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--- |
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library_name: transformers |
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license: llama2 |
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base_model: llava-hf/llava-1.5-7b-hf |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- rouge |
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model-index: |
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- name: sft-llava-1.5-7b_lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sft-llava-1.5-7b_lora |
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This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.9404 |
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- Bleu: 0.1802 |
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- Rouge1: 0.4861 |
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- Rouge2: 0.1709 |
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- Rougel: 0.3580 |
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- Bertscore Precision: 0.6578 |
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- Bertscore Recall: 0.7479 |
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- Bertscore F1: 0.6999 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rouge2 | Rougel | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:------:|:-------------------:|:----------------:|:------------:| |
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| 5.7514 | 0.3101 | 200 | 5.6831 | 0.0772 | 0.2028 | 0.0717 | 0.1778 | 0.6381 | 0.7437 | 0.6869 | |
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| 2.9737 | 0.6202 | 400 | 2.9242 | 0.1580 | 0.4319 | 0.1445 | 0.3306 | 0.6578 | 0.7479 | 0.6999 | |
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| 2.6756 | 0.9302 | 600 | 2.6594 | 0.1839 | 0.4859 | 0.1759 | 0.3680 | 0.6381 | 0.7437 | 0.6869 | |
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| 2.18 | 1.2403 | 800 | 2.5783 | 0.1754 | 0.4864 | 0.1754 | 0.3775 | 0.6578 | 0.7479 | 0.6999 | |
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| 2.0957 | 1.5504 | 1000 | 2.5019 | 0.1849 | 0.4877 | 0.1850 | 0.3801 | 0.6578 | 0.7479 | 0.6999 | |
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| 2.0109 | 1.8605 | 1200 | 2.4393 | 0.1879 | 0.4911 | 0.1840 | 0.3859 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.7656 | 2.1705 | 1400 | 2.9613 | 0.1808 | 0.4810 | 0.1719 | 0.3644 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.7271 | 2.4806 | 1600 | 3.0544 | 0.1817 | 0.4795 | 0.1695 | 0.3629 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.6746 | 2.7907 | 1800 | 3.0377 | 0.1754 | 0.4765 | 0.1639 | 0.3508 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.1183 | 3.1008 | 2000 | 3.6408 | 0.1801 | 0.4821 | 0.1710 | 0.3636 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.1123 | 3.4109 | 2200 | 3.6913 | 0.1765 | 0.4903 | 0.1712 | 0.3629 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.1051 | 3.7209 | 2400 | 3.7181 | 0.1766 | 0.4884 | 0.1701 | 0.3618 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.046 | 4.0310 | 2600 | 3.7719 | 0.1781 | 0.4849 | 0.1711 | 0.3598 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.0444 | 4.3411 | 2800 | 3.9170 | 0.1801 | 0.4852 | 0.1719 | 0.3595 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.0452 | 4.6512 | 3000 | 3.9377 | 0.1808 | 0.4872 | 0.1714 | 0.3604 | 0.6578 | 0.7479 | 0.6999 | |
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| 0.0449 | 4.9612 | 3200 | 3.9404 | 0.1802 | 0.4861 | 0.1709 | 0.3580 | 0.6578 | 0.7479 | 0.6999 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.2.0a0+81ea7a4 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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