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