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demo.log
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nohup: ignoring input
[2024-09-19 19:07:31,185] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s]Loading checkpoint shards: 17%|█▋ | 1/6 [00:03<00:18, 3.64s/it]Loading checkpoint shards: 33%|███▎ | 2/6 [00:07<00:14, 3.66s/it]Loading checkpoint shards: 50%|█████ | 3/6 [00:10<00:10, 3.63s/it]Loading checkpoint shards: 67%|██████▋ | 4/6 [00:14<00:07, 3.62s/it]Loading checkpoint shards: 83%|████████▎ | 5/6 [00:18<00:03, 3.61s/it]Loading checkpoint shards: 100%|██████████| 6/6 [00:19<00:00, 2.77s/it]Loading checkpoint shards: 100%|██████████| 6/6 [00:19<00:00, 3.21s/it]
Some weights of the model checkpoint at ./checkpoints_new/llava-uhd-144-13b-loc-unpad-fullft were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc2.bias', 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- This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
/home/guozonghao/anaconda3/envs/uhd-grounding/lib/python3.9/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
return self.fget.__get__(instance, owner)()
---------init adapt_vision_model---------
---------from ft ckpt---------
load info: <All keys matched successfully>
/home/guozonghao/anaconda3/envs/uhd-grounding/lib/python3.9/site-packages/transformers/generation/utils.py:1295: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )
warnings.warn(
/home/guozonghao/anaconda3/envs/uhd-grounding/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
warnings.warn(
/home/guozonghao/anaconda3/envs/uhd-grounding/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `None` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
warnings.warn(