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č → \r). See tokenizers#1996 and the upstream fix in tokenizers#1995.ZlatorogTokenizerFast (tokenization_zlatorog.py), which decodes added tokens the same way as the Transformers 4.x slow Zlatorog tokenizer. Token ids and vocabulary strings are unchanged.transformers>=4.45 or >=5.0tokenizers>=0.22trust_remote_code=True (loads ZlatorogTokenizerFast)1from transformers import AutoTokenizer
2
3tok = AutoTokenizer.from_pretrained(
4 "zidsi/Zlatorog-30B-MoE-tokenizer",
5 trust_remote_code=True,
6)
7
8word = "Začnimo"
9ids = tok.encode(word, add_special_tokens=False)
10assert tok.decode(ids) == wordzidsi/Zlatorog-30B-MoE-CPT_Long (or any checkpoint trained with the same vocabulary).a2759ee7565dc7c55c9c93c3f9e72190dcf5def4). See the companion repo’s artifacts/affected_added_tokens.json for the full checklist.