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Llama-3.2-11B-Vision-Instruct

This model is a fine-tuned version of meta-llama/Llama-3.2-11B-Vision-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7115
  • Bleu: 0.3191
  • Rouge1: 0.6462
  • Rouge2: 0.3482
  • Rougel: 0.5529
  • Bertscore Precision: 0.8764
  • Bertscore Recall: 0.8935
  • Bertscore F1: 0.8848

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss Bleu Rouge1 Rouge2 Rougel Bertscore Precision Bertscore Recall Bertscore F1
1.7942 0.6202 50 1.7909 0.2890 0.6131 0.3240 0.5197 0.8199 0.8912 0.8535
1.7177 1.2403 100 1.7262 0.3165 0.6445 0.3454 0.5501 0.8724 0.8928 0.8825
1.7198 1.8605 150 1.7158 0.3184 0.6462 0.3475 0.5520 0.8753 0.8932 0.8841
1.6898 2.4806 200 1.7115 0.3191 0.6462 0.3482 0.5529 0.8764 0.8935 0.8848

Framework versions

  • PEFT 0.13.0
  • Transformers 4.45.2
  • Pytorch 2.2.0a0+81ea7a4
  • Datasets 3.0.1
  • Tokenizers 0.20.1
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