Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
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1.16k
"return_dict_in_generate": false,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": "bfloat16",
"torchscript": false,
"transformers_version": "4.37.2",
"typical_p": 1.0,
"use_bfloat16": true,
"use_flash_attn": true
}
}
10/23/2024 14:41:06 - INFO - __main__ - Using flash_attention_2 for InternLM
[INFO|modeling_utils.py:3473] 2024-10-23 14:41:06,505 >> loading weights file /home/yunjie/data/Mini-InternVL-Chat-2B-V1-5/model.safetensors
[WARNING|logging.py:314] 2024-10-23 14:41:06,533 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|modeling_utils.py:1426] 2024-10-23 14:41:06,553 >> Instantiating InternVLChatModel model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:826] 2024-10-23 14:41:06,554 >> Generate config GenerationConfig {}
[INFO|configuration_utils.py:826] 2024-10-23 14:41:06,584 >> Generate config GenerationConfig {
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 2
}
[WARNING|logging.py:314] 2024-10-23 14:41:06,603 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[WARNING|logging.py:314] 2024-10-23 14:41:06,603 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|modeling_utils.py:4350] 2024-10-23 14:41:12,865 >> All model checkpoint weights were used when initializing InternVLChatModel.
[INFO|modeling_utils.py:4358] 2024-10-23 14:41:12,866 >> 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] 2024-10-23 14:41:12,868 >> loading configuration file /home/yunjie/data/Mini-InternVL-Chat-2B-V1-5/generation_config.json
[INFO|configuration_utils.py:826] 2024-10-23 14:41:12,869 >> Generate config GenerationConfig {
"eos_token_id": [
92542,
92543
]
}
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
[INFO|tokenization_utils_base.py:2025] 2024-10-23 14:41:13,001 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:2025] 2024-10-23 14:41:13,002 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2025] 2024-10-23 14:41:13,002 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2025] 2024-10-23 14:41:13,002 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2025] 2024-10-23 14:41:13,002 >> loading file tokenizer.json
[INFO|configuration_utils.py:727] 2024-10-23 14:41:13,002 >> loading configuration file /home/yunjie/data/bert-base-uncased/config.json
[INFO|configuration_utils.py:792] 2024-10-23 14:41:13,002 >> 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
}
loading bert-base-uncased from /home/yunjie/data/bert-base-uncased
[INFO|configuration_utils.py:727] 2024-10-23 14:41:13,031 >> loading configuration file /home/yunjie/data/bert-base-uncased/config.json
[INFO|configuration_utils.py:792] 2024-10-23 14:41:13,031 >> 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,