import transformers
from transformers import GenerationConfig
import torch

name = "gpt2"
tokenizer = transformers.AutoTokenizer.from_pretrained(name)
model = transformers.AutoModelForCausalLM.from_pretrained(name)

gc = GenerationConfig(
    max_new_tokens=40,
    eos_token_id=tokenizer.encode(" black white green red brown blue yellow purple pink orange"),
    pad_token_id=tokenizer.eos_token_id,
    num_beams=3,
)

input_ids = tokenizer.encode("Hello, I have 3 cats, one of them is colored", return_tensors="pt")
output = model.generate(input_ids, generation_config=gc)
tokenizer.decode(output[0])

File [/usr/local/lib/python3.10/site-packages/transformers/generation/utils.py:2985](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f6c756e675f63616e636572222c2273657474696e6773223a7b22686f7374223a227373683a2f2f796f6e69676f5f6770227d7d.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/site-packages/transformers/generation/utils.py:2985), in GenerationMixin.beam_search(self, input_ids, beam_scorer, logits_processor, stopping_criteria, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)
   2982 next_tokens = next_tokens % vocab_size
   2984 # stateless
-> 2985 beam_outputs = beam_scorer.process(
   2986     input_ids,
   2987     next_token_scores,
   2988     next_tokens,
   2989     next_indices,
   2990     pad_token_id=pad_token_id,
   2991     eos_token_id=eos_token_id,
   2992     beam_indices=beam_indices,
   2993 )
   2995 beam_scores = beam_outputs["next_beam_scores"]
   2996 beam_next_tokens = beam_outputs["next_beam_tokens"]

File [/usr/local/lib/python3.10/site-packages/transformers/generation/beam_search.py:297](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f6c756e675f63616e636572222c2273657474696e6773223a7b22686f7374223a227373683a2f2f796f6e69676f5f6770227d7d.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/site-packages/transformers/generation/beam_search.py:297), in BeamSearchScorer.process(self, input_ids, next_scores, next_tokens, next_indices, pad_token_id, eos_token_id, beam_indices, group_index)
    294         break
    296 if beam_idx < self.group_size:
--> 297     raise ValueError(
    298         f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:"
    299         f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected."
    300     )
    302 # Check if we are done so that we can save a pad step if all(done)
    303 self._done[batch_group_idx] = self._done[batch_group_idx] or self._beam_hyps[batch_group_idx].is_done(
    304     next_scores[batch_idx].max().item(), cur_len
    305 )
ValueError: At most 3 tokens in tensor([ 2266, 11398,  4171,  4077,    11, 10912]) can be equal to `eos_token_id: [2042, 2330, 4077, 2266, 7586, 4171, 7872, 14032, 11398, 10912]`. Make sure tensor([ 2266, 11398,  4171,  4077,    11, 10912]) are corrected.