Datasets:
TMarcus
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InternVL

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"use_flash_attn": true
}
}
10/22/2024 17:17:01 - INFO - __main__ - Using flash_attention_2 for InternLM
[INFO|modeling_utils.py:3473] 2024-10-22 17:17:01,285 >> loading weights file /home/yunjie/data/Mini-InternVL-Chat-2B-V1-5/model.safetensors
[INFO|modeling_utils.py:1426] 2024-10-22 17:17:01,298 >> Instantiating InternVLChatModel model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:826] 2024-10-22 17:17:01,299 >> Generate config GenerationConfig {}
[WARNING|logging.py:314] 2024-10-22 17:17:01,319 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|configuration_utils.py:826] 2024-10-22 17:17:01,330 >> Generate config GenerationConfig {
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 2
}
[WARNING|logging.py:314] 2024-10-22 17:17:01,381 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[WARNING|logging.py:314] 2024-10-22 17:17:01,403 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|modeling_utils.py:4350] 2024-10-22 17:17:04,530 >> All model checkpoint weights were used when initializing InternVLChatModel.
[INFO|modeling_utils.py:4358] 2024-10-22 17:17:04,530 >> All the weights of InternVLChatModel were initialized from the model checkpoint at /home/yunjie/data/Mini-InternVL-Chat-2B-V1-5.
If your task is similar to the task the model of the checkpoint was trained on, you can already use InternVLChatModel for predictions without further training.
[INFO|configuration_utils.py:779] 2024-10-22 17:17:04,533 >> loading configuration file /home/yunjie/data/Mini-InternVL-Chat-2B-V1-5/generation_config.json
[INFO|configuration_utils.py:826] 2024-10-22 17:17:04,533 >> Generate config GenerationConfig {
"eos_token_id": [
92542,
92543
]
}
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
[INFO|tokenization_utils_base.py:2025] 2024-10-22 17:17:04,657 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:2025] 2024-10-22 17:17:04,657 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2025] 2024-10-22 17:17:04,657 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2025] 2024-10-22 17:17:04,657 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2025] 2024-10-22 17:17:04,657 >> loading file tokenizer.json
[INFO|configuration_utils.py:727] 2024-10-22 17:17:04,657 >> loading configuration file /home/yunjie/data/bert-base-uncased/config.json
[INFO|configuration_utils.py:792] 2024-10-22 17:17:04,658 >> Model config BertConfig {
"_name_or_path": "/home/yunjie/data/bert-base-uncased",
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.37.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
[INFO|configuration_utils.py:727] 2024-10-22 17:17:04,686 >> loading configuration file /home/yunjie/data/bert-base-uncased/config.json
[INFO|configuration_utils.py:792] 2024-10-22 17:17:04,686 >> Model config BertConfig {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.37.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
[INFO|modeling_utils.py:3473] 2024-10-22 17:17:04,686 >> loading weights file /home/yunjie/data/bert-base-uncased/model.safetensors
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
[INFO|modeling_utils.py:4340] 2024-10-22 17:17:04,891 >> Some weights of the model checkpoint at /home/yunjie/data/bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[INFO|modeling_utils.py:4358] 2024-10-22 17:17:04,891 >> All the weights of BertModel were initialized from the model checkpoint at /home/yunjie/data/bert-base-uncased.
If your task is similar to the task the model of the checkpoint was trained on, you can already use BertModel for predictions without further training.
loading bert-base-uncased done
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
loading bert-base-uncased done