Ah yes, sigmoid needs to be in between the Linear layers.
But even when I added the sigmoid activation:

from collections import Counter 
import random
random.seed(100)

import torch
from torch import nn
from torch.autograd import Variable
from torch import FloatTensor
from torch import optim
use_cuda = torch.cuda.is_available()


all_results=[]

for _ in range(100):
    hidden_dim = 2

    model = nn.Sequential(nn.Linear(input_dim, hidden_dim),
                          nn.Sigmoid(), # Does the sigmoid has a build in biased? 
                          nn.Linear(hidden_dim, output_dim),
                          nn.Sigmoid())

    criterion = nn.MSELoss()
    learning_rate = 0.03
    optimizer = optim.SGD(model.parameters(), lr=learning_rate)
    num_epochs = 3000

    for _ in range(num_epochs):
        predictions = model(X_pt)
        loss_this_epoch = criterion(predictions, Y_pt)
        loss_this_epoch.backward()
        optimizer.step()
        ##print([float(_pred) for _pred in predictions], list(map(int, Y_pt)), loss_this_epoch.data[0])

    x_pred = [int(model(_x)) for _x in X_pt]
    y_truth = list([int(_y[0]) for _y in Y_pt])
    all_results.append([x_pred == y_truth, x_pred, loss_this_epoch.data[0]])


tf, outputsss, losses__ = zip(*all_results)
print(Counter(tf))

I’m getting a rate of 37 out of 100, the model successfully learnt XOR. Any ideas?