{
  "format": "jev-style-latency-v1",
  "model": "Jev-Style-2B-Decision-v3",
  "machine": {
    "chip": "Apple M1 Max",
    "memory_bytes": 68719476736,
    "macos": "15.7.5",
    "python": "3.12.13"
  },
  "shared_machine_note": "measured while other agents' jobs ran on the same Mac (among them a CPU-heavy PyTorch parity job using ~3.5 cores and ~16 GB); load averages at the end of each run are recorded per row. Treat the numbers as indicative, not as a clean benchmark.",
  "protocol": {
    "runtimes": "the staged runtimes of the three repos (jev_style_decision_gguf.py + jev-score-v2 built with build_jev_score.sh against llama.cpp 441df11f, Metal, all layers on the GPU; jev_style_decision_mlx.py, mlx 0.32.2 / mlx-lm 0.31.3; jev_style_decision.py on MPS, float32)",
    "weights": "trained release weights: release_2b/gguf/model-*.gguf (tensor data identical to the named repo files), release_2b/mlx/{bf16,affine8-g64}, candidate_2b/hf-candidate (torch)",
    "state": "plain text (repository documentation + source code, English) cut to target-150 tokens; the total input per question is recorded in input_tokens_per_question",
    "questions": "n_questions=1: one 4-option choice question (decide); n_questions=10: 10 mixed questions (4 choice, 4 true/false, 2 score) about the same state in one score_many call",
    "one_process_per_row": true,
    "cold_s": "first scoring call after loading (state + questions; includes GPU warm-up)",
    "warm_median_s": "median of 3 further calls, cached state dropped before each (state recomputed)",
    "state_cached_median_s": "median of 3 calls with the state already computed (only the question blocks run)",
    "load_s": "runtime construction (weights from the OS file cache in most rows)",
    "wall_time": "time.perf_counter around decide()/score_many(), incl. tokenisation and rendering",
    "peak_rss_bytes": "ru_maxrss of the Python process (and of the jev-score-v2 child for GGUF); includes memory-mapped weight pages",
    "peak_phys_footprint_bytes": "macOS lifetime-max physical footprint (proc_pid_rusage v4): memory the process owns, incl. Metal / MLX buffers it allocates. It does NOT count clean memory-mapped file pages, so for GGUF (jev-score-v2 maps the .gguf file) it excludes the weights and is not a memory requirement; use peak_rss_bytes.jev_score_v2 (which includes the mapped weight pages) as the upper bound for GGUF"
  },
  "reruns": {
    "gguf_and_mlx_rows": "all 18 GGUF rows and all 12 MLX rows were re-measured on 2026-09-27 03:11-03:28 (fix round, same script, same state text, same answers) because an independent re-run of the first session's GGUF Q8_0 / Q4_K_M 10-question rows was ~2.4x faster (contention during the first session). The first-session rows are kept in latency/runs_superseded_2026-09-27/ and are not used here. The 6 torch MPS rows are from the first session (not re-measured).",
    "superseded_dir": "latency/runs_superseded_2026-09-27/"
  },
  "complete": true,
  "missing_runs": [],
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