Help with using gpt-neo models correctly
### System Info

TPU v2  (I am not using the `run_clm_mp.py`, so I do not really need TPU v3-8)

- `transformers` version: 4.29.0.dev0
- Platform: Linux-5.13.0-1027-gcp-x86_64-with-glibc2.31
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- Safetensors version: not installed
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?[/GPU](https://vscode-remote+ssh-002dremote-002b35-002e238-002e156-002e174.vscode-resource.vscode-cdn.net/GPU)?[/TPU](https://vscode-remote+ssh-002dremote-002b35-002e238-002e156-002e174.vscode-resource.vscode-cdn.net/TPU)?): 0.6.9 (tpu)
- Jax version: 0.4.8
- JaxLib version: 0.4.7
- Using GPU in script?: <fill in>  No
- Using distributed or parallel set-up in script?: <fill in>  NA



### Who can help?

@ArthurZucker and @younesbelkada

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)

### Reproduction

Please just use https://gist.github.com/buttercutter/df275bc0f26180cb0f77479482855b83/27ded5e8d0b416664bc2f396886510b857050679

### Expected behavior

There should not be any illegal characters like `)]` in the model output

Please advise on how the numerical values for `emb.at[:50257, :]` and `vocab_size=50264` are being derived.
Why `remove_columns=column_names` which will end up feeding nothing to the [nlp model training process](https://github.com/huggingface/transformers/blob/3335724376319a0c453049d0cd883504f530ff52/examples/research_projects/jax-projects/model_parallel/run_clm_mp.py#L356) ?

Feel free to correct me if I miss anything or wrong.

![image](https://github.com/huggingface/transformers/assets/3324659/ddbdcdec-4d56-41f2-b8e0-28a27cc6c365)

Edit:  It seems that `remove_columns` is to remove tokenizer inputs away from the tokenized outputs.<|||||>```python
input_ids[0] =  [   82  6442    25 ... 50256 50256 50256]
  0%|          | 0[/104](https://vscode-remote+ssh-002dremote-002b35-002e238-002e156-002e174.vscode-resource.vscode-cdn.net/104) [00:01<?, ?it[/s](https://vscode-remote+ssh-002dremote-002b35-002e238-002e156-002e174.vscode-resource.vscode-cdn.net/s)]
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:94                                                                                   │
│                                                                                                  │
│    91 │                                                                                          │
│    92 │   # Generate the answer                                                                  │
│    93 │   #Changing temperature, top_k and top_p does not seem to change the outcome             │
│ ❱  94 │   outputs = model.generate(                                                              │
│    95 │   │   input_ids = eval_tokenized_dataset["input_ids"][index][None, :],                   │
│    96 │   │   max_new_tokens=generated_max_length,                                               │
│    97 │   │   pad_token_id = model.config.eos_token_id,                                          │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/generation/flax_utils.py:429 in       │
│ generate                                                                                         │
│                                                                                                  │
│    426 │   │   │   )                                                                             │
│    427 │   │   elif generation_config.do_sample and generation_config.num_beams == 1:            │
│    428 │   │   │   logits_warper = self._get_logits_warper(generation_config=generation_config)  │
│ ❱  429 │   │   │   return self._sample(                                                          │
│    430 │   │   │   │   input_ids,                                                                │
│    431 │   │   │   │   generation_config.max_length,                                             │
│    432 │   │   │   │   generation_config.pad_token_id,                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/generation/flax_utils.py:682 in       │
│ _sample                                                                                          │
│                                                                                                  │
│    679 │   │   model = self.decode if self.config.is_encoder_decoder else self                   │
│    680 │   │                                                                                     │
│    681 │   │   # initialize model specific kwargs                                                │
│ ❱  682 │   │   model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model  │
│    683 │   │                                                                                     │
│    684 │   │   # initialize state                                                                │
│    685 │   │   state = SampleState(                                                              │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:661 in prepare_inputs_for_generation                                                          │
│                                                                                                  │
│   658 │   │   # initializing the cache                                                           │
│   659 │   │   batch_size, seq_length = input_ids.shape                                           │
│   660 │   │                                                                                      │
│ ❱ 661 │   │   past_key_values = self.init_cache(batch_size, max_length)                          │
│   662 │   │   # Note that usually one would have to put 0's in the attention_mask for x > inpu   │
│   663 │   │   # But since GPTNeo uses a causal mask, those positions are masked anyways.         │
│   664 │   │   # Thus we can create a single static attention_mask here, which is more efficien   │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:396 in init_cache                                                                             │
│                                                                                                  │
│   393 │   │   attention_mask = jnp.ones_like(input_ids)                                          │
│   394 │   │   position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),   │
│   395 │   │                                                                                      │
│ ❱ 396 │   │   init_variables = self.module.init(                                                 │
│   397 │   │   │   jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=   │
│   398 │   │   )                                                                                  │
│   399 │   │   return unfreeze(init_variables["cache"])                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/jax/_src/traceback_util.py:166 in                  │
│ reraise_with_filtered_traceback                                                                  │
│                                                                                                  │
│   163   def reraise_with_filtered_traceback(*args, **kwargs):                                    │
│   164 │   __tracebackhide__ = True                                                               │
│   165 │   try:                                                                                   │
│ ❱ 166 │     return fun(*args, **kwargs)                                                          │
│   167 │   except Exception as e:                                                                 │
│   168 │     mode = _filtering_mode()                                                             │
│   169 │     if _is_under_reraiser(e) or mode == "off":                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:1640 in init                  │
│                                                                                                  │
│   1637 │   """                                                                                   │
│   1638 │   Module._module_checks(self)                                                           │
│   1639 │                                                                                         │
│ ❱ 1640 │   _, v_out = self.init_with_output(                                                     │
│   1641 │   │   rngs,                                                                             │
│   1642 │   │   *args,                                                                            │
│   1643 │   │   method=method,                                                                    │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/jax/_src/traceback_util.py:166 in                  │
│ reraise_with_filtered_traceback                                                                  │
│                                                                                                  │
│   163   def reraise_with_filtered_traceback(*args, **kwargs):                                    │
│   164 │   __tracebackhide__ = True                                                               │
│   165 │   try:                                                                                   │
│ ❱ 166 │     return fun(*args, **kwargs)                                                          │
│   167 │   except Exception as e:                                                                 │
│   168 │     mode = _filtering_mode()                                                             │
│   169 │     if _is_under_reraiser(e) or mode == "off":                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:1545 in init_with_output      │
│                                                                                                  │
│   1542 │   elif method is None:                                                                  │
│   1543 │     method = self.__call__                                                              │
│   1544 │   method = _get_unbound_fn(method)                                                      │
│ ❱ 1545 │   return init_with_output(                                                              │
│   1546 │   │   method,                                                                           │
│   1547 │   │   self,                                                                             │
│   1548 │   │   mutable=mutable,                                                                  │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/core/scope.py:965 in wrapper                  │
│                                                                                                  │
│    962 │   if not isinstance(rngs, dict):                                                        │
│    963 │     rngs = {'params': rngs}                                                             │
│    964 │   init_flags = {**(flags if flags is not None else {}), 'initializing': True}           │
│ ❱  965 │   return apply(fn, mutable=mutable, flags=init_flags)({}, *args, rngs=rngs,             │
│    966 │   │   │   │   │   │   │   │   │   │   │   │   │   │   **kwargs)                         │
│    967                                                                                           │
│    968   return wrapper                                                                          │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/core/scope.py:933 in wrapper                  │
│                                                                                                  │
│    930 │                                                                                         │
│    931 │   with bind(variables, rngs=rngs, mutable=mutable,                                      │
│    932 │   │   │     flags=flags).temporary() as root:                                           │
│ ❱  933 │     y = fn(root, *args, **kwargs)                                                       │
│    934 │   if mutable is not False:                                                              │
│    935 │     return y, root.mutable_variables()                                                  │
│    936 │   else:                                                                                 │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:2121 in scope_fn              │
│                                                                                                  │
│   2118   def scope_fn(scope, *args, **kwargs):                                                   │
│   2119 │   _context.capture_stack.append(capture_intermediates)                                  │
│   2120 │   try:                                                                                  │
│ ❱ 2121 │     return fn(module.clone(parent=scope), *args, **kwargs)                              │
│   2122 │   finally:                                                                              │
│   2123 │     _context.capture_stack.pop()                                                        │
│   2124                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:622 in __call__                                                                               │
│                                                                                                  │
│   619 │   │   output_hidden_states: bool = False,                                                │
│   620 │   │   return_dict: bool = True,                                                          │
│   621 │   ):                                                                                     │
│ ❱ 622 │   │   outputs = self.transformer(                                                        │
│   623 │   │   │   input_ids,                                                                     │
│   624 │   │   │   attention_mask,                                                                │
│   625 │   │   │   position_ids,                                                                  │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:555 in __call__                                                                               │
│                                                                                                  │
│   552 │   │   hidden_states = input_embeds + position_embeds                                     │
│   553 │   │   hidden_states = self.dropout(hidden_states, deterministic=deterministic)           │
│   554 │   │                                                                                      │
│ ❱ 555 │   │   outputs = self.h(                                                                  │
│   556 │   │   │   hidden_states,                                                                 │
│   557 │   │   │   attention_mask,                                                                │
│   558 │   │   │   deterministic=deterministic,                                                   │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:499 in __call__                                                                               │
│                                                                                                  │
│   496 │   │   │   if output_hidden_states:                                                       │
│   497 │   │   │   │   all_hidden_states += (hidden_states,)                                      │
│   498 │   │   │                                                                                  │
│ ❱ 499 │   │   │   layer_outputs = block(                                                         │
│   500 │   │   │   │   hidden_states,                                                             │
│   501 │   │   │   │   attention_mask,                                                            │
│   502 │   │   │   │   deterministic=deterministic,                                               │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:320 in __call__                                                                               │
│                                                                                                  │
│   317 │   ):                                                                                     │
│   318 │   │   residual = hidden_states                                                           │
│   319 │   │   hidden_states = self.ln_1(hidden_states)                                           │
│ ❱ 320 │   │   outputs = self.attn(                                                               │
│   321 │   │   │   hidden_states,                                                                 │
│   322 │   │   │   attention_mask=attention_mask,                                                 │
│   323 │   │   │   deterministic=deterministic,                                                   │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:266 in __call__                                                                               │
│                                                                                                  │
│   263 │   │   init_cache: bool = False,                                                          │
│   264 │   │   output_attentions: bool = False,                                                   │
│   265 │   ):                                                                                     │
│ ❱ 266 │   │   return self.attention(                                                             │
│   267 │   │   │   hidden_states,                                                                 │
│   268 │   │   │   attention_mask=attention_mask,                                                 │
│   269 │   │   │   deterministic=deterministic,                                                   │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:432 in wrapped_module_method  │
│                                                                                                  │
│    429 │   # otherwise call the wrapped function as is.                                          │
│    430 │   if args and isinstance(args[0], Module):                                              │
│    431 │     self, args = args[0], args[1:]                                                      │
│ ❱  432 │     return self._call_wrapped_method(fun, args, kwargs)                                 │
│    433 │   else:                                                                                 │
│    434 │     return fun(*args, **kwargs)                                                         │
│    435   wrapped_module_method.method_handler_wrapped = True  # type: ignore[attr-defined]       │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/flax/linen/module.py:864 in _call_wrapped_method   │
│                                                                                                  │
│    861 │     # call method                                                                       │
│    862 │     if _use_named_call:                                                                 │
│    863 │   │   with jax.named_scope(_derive_profiling_name(self, fun)):                          │
│ ❱  864 │   │     y = fun(self, *args, **kwargs)                                                  │
│    865 │     else:                                                                               │
│    866 │   │   y = fun(self, *args, **kwargs)                                                    │
│    867                                                                                           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_flax_gpt_neo. │
│ py:209 in __call__                                                                               │
│                                                                                                  │
│   206 │   │   batch_size = hidden_states.shape[0]                                                │
│   207 │   │   causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:   │
│   208 │   │                                                                                      │
│ ❱ 209 │   │   attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2))   │
│   210 │   │   attention_mask = combine_masks(attention_mask, causal_mask)                        │
│   211 │   │                                                                                      │
│   212 │   │   dropout_rng = None                                                                 │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py:1117 in broadcast_to   │
│                                                                                                  │
│   1114 The JAX version does not necessarily return a view of the input.                          │
│   1115 """)                                                                                      │
│   1116 def broadcast_to(array: ArrayLike, shape: Shape) -> Array:                                │
│ ❱ 1117   return util._broadcast_to(array, shape)                                                 │
│   1118                                                                                           │
│   1119                                                                                           │
│   1120 def _split(op: str, ary: ArrayLike, indices_or_sections: Union[int, ArrayLike],           │
│                                                                                                  │
│ /home/moe/.local/lib/python3.11/site-packages/jax/_src/numpy/util.py:418 in _broadcast_to        │
│                                                                                                  │
│   415 │   │   │   │   │    for arr_d, shape_d in safe_zip(arr_shape, shape_tail))                │
│   416 │   if nlead < 0 or not compatible:                                                        │
│   417 │     msg = "Incompatible shapes for broadcasting: {} and requested shape {}"              │
│ ❱ 418 │     raise ValueError(msg.format(arr_shape, shape))                                       │
│   419 │   diff, = np.where(tuple(not core.symbolic_equal_dim(arr_d, shape_d)                     │
│   420 │   │   │   │   │   │      for arr_d, shape_d in safe_zip(arr_shape, shape_tail)))         │
│   421 │   new_dims = tuple(range(nlead)) + tuple(nlead + diff)                                   │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ValueError: Incompatible shapes for broadcasting: (1, 1, 1, 2296) and requested shape (1, 1, 2048, 2048)
```

If I use the following code with 
[eval_arc_test_dataset_solve_prefix.csv](https://github.com/huggingface/transformers/files/11471190/eval_arc_test_dataset_solve_prefix.csv) and `expected_length = config.max_position_embeddings` and `generated_max_length = len(prompt) + len(correct_answer)` after the [embedding resizing/sharding code](https://github.com/huggingface/transformers/tree/3335724376319a0c453049d0cd883504f530ff52/examples/research_projects/jax-projects/model_parallel#model-parallel-language-model-training-example) , I have the above error.

```python
import pandas as pd
from tqdm import tqdm
import jax
import numpy as np
from transformers import FlaxGPTNeoForCausalLM, AutoTokenizer, AutoConfig

model_name = './gpt-neo-125M'
model = FlaxGPTNeoForCausalLM.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

# Function to calculate character accuracy
def character_accuracy(predicted, correct):
    matching_chars = sum(c1 == c2 for c1, c2 in zip(predicted, correct))
    return matching_chars / max(len(predicted), len(correct))

# Initialize counters and lists for results
total_correct = 0
total_rows = len(df)
correct = []
char_accuracy = []
predictions = []

# Read the CSV file
df = pd.read_csv("./eval_arc_test_dataset_solve_prefix.csv")

def tokenize_function(examples):
    # strip leading and trailing spaces from examples["correct_answer"]
    #print("examples[\"correct_answer\"] = ", examples["correct_answer"])
    examples["correct_answer"] = [x.strip() for x in examples["correct_answer"]]
    
    #empty resultant list
    prompt_correct_answer = [] 
    
    #choose the smaller list to iterate
    small_list = len(examples["prompt"]) < len(examples["correct_answer"]) and examples["prompt"] or examples["correct_answer"]
    prompt_correct_answer = [examples["prompt"][i]+examples["correct_answer"][i] for i in range(len(small_list))]  

    expected_length = config.max_position_embeddings  #data_args.block_size

    #tokenized_prompt = tokenizer(prompt_correct_answer, padding="longest", truncation=True, max_length=None)
    tokenized_prompt = tokenizer(prompt_correct_answer, padding="max_length", truncation=True, max_length=expected_length)

    # Force the length of the input_ids and attention_mask to match the expected length
    tokenized_prompt["input_ids"] = [seq[:expected_length] + [tokenizer.pad_token_id] * (expected_length - len(seq)) for seq in tokenized_prompt["input_ids"]]
    tokenized_prompt["attention_mask"] = [mask[:expected_length] + [0] * (expected_length - len(mask)) for mask in tokenized_prompt["attention_mask"]]

    # Convert tokenized sequences to arrays of integers
    input_ids = np.array(tokenized_prompt["input_ids"], dtype=np.int32)
    attention_mask = np.array(tokenized_prompt["attention_mask"], dtype=np.int32)

    print("input_ids[0] = ", input_ids[0])

    return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": input_ids}

'''
eval_tokenized_dataset = df.map(
    tokenize_function,
    batched=True,
    num_proc=2,
    remove_columns=['prompt', 'correct_answer'],
    load_from_cache_file=False,
)
'''

def process_dataframe(df):
    # Convert the DataFrame to a dictionary
    examples = df.to_dict(orient='list')

    # Call the tokenize_function with the examples dictionary
    return tokenize_function(examples)

# Process the DataFrame using the process_dataframe function
eval_tokenized_dataset = process_dataframe(df)

# Iterate over rows in the DataFrame
for index, row in tqdm(df.iterrows(), total=total_rows):
    prompt = row['prompt']
    correct_answer = row['correct_answer']

    generated_max_length = len(prompt) + len(correct_answer)
    #print("generated_max_length = ", generated_max_length)

    #Changing the seed and thus the prng_key value below, does seem to change the outcome.
    seed = 1000
    model.seed = seed

    #inputs = tokenizer(prompt, return_tensors="np")

    # Generate the answer
    #Changing temperature, top_k and top_p does not seem to change the outcome
    outputs = model.generate(
        input_ids = eval_tokenized_dataset["input_ids"][index][None, :], 
        max_new_tokens=generated_max_length, 
        pad_token_id = model.config.eos_token_id,    
        prng_key=jax.random.PRNGKey(seed),
        temperature=0.8,
        early_stopping=True,
        top_k=50,
        top_p=0.95,
        do_sample=True,
        no_repeat_ngram_size=2)

    output_sequence = outputs['sequences'].squeeze(0)
    generated_answer = tokenizer.decode(output_sequence, clean_up_tokenization_spaces=True)

    predictions.append(generated_answer)

    # Calculate correctness and character accuracy
    is_correct = int(generated_answer == correct_answer)
    char_acc = character_accuracy(generated_answer, correct_answer)

    # Update counters and lists
    total_correct += is_correct
    correct.append(is_correct)
    char_accuracy.append(char_acc)

# Add the new columns to the DataFrame
df['predictions'] = predictions
df['correct'] = correct
df['character_accuracy'] = char_accuracy

# Calculate and print the statistics
percentage_correct = total_correct / total_rows * 100
avg_char_accuracy = sum(char_accuracy) / total_rows * 100

print(f"Total correct answers: {total_correct}")
print(f"Percentage correct: {percentage_correct:.2f}%")
print(f"avg_char_accuracy: {avg_char_accuracy:.2f}%")

# Save the updated DataFrame to a new CSV file
df.to_csv("eval_arc_test_dataset_solve_prefix.csv", index=False)
```<|||||>Hey! Thanks for opening an issue. 
For more help on how to use the model, I would recommend you to ask and check out [forum](https://discuss.huggingface.co/). 
However, if you want help for this particular problem, we would need a *minimal*  reproducing script where you remove everything that is not necessary to reproduce the particular bug! That would help us a lot<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.