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{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(4)\n\ndef norm(x):\n    return float(la.norm(x))\n\nprint(\"All calculations below are weight-only linear algebra; no network evaluations.\")\nfor l in range(4):\n    print(\"\\nLAYER\", l)\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        a = W[f\"{name}.{l}\"].astype(np.float64)\n        print(name, \"min/mean/max/std/norm\",\n              np.round([a.min(), a.mean(), a.max(), a.std(), norm(a)], 5))\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 = la.svdvals(a)\n        print(name, \"Frob\", round(norm(a), 4),\n              \"top8_sv\", np.round(s[:8], 4),\n              \"stable_rank\", round(np.sum(s*s)/(s[0]*s[0]), 3))\n    print(\"HEAD: QK_frob QK_top4_sv QK_top1_energy QK_sym_fraction QK_trace; \"\n          \"OV_frob OV_top4_sv OV_trace OV_eig_posmax OV_eig_negmin\")\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)\n        k = W[f\"W_k.{l}\"][sl].astype(np.float64)\n        v = W[f\"W_v.{l}\"][sl].astype(np.float64)\n        o = W[f\"W_o.{l}\"][:, sl].astype(np.float64)\n        a = q.T @ k\n        b = o @ v\n        sa, sb = la.svdvals(a)[:64], la.svdvals(b)[:64]\n        eb = la.eigvals(v @ o)\n        print(f\"L{l}H{j}\",\n              round(norm(a), 4), np.round(sa[:4], 4),\n              round(sa[0]**2/np.sum(sa**2), 4),\n              round(norm((a+a.T)/2)/norm(a), 4), round(np.trace(a), 4),\n              \";\", round(norm(b), 4), np.round(sb[:4], 4),\n              round(np.trace(b), 4), round(eb.real.max(), 4),\n              round(eb.real.min(), 4))\n```\n"}}
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