The example seems not working, does anyone have a working code for this model ?

Cell In[22], line 10
      7 images.long()
      9 # Pass the sample tensors to the model's forward function
---> 10 output = model.forward(text_tokens=text_tokens, images=images)
     12 # Print the output from the model
     13 print(f"Output: {output}")

File /data/conda/envs/eqbench/lib/python3.10/site-packages/kosmosx/model.py:251, in Kosmos.forward(self, text_tokens, images, **kwargs)
    248     raise
    250 try:
--> 251     return self.decoder(model_input, passed_x=model_input)[0]
    252 except Exception as e:
    253     logging.error(f"Failed during model forward pass: {e}")

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torchscale/architecture/decoder.py:399, in Decoder.forward(self, prev_output_tokens, self_attn_padding_mask, encoder_out, incremental_state, features_only, return_all_hiddens, token_embeddings, **kwargs)
    387 def forward(
    388     self,
    389     prev_output_tokens,
   (...)
    397 ):
    398     # embed tokens and positions
--> 399     x, _ = self.forward_embedding(
    400         prev_output_tokens, token_embeddings, incremental_state
    401     )
    402     is_first_step = self.is_first_step(incremental_state)
    404     # relative position

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torchscale/architecture/decoder.py:368, in Decoder.forward_embedding(self, tokens, token_embedding, incremental_state)
    365         positions = positions[:, -1:]
    367 if token_embedding is None:
--> 368     token_embedding = self.embed_tokens(tokens)
    370 x = embed = self.embed_scale * token_embedding
    372 if positions is not None:

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File /data/conda/envs/eqbench/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:127, in Embedding.forward(self, input)
    126 def forward(self, input: Tensor) -> Tensor:
--> 127     emb = F.embedding(
    128         input,
    129         self.weight,
    130         self.padding_idx,
    131         self.max_norm,
    132         self.norm_type,
    133         self.scale_grad_by_freq,
    134         self.sparse,
    135     )
    137     return emb

File /data/conda/envs/eqbench/lib/python3.10/site-packages/torch/nn/functional.py:2233, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
   2227     # Note [embedding_renorm set_grad_enabled]
   2228     # XXX: equivalent to
   2229     # with torch.no_grad():
   2230     #   torch.embedding_renorm_
   2231     # remove once script supports set_grad_enabled
   2232     _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2233 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)