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Fixes #12 in the easiest way I could think of.

I noticed some opportunity to collapse some of the logic, in case you
are interested.

There's currently a discrepancy in the tokenization between the router
and python server code. The latter includes special tokens but former
does not.

This results in a token count mismatch for seq2seq models such as mt0
where the tokenizer emits an EOS token at the end.

This in turn results in some unexpected/incorrect output, in particular
when batch concatenation is involved, because the python code uses the
input length passed from the router for each row.

As far as I can tell, it is better to include this token in the encoder
`input_ids`, so I guess it's best to just adjust on the router side.

AFAIK there is no torch device type called "gpu".

- Avoid theoretical hang in batcher loop
- Avoid a couple of clones in the router generate method
- Keep attention mask tensors as integers
- Remove num_heads attribute
Co-authored-by: OlivierDehaene <Olivier.dehaene@gmail.com>

Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>

Added validation logic