Lumbridge Compute — Apache-2.0

docs/evals.md

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+# Lumbridge Compute evaluations (`lumbridge/v1`)
+
+Lumbridge Compute evaluates the model configuration that is actually serving: weights,
+quantization, context, runtime, parsers, and speculative decoder. A model name
+without its serving configuration is not a reproducible benchmark target.
+
+```bash
+lumbridge-compute eval ls
+lumbridge-compute eval run smoke
+lumbridge-compute eval run performance --model brain --repeat 5
+lumbridge-compute eval run finance-core --base-url http://your-node:8001/v1
+```
+
+Suites live in `evals/*.eval.yaml`. Each case has a stable id, prompt, category,
+generation limit, and deterministic assertions. Runs produce append-only JSON in
+`eval-results/` with raw outputs and per-sample metrics.
+
+## Metrics
+
+- **TTFT**: wall time until the first streamed content or reasoning token.
+- **Prefill tok/s (approximate)**: API-reported prompt tokens divided by TTFT.
+  This is client-observed and includes queueing/scheduling; server-native prefill
+  metrics should be added as a separate source rather than conflated with it.
+- **Decode tok/s**: completion tokens divided by time after the first token.
+- **Score**: share of samples satisfying every declared assertion.
+
+Performance runs should include warmups in automation and record hardware, Lumbridge Compute
+scene, runtime version, model revision, and cold/warm cache state. The v1 artifact
+is deliberately local and portable; a future registry can ingest the same JSON.
+
+## Eval-to-training bridge
+
+Capability suites should graduate into environment packages containing a dataset,
+harness, and reward function. That common contract can be adapted to Prime
+Intellect `verifiers` for evaluation, synthetic-data generation, SFT, or RL with
+`prime-rl`. Lumbridge Compute owns scene scheduling, memory admission, checkpoints, and process
+lifecycle; the training framework owns optimization and distributed execution.