Ok, removed the rewrap.
Yes, I’ve run main.py which finishes and saves a model.pt file, then I immediately run generate.py.
The only difference is, I replaced one line in generate.py 1  ("input = Variable") with code borrowed from main.py:
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(1)
# input = Variable(torch.rand(1, 1).mul(ntokens).long(), volatile=True)
def batchify(data, bsz):       # breaks into parallel streams
    nbatch = data.size(0) // bsz
    data = data.narrow(0, 0, nbatch * bsz)
    data = data.view(bsz, -1).t().contiguous()
    if args.cuda:
        data = data.cuda()
    return data
eval_batch_size = 10
test_data = batchify(corpus.test, eval_batch_size)
def get_batch(source, i, evaluation=False):
    bptt = 20
    seq_len = min(bptt, len(source) - 1 - i)
    data = Variable(source[i:i+seq_len], volatile=evaluation)
    target = Variable(source[i+1:i+1+seq_len].view(-1))
    return data, target
input, target = get_batch(test_data, 0, evaluation=True)

…and the rest of generate.py  is unchanged from your original.
Running this version of the code produces an error about hidden size…

Traceback (most recent call last):
File “generate.py 1”, line 93, in 
output, hidden = model(input, hidden)
File “/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py”, line 210, in call
result = self.forward(*input, **kwargs)
File “/home/mcskwayrd/neural/torch/pytorch/examples/word_language_model/model.py”, line 28, in forward
output, hidden = self.rnn(emb, hidden)
File “/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py”, line 210, in call
result = self.forward(*input, **kwargs)
File “/usr/local/lib/python2.7/dist-packages/torch/nn/modules/rnn.py”, line 79, in forward
return func(input, self.all_weights, hx)
File “/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/rnn.py”, line 228, in forward
return func(input, *fargs, **fkwargs)
File “/usr/local/lib/python2.7/dist-packages/torch/autograd/function.py”, line 202, in _do_forward
flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
File “/usr/local/lib/python2.7/dist-packages/torch/autograd/function.py”, line 218, in forward
result = self.forward_extended(*nested_tensors)
File “/usr/local/lib/python2.7/dist-packages/torch/nn/_functions/rnn.py”, line 180, in forward_extended
cudnn.rnn.forward(self, input, hx, weight, output, hy)
File “/usr/local/lib/python2.7/dist-packages/torch/backends/cudnn/rnn.py”, line 244, in forward
hidden_size, tuple(hx.size())))
RuntimeError: Expected hidden size (2, 10L, 200), got (2L, 1L, 200L)

It seems that it wants the hidden size to somehow follow the batch size, only with no "L"s…