diff --git "a/weights/final_model_metadata.json" "b/weights/final_model_metadata.json"
--- "a/weights/final_model_metadata.json"
+++ "b/weights/final_model_metadata.json"
@@ -4,308 +4,38 @@
     "state": {
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         "step": "tensor(12520.)",
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+        "exp_avg": "tensor([[ 1.5781e-04, -3.5266e-04, -9.7583e-05,  ...,  4.1940e-05,\n          5.6975e-05,  5.5798e-05],\n        [ 9.8742e-05, -1.3119e-04,  1.3677e-05,  ..., -1.9366e-05,\n          1.0349e-04,  5.0592e-05],\n        [-2.9130e-04,  1.9895e-04, -9.0260e-05,  ..., -9.9918e-05,\n         -1.8629e-04, -6.0961e-05],\n        ...,\n        [ 5.7664e-05,  4.7397e-05,  1.2529e-04,  ...,  2.8799e-05,\n         -5.4763e-05, -6.6750e-06],\n        [ 6.3787e-05, -2.1633e-05,  6.6595e-05,  ...,  2.7704e-05,\n          8.9652e-05, -4.2091e-06],\n        [ 1.5883e-04, -1.1030e-04,  6.1721e-05,  ...,  1.1175e-04,\n         -8.8536e-06,  8.7643e-05]], device='cuda:0')",
+        "exp_avg_sq": "tensor([[1.6698e-07, 2.8303e-07, 5.0525e-08,  ..., 4.0017e-08, 8.8370e-08,\n         5.3000e-08],\n        [2.5771e-07, 4.5601e-07, 5.9020e-08,  ..., 4.0411e-08, 1.4522e-07,\n         7.0853e-08],\n        [2.3775e-07, 4.7696e-07, 6.2997e-08,  ..., 6.3045e-08, 5.9060e-08,\n         4.6280e-08],\n        ...,\n        [1.4690e-07, 4.0507e-07, 4.7863e-08,  ..., 4.8895e-08, 4.3604e-08,\n         3.9252e-08],\n        [2.7245e-07, 2.1035e-07, 4.9765e-08,  ..., 6.8004e-08, 5.1936e-08,\n         4.4202e-08],\n        [1.1928e-07, 1.4219e-07, 3.6373e-08,  ..., 5.2237e-08, 4.5473e-08,\n         3.9891e-08]], device='cuda:0')"
       },
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       },
       "2": {
         "step": "tensor(12520.)",
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-        "exp_avg_sq": "tensor([[1.9624e-09, 2.2615e-10, 1.6196e-09,  ..., 6.6825e-09, 4.7686e-16,\n         6.7964e-10],\n        [2.0070e-09, 7.9523e-10, 4.2056e-10,  ..., 2.2196e-10, 2.6458e-14,\n         4.2702e-09],\n        [2.3410e-10, 3.6115e-10, 3.2898e-10,  ..., 6.0066e-10, 1.8233e-13,\n         1.4097e-09],\n        ...,\n        [4.7294e-11, 4.8290e-11, 5.5586e-10,  ..., 7.2295e-11, 1.8310e-14,\n         1.7603e-11],\n        [5.4413e-10, 2.2035e-11, 1.9991e-10,  ..., 3.0986e-10, 3.6935e-17,\n         4.8808e-10],\n        [3.3296e-09, 3.1063e-10, 6.0098e-09,  ..., 1.7746e-09, 3.2723e-15,\n         2.6791e-09]], device='cuda:0')"
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       },
       "3": {
         "step": "tensor(12520.)",
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1.5635e-07, 3.6905e-07, 2.6259e-07, 3.8687e-07,\n        4.7996e-07, 1.8078e-07, 2.1399e-07, 2.5266e-07, 1.4999e-07, 2.1441e-07,\n        1.4788e-07, 2.1741e-07, 9.1990e-08, 2.6614e-07, 3.7700e-07, 3.6182e-07,\n        3.6236e-07, 2.6526e-07, 3.3645e-07, 1.1933e-07, 2.2411e-07, 3.3755e-07,\n        2.1433e-07, 2.4179e-07, 3.1439e-07, 3.4441e-07, 1.0257e-07, 1.6522e-07,\n        1.9009e-07, 2.2387e-07, 2.0718e-07, 2.9718e-07, 4.3104e-07, 1.5074e-07,\n        3.9462e-07, 1.9716e-07, 4.2145e-07, 2.9546e-07, 2.5878e-07, 4.3282e-07,\n        2.6977e-07, 3.2924e-07, 3.1842e-07, 2.2467e-07, 2.6467e-07, 3.6254e-07,\n        3.0816e-07, 1.7705e-07, 3.1104e-07, 2.2346e-07, 2.1714e-07, 1.9056e-07,\n        1.3208e-07, 2.5582e-07, 2.1585e-07, 1.4291e-07, 4.6411e-07, 2.5803e-07,\n        1.5700e-07, 2.7100e-07, 1.7117e-07, 3.7821e-07, 3.3190e-07, 2.8111e-07,\n        1.8160e-07, 2.7962e-07, 1.7832e-07, 1.5373e-07, 2.8135e-07, 1.4392e-07,\n        1.2374e-07, 1.5946e-07, 3.9163e-07, 2.9864e-07, 1.5609e-07, 1.6107e-07,\n        3.6032e-07, 2.3995e-07, 1.0812e-07, 3.3769e-07, 2.4657e-07, 3.6158e-07,\n        3.8523e-07, 1.2528e-07, 2.2048e-07, 3.0105e-07, 4.2159e-07, 3.1306e-07,\n        1.5309e-07, 2.6877e-07, 1.0997e-07, 2.1401e-07, 2.8960e-07, 2.2950e-07,\n        2.6709e-07, 3.3779e-07, 4.1425e-07, 9.7348e-08, 1.9150e-07, 3.3333e-07,\n        1.7449e-07, 2.9185e-07, 4.2516e-07, 1.4202e-07, 2.8163e-07, 2.7556e-07,\n        2.6623e-07, 1.8766e-07, 2.1143e-07, 3.4654e-07, 1.7227e-07, 2.8115e-07,\n        4.5815e-07, 2.0989e-07, 1.9896e-07, 2.8303e-07, 3.1401e-07, 3.3674e-07,\n        9.0994e-08, 3.1324e-07, 3.4415e-07, 3.7619e-07, 3.6785e-07, 3.0748e-07,\n        2.2138e-07, 3.8593e-07, 2.5920e-07, 1.2004e-07, 2.4800e-07, 4.2623e-07,\n        1.6435e-07, 2.9217e-07, 4.2009e-07, 2.9591e-07, 2.2528e-07, 3.0166e-07,\n        2.8132e-07, 2.9005e-07, 1.2789e-07, 2.5149e-07, 1.3761e-07, 8.1217e-08,\n        1.5388e-07, 2.6906e-07], device='cuda:0')"
+        "exp_avg": "tensor([ 4.1373e-03,  2.0046e-03, -2.0093e-03,  6.0270e-04, -4.2223e-03,\n         1.4737e-03, -8.3424e-04,  9.9720e-04,  1.3730e-03,  2.3100e-03,\n        -1.5413e-03,  3.5469e-03, -9.4580e-04, -2.4875e-03, -1.5314e-03,\n         1.3928e-03, -5.4596e-03, -9.6337e-04,  2.7346e-03,  2.4711e-03,\n         2.5775e-03, -3.8221e-04,  2.5814e-03,  1.7790e-03, -1.9503e-03,\n         1.3163e-04,  1.7431e-04, -2.8731e-03, -2.3889e-04, -1.5291e-03,\n         4.2299e-04, -5.5862e-05,  5.5053e-04,  2.2228e-03, -1.0593e-03,\n         1.3684e-03, -1.3330e-03,  6.9733e-03, -8.4587e-04, -5.1940e-03,\n        -5.5081e-03, -1.6495e-04, -7.0945e-04, -1.2496e-03, -4.8574e-04,\n         2.2032e-03,  2.6144e-03,  1.0518e-03,  4.1722e-04,  1.1655e-03,\n         4.6709e-05,  4.1575e-04,  3.0638e-04,  3.0734e-04,  1.9658e-03,\n         3.8384e-03,  7.7699e-04,  1.5948e-03, -1.2091e-03,  1.6686e-03,\n         1.5183e-03, -4.7683e-04, -4.1525e-04,  3.2692e-03, -2.4660e-04,\n        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-1.9482e-03, -1.4571e-03,\n        -6.8159e-04,  7.9098e-04, -2.5212e-03, -1.1660e-03, -6.3629e-04,\n         2.1819e-03,  1.2585e-03,  3.1514e-04,  6.4672e-04,  4.3465e-03,\n         1.0540e-03, -3.3350e-03,  1.9752e-03, -4.5015e-04,  1.2544e-03,\n         3.8538e-03, -1.2945e-03,  2.2922e-03,  1.9641e-03, -1.4458e-03,\n         2.9125e-03, -5.2555e-03, -1.2550e-03, -4.5086e-04,  1.8257e-03,\n         1.6713e-03, -2.5974e-03, -1.5011e-03,  1.2974e-03, -5.5984e-04,\n         7.4047e-04, -4.3050e-03,  2.2720e-03, -1.1104e-04,  4.3742e-04,\n        -9.5901e-04,  7.1693e-04, -1.3201e-03,  4.0388e-04, -3.2207e-03,\n        -5.4032e-03,  9.7979e-04, -1.6721e-03, -1.9822e-03, -1.7101e-03,\n        -1.4230e-03,  1.6750e-03, -2.8188e-03, -1.2123e-03,  8.2985e-04,\n        -3.1523e-04, -9.4855e-04, -6.0235e-04, -2.6111e-03,  3.0811e-03,\n        -4.4801e-03, -9.1183e-04, -3.7965e-03, -7.7872e-04,  1.7078e-04,\n        -8.8201e-04,  1.0211e-03,  1.7733e-03, -2.4294e-03,  3.0546e-04,\n         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-5.6052e-45,  5.6052e-45,\n        -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n        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 5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         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 5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n        -5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,  5.6052e-45,\n        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n        -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45, -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n        -5.6052e-45,  5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45,\n        -5.6052e-45, -5.6052e-45,  5.6052e-45, -5.6052e-45, -5.6052e-45,\n         5.6052e-45,  5.6052e-45, -5.6052e-45], device='cuda:0')",
-        "exp_avg_sq": "tensor([1.5030e-16, 1.2033e-16, 3.6158e-16, 4.3927e-17, 1.7953e-16, 2.8655e-16,\n        8.1683e-17, 3.4874e-16, 8.5335e-16, 2.8842e-15, 1.0427e-16, 4.3297e-17,\n        1.2022e-15, 3.2846e-16, 2.2252e-15, 1.3137e-18, 2.2382e-16, 5.0257e-17,\n        5.9053e-16, 1.6287e-15, 1.3915e-16, 3.6341e-19, 6.2304e-18, 3.4628e-17,\n        2.1056e-16, 1.4621e-16, 3.1464e-16, 4.4039e-16, 4.0741e-17, 3.3485e-16,\n        9.2019e-16, 1.0668e-16, 6.3573e-16, 1.0061e-15, 2.2585e-15, 1.3883e-15,\n        3.5670e-16, 3.7955e-16, 6.9729e-16, 1.5652e-16, 2.7827e-17, 4.8357e-16,\n        5.0564e-16, 2.4417e-18, 3.0529e-15, 1.4523e-15, 1.3821e-17, 4.3511e-18,\n        1.6472e-16, 5.1954e-16, 5.6672e-16, 3.5148e-16, 4.0218e-16, 1.8270e-16,\n        1.6649e-15, 6.2191e-16, 9.2419e-17, 1.7225e-15, 1.3198e-17, 3.2820e-16,\n        4.8614e-16, 1.2252e-16, 1.0234e-17, 3.1667e-15, 7.9721e-17, 1.9019e-15,\n        1.1794e-17, 1.9913e-15, 3.6852e-16, 3.7442e-15, 4.4203e-15, 1.0713e-16,\n        1.0662e-15, 2.9442e-15, 2.0987e-15, 1.9514e-16, 5.1911e-15, 1.9877e-15,\n        7.2849e-15, 1.9621e-17, 2.0797e-16, 6.2376e-16, 2.4694e-16, 8.3332e-16,\n        2.6343e-15, 9.6957e-17, 1.9619e-16, 6.3558e-16, 7.5790e-15, 1.8197e-16,\n        1.4372e-15, 1.0229e-17, 5.9640e-15, 5.7337e-15, 9.8354e-16, 8.2919e-16,\n        6.5541e-18, 1.0609e-15, 1.0897e-15, 8.2907e-16, 2.4169e-15, 5.8989e-18,\n        3.1290e-17, 1.2344e-17, 6.3702e-18, 1.0085e-16, 2.5662e-17, 1.4676e-16,\n        5.9641e-16, 1.8918e-15, 2.9047e-16, 1.5169e-16, 8.6972e-17, 4.7852e-16,\n        1.3167e-16, 1.8321e-15, 6.8405e-16, 1.3351e-15, 2.0841e-15, 7.0428e-16,\n        2.8270e-17, 6.6832e-18, 1.3307e-15, 1.7973e-16, 1.2319e-16, 7.0940e-17,\n        2.3100e-15, 3.7267e-16, 5.7320e-17, 2.1158e-15, 9.0282e-17, 2.5541e-16,\n        3.6162e-16, 1.4009e-15, 2.6499e-15, 2.4433e-17, 1.0154e-16, 2.0166e-16,\n        1.4468e-16, 3.7966e-18, 5.9881e-16, 1.6655e-16, 4.8718e-17, 2.1894e-18,\n        4.8273e-16, 7.6731e-16, 1.1443e-15, 5.0164e-16, 8.8946e-17, 3.6526e-16,\n        8.5643e-18, 1.2143e-15, 1.2618e-17, 3.6602e-17, 1.8230e-15, 8.9932e-16,\n        2.9312e-18, 1.8087e-18, 1.2083e-16, 8.0881e-16, 3.6040e-15, 2.2355e-16,\n        2.1654e-16, 1.1455e-16, 3.1717e-16, 3.7409e-16, 3.0559e-16, 5.9733e-16,\n        6.9244e-18, 7.4796e-17, 8.5160e-16, 1.7150e-16, 2.1619e-16, 1.2649e-15,\n        7.9487e-19, 9.1414e-16, 1.1979e-15, 1.0900e-15, 9.2516e-18, 6.0697e-17,\n        6.9439e-17, 4.4130e-16, 1.1301e-18, 2.8590e-17, 2.0601e-16, 2.4062e-15,\n        2.2992e-16, 3.7554e-17, 1.0109e-15, 1.1461e-16, 9.8784e-17, 8.2893e-16,\n        5.8375e-16, 6.9525e-16, 3.7795e-16, 1.0749e-15, 1.2974e-18, 7.8222e-17,\n        5.8334e-16, 6.6346e-16, 1.1030e-17, 1.7091e-15, 8.1652e-17, 9.8791e-16,\n        5.3649e-17, 4.2069e-17, 1.6441e-17, 3.2359e-16, 2.0820e-15, 6.6790e-17,\n        3.4020e-16, 5.4958e-16, 1.3166e-15, 1.3994e-16, 7.2739e-18, 1.5676e-16,\n        9.1442e-17, 8.8781e-17, 4.4403e-17, 9.9208e-17, 2.5303e-17, 6.8817e-15,\n        1.8271e-15, 1.1357e-15, 3.4298e-18, 3.7691e-17, 3.7616e-16, 1.2152e-15,\n        3.6036e-15, 1.1500e-15, 4.0552e-17, 2.2085e-16, 1.2777e-15, 1.1181e-17,\n        7.3198e-16, 5.5802e-16, 1.5095e-15, 3.2981e-16, 2.3810e-16, 4.9127e-17,\n        9.8406e-17, 4.3789e-17, 1.1635e-15, 2.9990e-16, 2.8872e-16, 2.0398e-15,\n        3.5957e-16, 3.4429e-18, 7.8661e-16, 7.9789e-16, 3.3263e-15, 6.8238e-16,\n        9.7932e-16, 5.3557e-16, 8.3972e-19, 4.2418e-16, 2.2978e-30, 5.6272e-32,\n        3.5795e-31, 6.5112e-32, 3.6775e-31, 1.0948e-31, 5.9114e-31, 1.4252e-32,\n        3.2284e-31, 5.0209e-31, 4.6983e-31, 1.2069e-32, 3.0504e-34, 2.5644e-32,\n        1.4422e-31, 1.0778e-32, 2.5042e-31, 1.9182e-32, 2.2839e-31, 5.4957e-32,\n        3.5313e-33, 1.7712e-31, 1.0017e-32, 2.0640e-31, 2.9222e-33, 1.7662e-31,\n        3.9021e-31, 1.6875e-32, 1.0185e-30, 3.4740e-31, 5.6510e-32, 8.1403e-32,\n        1.0426e-31, 6.2391e-31, 5.8241e-32, 4.8005e-32, 1.9514e-32, 1.0576e-31,\n        2.8486e-31, 5.6322e-32, 9.9889e-33, 1.1582e-31, 2.3294e-32, 5.1514e-32,\n        4.8687e-32, 4.3910e-31, 1.7973e-31, 6.0212e-32, 6.4553e-32, 4.0085e-32,\n        8.0251e-32, 2.6591e-31, 2.6820e-31, 3.3614e-31, 1.3690e-31, 3.1483e-32,\n        2.7990e-31, 4.8179e-31, 6.6033e-31, 2.8822e-31, 2.3279e-31, 1.6134e-31,\n        1.1100e-32, 9.9905e-32, 7.5284e-34, 3.8992e-31, 1.0324e-30, 3.2184e-31,\n        8.6953e-33, 5.1672e-32, 3.1419e-31, 1.0506e-31, 1.7039e-31, 1.0248e-32,\n        8.7771e-32, 4.7469e-32, 1.6700e-31, 4.7043e-31, 1.6206e-30, 5.9368e-31,\n        6.4312e-32, 4.8721e-31, 5.9019e-32, 7.8748e-31, 9.6346e-32, 8.8921e-32,\n        7.9163e-31, 2.7524e-31, 1.4262e-31, 1.8249e-32, 3.1875e-32, 1.7352e-31,\n        1.8200e-31, 3.8298e-32, 1.4797e-31, 1.0522e-32, 1.3969e-32, 4.5730e-31,\n        2.4023e-32, 1.3766e-31, 2.0612e-31, 1.4378e-32, 1.2560e-31, 1.1868e-31,\n        4.7636e-32, 6.5361e-32, 4.0790e-32, 6.8090e-31, 2.7658e-31, 4.9247e-32,\n        3.7666e-32, 2.0032e-31, 2.1691e-31, 4.0737e-31, 1.7157e-31, 8.0285e-31,\n        2.2048e-31, 2.9869e-31, 6.9457e-31, 8.5282e-31, 5.1603e-31, 2.7181e-31,\n        3.2067e-31, 1.5703e-31, 3.5956e-32, 3.2210e-32, 1.7887e-30, 2.1014e-33,\n        3.3977e-31, 2.0228e-32, 7.9469e-32, 9.6858e-32, 2.4937e-31, 8.0578e-32,\n        2.1133e-32, 2.3733e-35, 5.4267e-33, 3.1921e-32, 7.1651e-32, 1.8620e-31,\n        1.7708e-32, 4.2068e-31, 1.6117e-30, 1.8194e-31, 5.2056e-31, 6.9364e-32,\n        2.8462e-31, 7.8503e-31, 3.6172e-31, 2.4241e-32, 2.6104e-32, 1.0572e-30,\n        1.8203e-31, 2.0464e-31, 9.4908e-33, 1.3021e-31, 4.5077e-31, 1.4760e-31,\n        7.7344e-34, 2.0358e-31, 5.9918e-32, 5.8322e-32, 3.3106e-31, 3.0603e-32,\n        2.1393e-32, 1.8568e-31, 1.2279e-31, 7.8630e-33, 7.2293e-32, 8.4171e-31,\n        4.9711e-32, 1.7274e-30, 8.7889e-31, 1.2384e-32, 2.8634e-31, 9.0804e-31,\n        8.9851e-31, 3.3965e-32, 4.2621e-32, 7.4448e-32, 5.2262e-32, 2.2482e-31,\n        4.4529e-33, 2.2671e-31, 1.1597e-31, 2.9173e-32, 1.0585e-31, 1.9580e-33,\n        3.6657e-31, 2.2449e-33, 4.5086e-31, 2.5167e-31, 2.7764e-31, 6.3070e-31,\n        5.7491e-31, 2.0839e-32, 1.1747e-30, 1.0670e-31, 2.7191e-31, 5.8718e-32,\n        1.7371e-33, 9.8444e-32, 1.3547e-31, 2.2597e-31, 2.6092e-32, 8.0985e-32,\n        5.3230e-33, 1.2284e-31, 1.8476e-31, 8.0932e-33, 3.6226e-31, 6.8792e-33,\n        2.0772e-31, 7.7433e-32, 3.9628e-31, 1.8137e-31, 1.0331e-32, 8.2072e-32,\n        1.9596e-31, 5.3454e-32, 5.9271e-32, 1.0799e-31, 2.1131e-31, 1.2406e-31,\n        1.6344e-31, 4.7275e-33, 2.1180e-31, 2.2774e-31, 6.0832e-31, 6.1756e-31,\n        1.5357e-32, 1.1305e-31, 1.6125e-32, 1.0779e-31, 2.3072e-33, 2.9735e-32,\n        3.5686e-32, 1.9041e-31, 6.5518e-33, 9.6570e-33, 4.2447e-32, 4.7439e-31,\n        3.3868e-32, 9.7281e-32, 2.5881e-32, 3.2417e-31, 6.6878e-31, 6.9631e-35,\n        6.3654e-31, 4.4801e-31, 6.1304e-31, 2.8852e-33, 1.0090e-31, 6.9146e-31,\n        3.0461e-32, 1.3644e-32, 8.7774e-13, 1.3257e-12, 1.3948e-14, 1.1140e-11,\n        3.0310e-12, 7.6036e-13, 1.9167e-12, 5.4003e-12, 1.0748e-13, 1.3425e-12,\n        8.8795e-12, 2.1071e-14, 6.2435e-13, 2.2625e-12, 1.1229e-11, 2.5434e-13,\n        3.3244e-12, 1.1169e-11, 7.6740e-13, 1.2850e-12, 3.5772e-12, 6.3275e-15,\n        4.7045e-12, 1.4372e-11, 9.0587e-13, 1.5664e-11, 3.3139e-13, 2.5054e-12,\n        2.0597e-12, 9.4364e-12, 2.0146e-12, 2.1854e-14, 3.5533e-13, 2.3128e-14,\n        4.3272e-12, 6.0001e-12, 6.0114e-12, 9.2311e-13, 1.7581e-13, 5.8776e-12,\n        1.1147e-11, 5.5990e-12, 6.6264e-12, 5.1708e-12, 2.2461e-12, 1.4722e-14,\n        4.0249e-12, 1.1462e-12, 5.4942e-12, 3.9154e-12, 2.1914e-11, 4.2620e-12,\n        7.2631e-13, 3.3762e-14, 1.8755e-12, 1.8706e-12, 1.2011e-11, 1.0250e-12,\n        2.8729e-12, 2.9027e-12, 2.9765e-12, 1.1425e-12, 1.0990e-12, 8.0734e-13,\n        3.1215e-13, 2.5147e-14, 6.5295e-12, 2.4350e-12, 3.3112e-12, 4.2946e-12,\n        3.1049e-13, 1.0072e-13, 6.1339e-14, 1.4566e-11, 4.7155e-13, 1.9892e-11,\n        3.8108e-12, 1.1045e-11, 5.0680e-12, 5.1284e-13, 3.2115e-12, 3.7494e-11,\n        1.3118e-12, 8.2886e-12, 3.0112e-13, 1.1266e-11, 4.0708e-12, 2.5800e-13,\n        7.2271e-14, 4.5091e-12, 6.5587e-12, 3.0592e-12, 2.2141e-14, 6.2799e-12,\n        1.6082e-11, 7.9256e-13, 3.3866e-13, 6.5728e-12, 6.1881e-13, 6.2210e-13,\n        8.3281e-13, 3.9140e-12, 1.0479e-13, 5.1375e-13, 1.3881e-12, 1.3860e-11,\n        8.3876e-12, 1.1941e-12, 1.2706e-13, 1.4892e-12, 6.7140e-13, 2.2397e-12,\n        4.1851e-12, 2.3187e-13, 3.2135e-12, 4.4724e-12, 1.1827e-12, 5.5869e-13,\n        7.8950e-12, 1.5476e-13, 5.5090e-13, 3.0284e-11, 5.5404e-12, 1.9130e-11,\n        6.9026e-12, 2.2893e-12, 1.7021e-13, 2.7997e-13, 3.4591e-12, 3.8778e-12,\n        2.1636e-12, 3.0590e-12, 7.6568e-13, 1.2954e-14, 3.3105e-12, 1.2383e-12,\n        1.7781e-12, 8.1996e-12, 9.2527e-13, 1.8089e-13, 1.4636e-15, 6.7807e-12,\n        2.9350e-12, 6.8403e-14, 2.0160e-12, 1.6296e-12, 4.2521e-13, 1.4501e-11,\n        6.1924e-13, 1.1190e-14, 1.9146e-12, 2.1659e-11, 1.1248e-12, 9.1855e-13,\n        8.5893e-12, 2.5763e-13, 9.7622e-14, 1.6231e-12, 1.4121e-15, 2.7524e-12,\n        6.9283e-12, 9.7786e-12, 2.8271e-13, 1.8488e-12, 2.5766e-12, 4.6350e-12,\n        2.1998e-12, 1.0678e-12, 1.7747e-13, 1.1593e-11, 7.1484e-13, 1.4957e-11,\n        9.4621e-12, 7.1391e-13, 2.4101e-12, 6.2597e-13, 3.8636e-14, 7.9300e-13,\n        6.4669e-12, 2.4944e-14, 4.4979e-12, 1.2853e-11, 3.3945e-14, 3.0572e-12,\n        8.4930e-12, 7.9902e-12, 2.1601e-12, 1.2484e-12, 9.7515e-14, 8.2193e-12,\n        1.4984e-13, 2.7828e-12, 1.2613e-11, 1.3055e-11, 2.2725e-12, 3.8277e-12,\n        6.8286e-12, 1.6506e-11, 1.5939e-13, 7.3215e-12, 9.0980e-13, 4.5850e-13,\n        1.6999e-11, 1.1662e-12, 5.3875e-13, 2.0797e-12, 6.7420e-13, 1.2232e-12,\n        1.9007e-12, 6.2310e-13, 3.5893e-12, 2.6925e-12, 8.6406e-12, 6.6996e-13,\n        9.9942e-14, 2.7192e-11, 8.5598e-13, 1.6101e-11, 3.2666e-14, 1.7603e-12,\n        1.3775e-12, 6.1187e-12, 6.9856e-12, 4.1244e-12, 8.9290e-12, 3.1637e-13,\n        5.6616e-12, 7.5024e-13, 3.4891e-11, 2.2587e-13, 8.2396e-13, 3.4299e-12,\n        3.5050e-12, 2.0482e-12, 7.1594e-12, 2.5906e-12, 7.2651e-13, 6.4472e-13,\n        4.3733e-12, 2.6201e-14, 6.3702e-12, 2.4042e-13, 5.9099e-16, 1.1925e-12,\n        6.0651e-12, 8.0081e-14, 1.6027e-12, 1.5621e-13, 2.1099e-13, 1.4163e-12,\n        5.0702e-12, 3.6620e-12, 2.7557e-12, 1.1961e-11, 3.6146e-12, 1.0546e-12],\n       device='cuda:0')"
-      },
-      "54": {
-        "step": "tensor(11268.)",
-        "exp_avg": "tensor([[ 5.6052e-45,  5.6052e-45, -5.6052e-45,  ...,  5.6052e-45,\n         -5.6052e-45, -5.6052e-45],\n        [ 5.6052e-45,  5.6052e-45, -5.6052e-45,  ...,  5.6052e-45,\n         -5.6052e-45, -5.6052e-45],\n        [ 5.6052e-45,  5.6052e-45, -5.6052e-45,  ...,  5.6052e-45,\n         -5.6052e-45, -5.6052e-45],\n        ...,\n        [ 5.6052e-45,  5.6052e-45, -5.6052e-45,  ...,  5.6052e-45,\n         -5.6052e-45, -5.6052e-45],\n        [-5.6052e-45,  5.6052e-45,  5.6052e-45,  ...,  5.6052e-45,\n          5.6052e-45,  5.6052e-45],\n        [ 5.6052e-45,  5.6052e-45, -5.6052e-45,  ...,  5.6052e-45,\n         -5.6052e-45, -5.6052e-45]], device='cuda:0')",
-        "exp_avg_sq": "tensor([[2.4202e-13, 7.9313e-15, 5.5237e-15,  ..., 1.8325e-14, 9.7267e-14,\n         1.2686e-13],\n        [1.2758e-13, 5.8437e-15, 4.1798e-15,  ..., 1.5476e-14, 5.5270e-14,\n         7.2375e-14],\n        [2.3338e-12, 5.2914e-14, 4.6270e-14,  ..., 7.6578e-14, 9.3305e-13,\n         1.1718e-12],\n        ...,\n        [7.3406e-13, 1.8380e-14, 1.3340e-14,  ..., 3.3773e-14, 2.9351e-13,\n         3.8971e-13],\n        [1.8208e-14, 1.3790e-15, 4.2671e-16,  ..., 6.9496e-15, 7.1599e-15,\n         6.6839e-15],\n        [4.1155e-14, 3.3339e-15, 2.5957e-15,  ..., 1.1174e-14, 2.0243e-14,\n         2.1292e-14]], device='cuda:0')"
-      },
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@@ -638,14 +145,6 @@
     "_is_initial": false,
     "_get_lr_called_within_step": false,
     "_last_lr": [
-      0.01,
-      0.01,
-      0.01,
-      0.01,
-      0.01,
-      0.01,
-      0.01,
-      0.01,
       0.01,
       0.01,
       0.01,
@@ -653,32 +152,21 @@
     ]
   },
   "metrics": {
-    "final_val_acc": 82.374
+    "final_val_acc": 75.38
   },
   "train_config": {
     "name": "david_training",
-    "run_id": "20251012_050214",
+    "run_id": "20251012_060013",
     "dataset_name": "AbstractPhil/imagenet-clip-features-orderly",
-    "model_variant": "clip_vit_l14",
+    "model_variant": "clip_vit_b16",
     "num_classes": 1000,
-    "preset": "clip_vit_l14_deep",
+    "preset": "small_fast",
     "custom_config_path": null,
     "num_classes_override": null,
     "use_belly_override": null,
     "belly_expand_override": null,
-    "progressive_training_override": true,
-    "scale_warmup_epochs_override": {
-      "256": 0,
-      "512": 1,
-      "768": 2,
-      "1024": 3,
-      "1280": 4,
-      "1536": 5,
-      "1792": 6,
-      "2048": 7,
-      "2304": 8,
-      "2560": 9
-    },
+    "progressive_training_override": false,
+    "scale_warmup_epochs_override": null,
     "num_epochs": 10,
     "batch_size": 1024,
     "learning_rate": 0.01,