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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svdvals\nfrom threadpoolctl import threadpool_limits\n\nnp.set_printoptions(precision=4, suppress=True)\nthreadpool_limits(limits=2)\n\ndef spectrum(a):\n    s = svdvals(np.asarray(a, dtype=np.float64), check_finite=False)\n    e = s*s\n    return s, e.sum()/max(e[0], 1e-30), np.searchsorted(np.cumsum(e), .9*e.sum())+1\n\nprint(\"WEIGHT KEYS:\", sorted(W))\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}\"]\n        print(name, \"min/mean/max/std:\", np.min(a), np.mean(a), np.max(a), np.std(a),\n              \"norm:\", np.linalg.norm(a))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = W[f\"{name}.{l}\"]\n        s, sr, r90 = spectrum(a)\n        print(name, \"rms %.5f topSV %s stableRank %.2f r90 %d\" %\n              (np.sqrt(np.mean(a*a)), np.array2string(s[:5], precision=3), sr, r90))\n    for j in range(6):\n        sl = slice(64*j, 64*(j+1))\n        q = W[f\"W_q.{l}\"][sl].astype(float)\n        k = W[f\"W_k.{l}\"][sl].astype(float)\n        v = W[f\"W_v.{l}\"][sl].astype(float)\n        o = W[f\"W_o.{l}\"][:, sl].astype(float)\n        a = q.T @ k / 8\n        b = o @ v\n        sa, ra, r90a = spectrum(a)\n        sb, rb, r90b = spectrum(b)\n        asym = np.sum(a*a.T)/max(np.sum(a*a), 1e-30)\n        bsym = np.sum(b*b.T)/max(np.sum(b*b), 1e-30)\n        print(\"L%dH%d QK: norm %.3f top %s sr %.2f r90 %d symmetry %.3f; \"\n              \"OV: norm %.3f top %s sr %.2f r90 %d symmetry %.3f trace %.3f\" %\n              (l, j, np.linalg.norm(a), np.array2string(sa[:5], precision=3),\n               ra, r90a, asym, np.linalg.norm(b), np.array2string(sb[:5], precision=3),\n               rb, r90b, bsym, np.trace(b)))\n```\n"}}
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