Machine-readable findings: correlations + per-model summary + caveats

arch_findings.json

@@ -0,0 +1,125 @@
+{
+  "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."
+  ]
+}