Add HF Jobs how-to for benchmarking a model

Adds a short README section and bench/score_with_jobs.{sh,py}: one command scores any /v1/systemone model on the test split with HF Jobs (accuracy, KL, Brier, ECE). See discussion #37 for the full 19-model run.

README.md

@@ -143,6 +143,27 @@ against it by setting `TYPESAFE_BASE_URL`.
 To add a model, score it on `test` with the full distributions and open a
 discussion with the numbers and the mode (specialist or general) it used.
 
+### Benchmarking a model with HF Jobs
+
+One command scores any model that serves vLLM's `/v1/systemone` (any model
+without a native server works this way; serve it with a current nightly). It
+runs one request per case (state + all five questions) over the `test` split
+and prints accuracy, KL from gold, Brier and ECE:
+
+```bash
+hf jobs run --flavor a100-large --timeout 2h \\
+  --env MODEL_ID=your-org/your-model \\
+  --volume hf://datasets/LocalLLaMA/typed-decisions:/repo:ro \\
+  pytorch/pytorch:2.9.1-cuda12.8-cudnn9-devel \\
+  bash /repo/bench/score_with_jobs.sh
+```
+
+- Pick the `--flavor` for the model's size: `l4x1` up to ~2B, `l40sx1` or
+  `a100-large` for ~27B, `h200` above. Gated models also need `--secrets HF_TOKEN`.
+- `bench/score_with_jobs.sh` installs vLLM nightly, serves the model, scores all
+  400 cases, and writes `results.json`.
+- Then open a discussion with the numbers and the mode (specialist or general).
+
 ### Notes on the rows
 
 - **Liquid AI d1** was measured on 2026-09-30 through Liquid's API