Made some progress.
I was able to convert the model to an ordereddict
I managed to extract the model weights from the .pt file. Is the information in this file in the correct format now?
model_verb = torch.load("reverb_full.pt", map_location="cuda").eval()
model_verb.state_dict()


  reverb_full.pt
              OrderedDict([('blocks.0.conv.weight',
              tensor([[[-0.1298,  0.0699, -0.4024, -0.4144, -0.6612, -0.3614, -0.2711,
                         0.1121,  0.3811,  0.4887,  0.6554, -0.4438,  0.3937]],
              
                      [[ 0.1641,  0.1940,  0.2088, -0.0812, -0.1690, -0.3806, -0.4686,
                        -0.4721, -0.5710, -0.3671, -0.3867,  0.6685, -0.0490]],
              
                      [[ 0.4627,  0.1778,  0.4389, -0.0471,  0.0029, -0.4056, -0.3544,
                        -0.2737, -0.2220,  0.4237,  0.3140, -0.0832,  0.1095]],
              
                      [[ 0.1386, -0.0815, -0.0198, -0.5040, -0.0545, -0.0723,  0.3019,
                         0.4373,  0.1611,  0.4337, -0.1162, -0.2702,  0.1192]],
              
                      [[ 0.0046,  0.4190,  0.3304,  0.3830,  0.2753,  0.0691, -0.0585,
                        -0.4008, -0.3159, -0.5296, -0.0253,  0.2469,  0.4646]],
              
                      [[ 0.3359,  0.6874,  0.4409,  0.1396,  0.1342,  0.0766,  0.4255,
                         0.2557,  0.6377,  0.1431,  0.2185, -0.0497, -0.2628]],
              
                      [[ 0.2687,  0.5740,  0.5573,  0.3525, -0.0186, -0.1923, -0.3670,
                        -0.2073, -0.3086, -0.1535, -0.4035,  0.5661, -0.4399]],
              
                      [[-0.0348,  0.2481,  0.3841,  0.5633,  0.3016,  0.6492,  0.0361,
                         0.3763, -0.2285,  0.0285, -0.0704, -0.3581,  0.4612]]])),
             ('blocks.0.conv.bias',
              tensor([-0.1317,  0.0801, -0.0657,  0.0111, -0.2019,  0.0651, -0.1624,  0.0881])),
             ('blocks.0.film.adaptor.weight', tensor([[-0.1962, -0.4742],
                      [ 0.0118,  0.2025],
                      [-0.0262, -0.5196],
                      [-0.7068,  0.0866],
                      [ 0.3649,  0.2775],
                      [ 0.1817,  0.3868],
                      [ 0.4642,  0.1849],
                      [ 0.5937, -0.4390],
                      [ 0.2238,  0.0662],
                      [ 0.1939,  0.0262],
                      [-0.4839, -0.3618],
                      [ 0.4020, -0.4487],
                      [ 0.1656,  0.2309],
                      [ 0.0682, -0.2875],
                      [ 0.0196, -0.0321],
                      [-0.6210,  0.5165]])),
             ('blocks.0.film.adaptor.bias',
              tensor([ 0.6033, -1.1071, -0.5714, -0.4874,  0.5016,  0.3080, -0.7018,  0.4283,
                      -0.1645, -0.7909, -0.0444,  0.4886, -0.1715,  0.5332,  0.4520,  0.7016])),
             ('blocks.0.act.weight', tensor([1.0010])),
             ('blocks.0.res.weight', tensor([[[ 0.5295]],
              
                      [[ 0.8668]],
              
                      [[-0.4472]],
              
                      [[-0.2738]],
              
                      [[ 0.0289]],
              
                      [[-0.0996]],
              
                      [[ 0.7559]],
              
                      [[ 0.2094]]])),
             ('blocks.1.conv.weight',
              tensor([[[-7.7118e-02,  8.3447e-02,  4.0766e-02,  1.0021e-01, -1.1097e-01,
                        -1.1143e-01,  1.7931e-01,  1.1139e-01,  1.7485e-01,  3.9384e-01,
                         3.1975e-01,  8.2028e-02, -3.1758e-02],
                       [ 5.2299e-02, -2.8190e-02, -6.4530e-02, -1.2040e-01, -6.6333e-02,
                        -5.0940e-02,  1.7104e-03,  1.6328e-01,  5.0179e-01,  3.3963e-02,
                         3.1775e-01, -2.9564e-02,  9.1750e-02],
                       [-9.0324e-02, -1.2507e-01, -2.4028e-03,  1.9625e-01,  1.4102e-02,
                         1.2354e-01, -7.0674e-03,  1.6727e-01, -4.3540e-02, -3.8468e-02,
                         5.8582e-02,  9.5664e-02, -2.1854e-01],
                       [ 4.7151e-02, -1.0220e-02, -2.5507e-02, -5.0436e-02,  7.4724e-02,
                        -1.5879e-02,  6.3913e-02, -1.9377e-01, -1.3045e-01, -8.6763e-02,
                        -2.4228e-01, -1.5414e-01, -5.2916e-03],
                       [ 1.2142e-01,  1.9297e-01,  7.4695e-02, -1.5313e-01, -1.0863e-01,
                        -1.1971e-02, -1.1517e-01, -3.2580e-01, -2.2143e-01, -8.9785e-02,
                        -1.3874e-01, -3.0188e-02, -1.7816e-01],
                       [-5.5444e-02, -1.6797e-01,  8.0191e-02, -9.3637e-02, -8.5481e-02,
                        -9.3189e-02, -1.5959e-01,  1.6686e-01,  1.9165e-02,  1.2173e-01,
                        -1.1160e-01, -8.7511e-02, -9.3956e-02],
                       [-7.4850e-02,  1.8889e-02, -1.8180e-01,  6.6027e-02,  1.1201e-01,
                        -3.8151e-02,  6.2812e-02, -1.0170e-01,  4.5755e-01, -1.6868e-01,
                         4.5296e-02, -3.9180e-02, -1.7269e-01],
                       [-5.1820e-02, -1.5111e-01, -4.1044e-02, -2.0386e-01, -7.6540e-02,
                        -1.7273e-01, -6.0671e-02,  6.4420e-03,  9.3887e-02,  2.3023e-02,
                         1.4818e-02, -4.1135e-02,  3.2534e-03]],
              
                      [[ 2.4877e-01,  3.7433e-01, -2.8565e-02, -7.1523e-02, -5.1796e-02,
                        -2.0912e-02,  1.1077e-01,  4.3306e-03,  1.3783e-01,  3.1706e-01,
                        -2.6163e-02, -3.7166e-01,  6.1846e-02],
                       [-1.5473e-01,  1.8668e-02,  1.3543e-02,  9.9810e-02, -1.6062e-01,
                        -2.7790e-01, -1.8959e-01, -4.4618e-03,  1.5717e-01, -1.3372e-01,
                         4.5878e-02, -3.1708e-01,  1.3398e-01],
                       [ 1.8705e-01, -3.2216e-02, -1.0383e-01, -1.7633e-01,  3.4827e-02,
                         1.1517e-01,  9.1034e-02, -3.0097e-02, -4.7922e-02,  1.6160e-01,
                         2.0551e-01,  2.4782e-01,  6.1695e-01],
                       [ 1.7939e-01, -1.3421e-01, -9.1555e-02, -6.3503e-02,  1.9247e-02,
                         1.0486e-01, -1.9626e-02,  2.2244e-01, -2.6803e-01, -8.8263e-02,
                         4.4043e-02, -1.6127e-01, -1.1129e-01],
                       [ 2.0268e-02, -3.6074e-01,  1.6279e-01,  2.2947e-01, -4.9506e-02,
                        -5.7764e-02, -8.1883e-02,  1.3576e-02, -2.1019e-01, -3.0769e-01,
                        -1.4614e-01,  1.3390e-01,  9.9876e-02],
                       [-3.3786e-01, -2.1277e-01, -5.0107e-02,  1.2277e-01,  3.5645e-02,
                        -1.9554e-01, -2.9955e-01,  3.2802e-02, -1.8622e-02, -1.5235e-01,
                         1.1373e-01, -4.2711e-01, -1.6299e-01],
                       [-8.6759e-02,  8.7027e-02,  9.5962e-03, -1.6813e-01, -1.0420e-02,
                         8.1293e-02, -6.2374e-02, -2.5756e-03, -8.2061e-03,  4.7230e-02,
                         1.6142e-01,  9.9275e-02, -2.0132e-01],
                       [-3.7854e-01,  7.2318e-02, -2.3543e-02,  9.0526e-02, -6.8289e-02,
                        -1.4311e-01, -1.6145e-02,  8.7110e-02,  6.3401e-02, -8.6678e-02,
                        -2.9887e-02, -1.9675e-01, -8.4255e-02]],
              
                      [[ 7.0295e-02,  1.2211e-01,  2.4917e-01,  6.7158e-02,  2.3083e-01,
                         2.5574e-01,  2.6019e-01,  1.0869e-02, -4.0891e-01, -1.7995e-01,
                        -1.9274e-01, -7.6871e-02,  2.4176e-01],
                       [ 3.4064e-01,  1.4763e-01,  2.1635e-01,  5.1499e-02, -5.7420e-02,
                         1.3587e-01,  4.1135e-02, -1.6955e-01,  2.1021e-02,  3.2060e-01,
                        -3.1013e-02, -9.5046e-02,  2.5616e-01],
                       [-4.2208e-02, -9.4603e-02, -2.3270e-01, -2.0234e-01,  6.6833e-03,
                        -8.6855e-02, -1.6360e-01, -1.4886e-01,  4.9193e-02,  2.2812e-02,
                        -2.0007e-01, -2.3832e-02,  3.2806e-01],
                       [-1.1941e-01, -6.7837e-02, -1.5781e-01, -1.3439e-01,  6.2534e-03,
                        -8.0717e-02, -7.8080e-02,  7.5875e-02,  5.0799e-02, -4.6651e-02,
                         1.8118e-01,  2.4067e-02, -1.4432e-02],
                       [ 7.1198e-02, -2.6421e-02,  1.3267e-01,  7.9205e-03, -3.2077e-02,
                         1.4993e-01,  2.0277e-01,  4.2164e-01,  3.3229e-01,  5.8825e-02,
                         4.0194e-01,  3.6094e-01, -2.0041e-02],
                       [ 1.2827e-01, -1.2717e-01,  3.4417e-02,  2.0028e-01,  9.4749e-02,
                        -8.7568e-02,  5.3344e-02,  9.0669e-02,  2.4654e-01, -5.8251e-02,
                         2.0593e-01,  1.5784e-01,  1.3699e-01],
                       [-1.2355e-01, -6.0096e-02, -5.4322e-02,  3.3811e-02,  1.0688e-02,
                         1.3268e-02, -4.7507e-02, -3.4242e-02, -4.9244e-01,  2.3481e-01,
                        -9.5430e-02, -1.2007e-01, -1.5129e-02],
                       [ 1.9327e-01,  7.2998e-02, -5.4335e-02,  1.0879e-01, -1.8569e-02,
                        -4.5218e-02,  8.2211e-02, -2.6313e-02,  8.0196e-02,  1.5123e-01,
                         2.4969e-01,  2.4261e-02, -9.3883e-02]],
              
                      [[ 2.7044e-01, -4.4969e-01, -1.6666e-01, -2.6497e-01,  5.7645e-01,
                         2.0759e-01,  3.3068e-01,  2.9487e-01,  9.7794e-02,  2.5556e-01,
                        -3.5541e-01, -3.4951e-01, -8.3556e-01],
                       [-2.6069e-01,  3.1115e-01,  3.5495e-01,  8.8355e-02,  4.8561e-02,
                         1.2516e-01,  2.6930e-01, -5.6581e-01, -1.7098e-01,  1.8108e-01,
                        -1.8756e-01,  6.6137e-02, -1.2789e-01],
                       [ 4.2592e-01,  1.8713e-01, -3.2536e-02,  1.8321e-01, -5.8510e-03,
                         1.4366e-01, -9.2859e-02,  1.1593e-01,  3.1847e-01,  4.9595e-01,
                         4.8627e-01,  5.9260e-01, -7.5621e-01],
                       [-2.1626e-01, -1.5242e-01, -4.2155e-01, -3.3769e-01,  8.0346e-02,
                        -3.1913e-01, -1.1539e-01,  8.5033e-02,  4.8322e-01,  1.2597e-01,
                        -1.9418e-01,  2.7334e-01, -8.4192e-02],
                       [-4.7208e-01,  1.4594e-01, -1.9303e-01, -3.8485e-01, -2.2596e-01,
                        -3.0036e-01, -9.1248e-02,  7.3560e-02,  1.7228e-01,  8.5677e-02,
                        -3.8423e-02,  2.1600e-01, -9.3320e-02],
                       [-3.4113e-01, -2.0200e-02,  1.5072e-01, -2.7774e-01, -4.4605e-01,
                         5.7131e-02, -4.0423e-01, -2.1992e-01, -7.4423e-02,  1.5540e-03,
                        -3.4256e-01,  1.3901e-01, -1.3820e-01],
                       [-1.7193e-01,  2.5362e-01, -2.7563e-01,  3.3381e-01, -9.4688e-02,
                         2.3650e-02,  1.0144e-01,  5.4558e-02, -1.3107e-01,  7.1892e-03,
                        -2.0494e-01,  3.3572e-02, -1.3990e-01],
                       [-4.7345e-01, -2.8328e-01, -1.8328e-02, -2.7507e-01, -2.8603e-01,
                        -2.4580e-02, -9.7987e-02, -4.0284e-01, -4.8121e-02,  1.4557e-03,
                         1.1749e-02, -1.1105e-04, -2.1119e-01]],
              
                      [[-1.7054e-01,  2.2629e-01,  3.3506e-02, -2.2939e-01, -5.5154e-01,
                        -2.0296e-01, -1.3050e-01,  4.7344e-02, -1.6214e-01, -6.0605e-01,
                         1.6213e-01,  1.9646e-01, -5.8959e-01],
                     ....
                       [ 0.3206],
                       [ 0.3733],
                       [ 0.2837],
                       [-0.0026],
                       [-0.2636],
                       [-0.0969],
                       [ 0.1241]],
              
                      [[ 0.1754],
                       [-0.1104],
                       [-0.2569],
                       [-0.4021],
                       [-0.2580],
                       [ 0.2359],
                       [ 0.0509],
                       [-0.0661]],
              
                      [[ 0.2241],
                       [-0.0666],
                       [-0.3219],
                       [-0.3639],
                       [ 0.0821],
                       [ 0.2402],
                       [ 0.1352],
                       [-0.0578]],
              
                      [[-0.1299],
                       [ 0.1841],
                       [-0.1881],
                       [-0.2539],
                       [-0.1086],
                       [ 0.0413],
                       [-0.2463],
                       [-0.2694]],
              
                      [[-0.2778],
                       [ 0.2714],
                       [ 0.1492],
                       [ 0.3711],
                       [ 0.2107],
                       [-0.0703],
                       [ 0.1655],
                       [ 0.0572]]])),
             ('blocks.4.conv.weight',
              tensor([[[ 0.0296,  0.0557, -0.1451,  0.0341,  0.1050, -0.1492, -0.0750,
                        -0.1599,  0.1910, -0.2220, -0.0977, -0.1056,  0.0507],
                       [-0.1125, -0.1840,  0.0892,  0.0035,  0.0436, -0.2446,  0.1554,
                        -0.0668, -0.0590, -0.0521,  0.0253,  0.1049,  0.0643],
                       [-0.2005,  0.0047,  0.0133, -0.0830,  0.0705,  0.2304, -0.5210,
                         0.1070, -0.0858, -0.2326, -0.1822,  0.2396,  0.2717],
                       [ 0.0448,  0.1796, -0.1071,  0.1064,  0.0658, -0.2038,  0.0239,
                         0.1440,  0.3935,  0.0065,  0.0390, -0.0515, -0.0550],
                       [ 0.0437, -0.0964, -0.0884,  0.2518, -0.1431, -0.1283, -0.2663,
                         0.1250, -0.1318,  0.0853,  0.2320,  0.0187,  0.0087],
                       [-0.2744, -0.0103,  0.1717, -0.1905,  0.0357, -0.0739, -0.1197,
                         0.1075,  0.1777,  0.2062, -0.2827,  0.3773,  0.0940],
                       [ 0.0672,  0.0666, -0.0710, -0.1071, -0.0116,  0.1229, -0.0914,
                        -0.1270, -0.0077,  0.3019, -0.2137, -0.2694, -0.1641],
                       [ 0.2700, -0.0170, -0.1053,  0.0857, -0.2341, -0.0246,  0.0311,
                        -0.3872,  0.0005,  0.2699, -0.1138,  0.2301,  0.2700]]])),
             ('blocks.4.conv.bias', tensor([-0.1231])),
             ('blocks.4.film.adaptor.weight', tensor([[ 0.0314, -0.0043],
                      [-0.6116, -0.5727]])),
             ('blocks.4.film.adaptor.bias', tensor([-0.5461, -0.2179])),
             ('blocks.4.act.weight', tensor([0.9973])),
             ('blocks.4.res.weight', tensor([[[-0.2765],
                       [ 0.2469],
                       [ 0.2226],
                       [ 0.3452],
                       [-0.0464],
                       [ 0.0720],
                       [-0.1452],
                       [ 0.1538]]]))])


I am not trying to go from ordereddict to json. I followed this tutorial How to convert Ordereddict to JSON? - GeeksforGeeks but I am getting an error when trying to convert.
from collections import OrderedDict
import json

model_verb_cuda = torch.load("reverb_full.pt", map_location="cuda").eval()
od1 = model_verb_cuda.state_dict()
od1= json.dumps(od1)
# Using a JSON string
with open('json_data.json', 'w') as outfile:
    outfile.write(od1)

/usr/lib/python3.7/json/encoder.py in default(self, o)
    177 
    178         """
--> 179         raise TypeError(f'Object of type {o.__class__.__name__} '
    180                         f'is not JSON serializable')
    181 

TypeError: Object of type Tensor is not JSON serializable

is it because I am dealing with a nested ordereddict?