Based on the values, it looks as if y is using uninitialized values.
Do you get the NaN (and high value) outputs using this code?
x = torch.tensor([[ 0.4134, -1.0348, -0.7237,  0.6970,  0.0487,  0.1217],
        [ 1.2217,  0.2465, -0.0832,  0.5530, -0.5716, -0.4916],
        [-0.3497, -0.1285, -1.3026, -0.0845, -0.5295, -0.7776],
        [ 1.2792,  0.1692, -1.3023, -1.9925, -0.6869,  0.8478]])

model = nn.Linear(6, 8)

with torch.no_grad():
    model.weight = nn.Parameter(torch.tensor([[-0.3934,  0.2317, -0.3121, -0.1111, -0.2597, -0.2182],
        [-0.2294,  0.3308, -0.1488, -0.1515,  0.1625,  0.0586],
        [-0.3984,  0.1251,  0.0899,  0.0807,  0.0110,  0.1535],
        [-0.2065, -0.3367,  0.1329,  0.4042, -0.0387, -0.3794],
        [ 0.2129,  0.1050, -0.2263, -0.3991,  0.3312,  0.3797],
        [ 0.3508,  0.1268, -0.2395,  0.0672,  0.3916,  0.0131],
        [-0.1437, -0.4070, -0.0663, -0.1292, -0.0127, -0.0040],
        [ 0.0037, -0.0610, -0.0200,  0.2865, -0.0804,  0.1235]]))
    model.bias = nn.Parameter(
        torch.tensor([ 0.3034, -0.0240,  0.1232,  0.3308,  0.1517, -0.3978, -0.2769,  0.0284]))


y = model(x)
print(y)