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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=4, suppress=True)\n\ndef summary(a):\n    a = np.asarray(a, dtype=np.float64)\n    return (f\"mean={a.mean():.5f} sd={a.std():.5f} \"\n            f\"min={a.min():.5f} max={a.max():.5f} norm={np.linalg.norm(a):.4f}\")\n\nprint(\"KEYS:\", sorted(W))\nfor l in range(4):\n    print(f\"\\nLAYER {l}\")\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        print(name, summary(W[f\"{name}.{l}\"]))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = W[f\"{name}.{l}\"].astype(np.float64)\n        s = np.linalg.svd(a, compute_uv=False)\n        er = (s @ s)**2 / np.sum(s**4)\n        print(name, f\"F={np.linalg.norm(a):.4f}\",\n              \"singular_top8=\", np.round(s[:8], 4),\n              f\"smedian={np.median(s):.4f} effective_rank={er:.2f}\")\n\nprint(\"\\nHEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)\")\nprint(\"tr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.\")\nfor l in range(4):\n    d = W[f\"ln1_w.{l}\"].astype(np.float64)\n    for j in range(6):\n        sl = slice(64*j, 64*(j+1))\n        q = W[f\"W_q.{l}\"][sl].astype(np.float64) * d\n        k = W[f\"W_k.{l}\"][sl].astype(np.float64) * d\n        v = W[f\"W_v.{l}\"][sl].astype(np.float64) * d\n        o = W[f\"W_o.{l}\"][:, sl].astype(np.float64)\n        a = q.T @ k / 8\n        b = o @ v\n        sa = np.linalg.svd(a, compute_uv=False)[:64]\n        sb = np.linalg.svd(b, compute_uv=False)[:64]\n        ea = np.linalg.eigvalsh((a+a.T)/2)\n        eb = np.linalg.eigvals(v @ o)\n        leadb = eb[np.argsort(np.abs(eb))[-4:][::-1]]\n        print(f\"L{l}H{j}\",\n              f\"QK_F={np.linalg.norm(a):.3f} tr/F={np.trace(a)/np.linalg.norm(a):.3f}\",\n              f\"sym={np.linalg.norm((a+a.T)/2)/np.linalg.norm(a):.3f}\",\n              f\"QK_ER={(sa@sa)**2/np.sum(sa**4):.2f}\",\n              \"QK_s1-4=\", np.round(sa[:4],3),\n              \"QK_sym_extremes=\", np.round(ea[[0,1,-2,-1]],3),\n              f\"OV_F={np.linalg.norm(b):.3f} tr/F={np.trace(b)/np.linalg.norm(b):.3f}\",\n              f\"OV_ER={(sb@sb)**2/np.sum(sb**4):.2f}\",\n              \"OV_eig=\", np.round(leadb,3))\n```\n"}}
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