arch_panel_findings.json

{
  "n_models": 4,
  "n_with_macro": 4,
  "correlations": {
    "total_params": {"r": -0.13171951916328523, "n": 4},
    "n_layers": {"r": -0.5375124456001662, "n": 4},
    "d_model": {"r": 0.5671372674429019, "n": 4},
    "n_heads": {"r": 0.5671372674429019, "n": 4},
    "ffn_dim": {"r": 0.9208998623145593, "n": 3},
    "vocab_size": {"r": 0.3646043405335206, "n": 4},
    "max_ctx": {"r": 0.1538621267359702, "n": 4}
  },
  "correlations_ranked": [
    ["ffn_dim", 0.9208998623145593, 3],
    ["d_model", 0.5671372674429019, 4],
    ["n_heads", 0.5671372674429019, 4],
    ["n_layers", -0.5375124456001662, 4],
    ["vocab_size", 0.3646043405335206, 4],
    ["max_ctx", 0.1538621267359702, 4],
    ["total_params", -0.13171951916328523, 4]
  ],
  "best_macro_model": {
    "repo_id": "exnivo/tinybrain-100m-base",
    "macro": 0.5120183232855188,
    "arch": {"total_params": 103385856, "n_layers": 12, "d_model": 768, "n_heads": 12, "ffn_dim": 2048, "vocab_size": 24000, "max_ctx": 2048}
  },
  "models": [
    {"repo_id": "exnivo/tinybrain-100m-base", "macro": 0.5120183232855188, "total_params": 103385856, "model_type": "llama", "n_layers": 12, "d_model": 768},
    {"repo_id": "aksern/nexi-g1", "macro": 0.4822002572056585, "total_params": 30339456, "model_type": "gpt2", "n_layers": 6, "d_model": 384},
    {"repo_id": "oddadmix/Emhotob-25M-Egyptian-English-v2", "macro": 0.39475337003755284, "total_params": 25271424, "model_type": "llama", "n_layers": 8, "d_model": 384},
    {"repo_id": "textilelabs/Loom-Crucible-Preview", "macro": 0.391990733057559, "total_params": 154980864, "model_type": "llama", "n_layers": 52, "d_model": 512}
  ],
  "caveats": [
    "n is very small (4 models); correlations are illustrative, not statistical.",
    "All scores are zero-shot loglikelihood on a single harness (lm-eval 0.4.13, float32, bs=8, cuda:0).",
    "BLiMP is the mean acc over its subtasks; ARC-Easy/PIQA are acc; HellaSwag is acc_norm.",
    "Models span different training corpora and token counts, so arch and data effects are confounded.",
    "A tiny model trained on a narrow domain can score well on one task and poorly on another; macro hides that."
  ]
}