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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\n\nnp.set_printoptions(precision=4, suppress=True, linewidth=160)\n\nwith threadpool_limits(limits=2):\n    print(\"AVAILABLE WEIGHTS\")\n    print([(k, tuple(v.shape)) for k, v in sorted(W.items())])\n    print(\"\\nLAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm\")\n    for l in range(4):\n        for name in (\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"):\n            a = np.asarray(W[f\"{name}.{l}\"], dtype=np.float64)\n            print(f\"{name}.{l}\", np.round([a.min(), np.median(a), a.max(), a.mean(), a.std(), la.norm(a)], 5))\n\n    print(\"\\nMATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values\")\n    for l in range(4):\n        for name in (\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"):\n            a = np.asarray(W[f\"{name}.{l}\"], dtype=np.float64)\n            s = la.svdvals(a)\n            print(f\"{name}.{l}: norm={la.norm(a):.4f} smax={s[0]:.4f} stable_rank={np.sum(s*s)/s[0]**2:.2f} top={np.round(s[:8],4)}\")\n\n    print(\"\\nHEAD WEIGHT PRODUCTS (no inputs or forward passes)\")\n    print(\"QK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).\")\n    for l in range(4):\n        for j in range(6):\n            sl = slice(64*j, 64*(j+1))\n            q = np.asarray(W[f\"W_q.{l}\"][sl], dtype=np.float64)\n            k = np.asarray(W[f\"W_k.{l}\"][sl], dtype=np.float64)\n            v = np.asarray(W[f\"W_v.{l}\"][sl], dtype=np.float64)\n            o = np.asarray(W[f\"W_o.{l}\"][:,sl], dtype=np.float64)\n            qk = q.T @ k / 8\n            ov = o @ v\n            sq = la.svdvals(qk)[:64]\n            so = la.svdvals(ov)[:64]\n            print(\n                f\"L{l}H{j}: Q,K,V,O norms={np.round([la.norm(q),la.norm(k),la.norm(v),la.norm(o)],3)} \"\n                f\"QK norm={la.norm(qk):.3f} top={np.round(sq[:6],3)} sr={np.sum(sq*sq)/sq[0]**2:.2f} \"\n                f\"symfrac={la.norm((qk+qk.T)/2)/la.norm(qk):.3f} \"\n                f\"trcos={np.trace(qk)/(np.sqrt(384)*la.norm(qk)):.4f}; \"\n                f\"OV norm={la.norm(ov):.3f} top={np.round(so[:4],3)} sr={np.sum(so*so)/so[0]**2:.2f} \"\n                f\"trcos={np.trace(ov)/(np.sqrt(384)*la.norm(ov)):.4f}\"\n            )\n```"}}
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