sft-llava-1.5-7b-hf3

This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 13.1181
  • Bleu: 0.0
  • Rouge1: 0.0651
  • Rouge2: 0.0043
  • Rougel: 0.0508
  • Bertscore Precision: 0.6243
  • Bertscore Recall: 0.7482
  • Bertscore F1: 0.6806

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 5.0

Training results

Training Loss Epoch Step Validation Loss Bleu Rouge1 Rouge2 Rougel Bertscore Precision Bertscore Recall Bertscore F1
6.903 0.3101 200 22.1793 0.0 0.0440 0.0 0.0441 0.6243 0.7482 0.6806
6.585 0.6202 400 27.3559 0.0 0.0546 0.0043 0.0425 0.6243 0.7482 0.6806
6.5197 0.9302 600 26.1987 0.0 0.0546 0.0043 0.0425 0.6243 0.7482 0.6806
6.2662 1.2403 800 21.1666 0.0 0.0633 0.0043 0.0520 0.6243 0.7482 0.6806
6.0303 1.5504 1000 21.0359 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.7602 1.8605 1200 19.0201 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.6359 2.1705 1400 18.6311 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.5176 2.4806 1600 17.9442 0.0 0.0649 0.0043 0.0496 0.6243 0.7482 0.6806
5.4608 2.7907 1800 16.6921 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.2881 3.1008 2000 15.3415 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.2429 3.4109 2200 14.8475 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.1929 3.7209 2400 14.2828 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.1259 4.0310 2600 13.8075 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.0379 4.3411 2800 13.4751 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.1071 4.6512 3000 13.2275 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806
5.1082 4.9612 3200 13.1181 0.0 0.0651 0.0043 0.0508 0.6243 0.7482 0.6806

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.2.0a0+81ea7a4
  • Datasets 3.0.1
  • Tokenizers 0.20.1
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