runtime error

Exit code: 1. Reason: 2.46M [00:00<00:00, 50.2MB/s] /usr/local/lib/python3.10/site-packages/transformers/convert_slow_tokenizer.py:564: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text. warnings.warn( pytorch_model.bin: 0%| | 0.00/286M [00:00<?, ?B/s] pytorch_model.bin: 93%|█████████▎| 265M/286M [00:01<00:00, 265MB/s] pytorch_model.bin: 100%|█████████▉| 286M/286M [00:01<00:00, 268MB/s] model.safetensors: 0%| | 0.00/286M [00:00<?, ?B/s] model.safetensors: 34%|███▍ | 97.3M/286M [00:01<00:02, 93.4MB/s] model.safetensors: 100%|█████████▉| 286M/286M [00:02<00:00, 133MB/s] Traceback (most recent call last): File "/home/user/app/app.py", line 7, in <module> model = GLiNER.from_pretrained("Ihor/gliner-biomed-bi-small-v1.0", trust_remote_code=True) File "/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn return fn(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/huggingface_hub/hub_mixin.py", line 566, in from_pretrained instance = cls._from_pretrained( File "/usr/local/lib/python3.10/site-packages/gliner/modules/base.py", line 234, in _from_pretrained model.load_state_dict(state_dict, strict=strict) File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2581, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for SpanGLiNER: size mismatch for token_rep_layer.bert_layer.model.embeddings.word_embeddings.weight: copying a param with shape torch.Size([128100, 768]) from checkpoint, the shape in current model is torch.Size([128004, 768]).

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