OpenJarvis

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Jon Saad-FalconandClaude Opus 4.6 9f5e97eed9 Add eval framework efficiency metrics: MFU, MBU, IPW, IPJ

Extend the eval framework to compute per-sample hardware efficiency
metrics when model hardware parameters are provided in TOML config:

- ModelConfig gains param_count_b, active_params_b, gpu_peak_tflops,
  gpu_peak_bandwidth_gb_s, num_gpus fields
- RunConfig gains metadata dict, populated by expand_suite() from
  model hardware params
- EvalRunner._process_one() computes IPW (Intelligence Per Watt),
  IPJ (Intelligence Per Joule), MFU, and MBU per sample
- All telemetry fields (energy, power, GPU util, throughput, MFU, MBU,
  IPW, IPJ) written to JSONL output and summary JSON
- RunSummary includes MetricStats (mean/median/min/max/std) for all
  telemetry metrics plus total_energy_joules
- GLM-4.7-Flash eval config enriched with A100 SXM hardware params
- 146 eval tests pass (26 new tests for telemetry, efficiency metrics,
  metadata flow, MetricStats helpers)
- User guide and API docs updated with new fields and output format

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-25 21:17:53 +00:00

agents.md

Fix mkdocs build: replace missing OpenClaw mkdocstrings refs with admonitions

2026-02-24 04:40:28 +00:00

bench.md

Add MkDocs Material documentation site with 40 pages and auto-generated API reference

2026-02-21 06:09:36 +00:00

channels.md

Fix mkdocs build: replace missing OpenClaw mkdocstrings refs with admonitions

2026-02-24 04:40:28 +00:00

core.md

Restructure codebase into 5-pillar architecture with MCP tool management, composition layer, and structured learning