok-dev runs the investigation and the judge
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> re-fit (thread `01M4S026GWS354WZGXZ5E34F31`). **No load tests or other traffic while their runs are in.** Round 1
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> (1300–1319) was the pre-cutover validation. Take it down with `docker compose down` when albok-dev is done.
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>
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> | port | front | floor | judge stamp (`/health` model) |
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> |---|---|---|---|
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> | `:8034` | `h2o-eval-front` | ON, 0.801, as the live cutover ran | `h2o-lightning-4b@b43f759b…`, identical to the cutover's |
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> | `:8035` | `h2o-eval-front-nofloor` | OFF (`SHIM_NOUL_FLOOR=0`) | `h2o-lightning-4b+noul-floor-0@b43f759b…` |
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>
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> Both fronts sit over ONE vLLM (14,420 MiB, KV 135,070 tokens, util-sized because GPU 3 is otherwise empty). Each
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> `/health` also reports `backend.noul_floor {value, source}`.
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>
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> Checked at 1428:
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> - `/decide/shared` 200 on both, with the cutover's smoke values reproduced (adult 0.052, personal 0.876).
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> - An https image URL is refused on both: 422 "not accepted here".
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> - **The vLLM backstop answered for the first time.** An https image URL posted straight to vLLM got 400 "The URL must
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> be from one of the allowed domains: ['media.invalid']", with no fetch.
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h2o-lightning-4b @ b43f759b behind `services/h2o-decision-front` (copied into `conf/front/`), for albok-dev's
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re-validation of Nemi's sensitivity judge before the GPU 1 cutover (Prime 2026-10-12 via brokkr). **Remove right after
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the cutover:** GPU 3 is for transient work only (Scriberr and MIA use it).
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- Weights: `/tank/aimodels/h2o-lightning-4b-b43f759b` (copied from the bench kit; model.safetensors sha256
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`2d51561f221e1549d91bd1c38b3512a1435dd32950d454a9f040f1d4d11497b6`, tokenizer.json `5f9e4d49…`, shim git oid
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`fee7807e…`, serve_config `5e6fef1c…`: all equal to Hugging Face at b43f759b).
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- vLLM exactly as benched: `--max-model-len 40960 --max-num-seqs 16 --gpu-memory-utilization 0.1506`, vision limits as
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the vendor's serve.sh; healthy ~3 min after `up` (graph capture), ~14.4 GB on GPU 3.
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- `.env` on the host: `FRONT_TOKEN` = intern-decision's token (= vault `albok/judge-token`).
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- Remove: `cd /opt/docker/compose/h2o-eval && docker compose down` (the weights stay for the GPU 1 seat).
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