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--- |
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base_model: llava-hf/llava-1.5-7b-hf |
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library_name: peft |
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license: llama2 |
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metrics: |
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- bleu |
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- rouge |
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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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model-index: |
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- name: llava_test |
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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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# llava_test |
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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: 0.0446 |
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- Bleu: 0.6353 |
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- Rouge1: 0.7885 |
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- Rouge2: 0.7889 |
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- Rougel: 0.7893 |
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- Bertscore Precision: 0.6807 |
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- Bertscore Recall: 0.7674 |
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- Bertscore F1: 0.7213 |
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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: 5e-05 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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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- num_epochs: 100.0 |
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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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| 0.3168 | 10.0 | 10 | 2.2001 | 0.0724 | 0.3123 | 0.1239 | 0.2433 | 0.7068 | 0.7777 | 0.7405 | |
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| 0.2454 | 20.0 | 20 | 1.6882 | 0.1061 | 0.4044 | 0.1840 | 0.3274 | 0.7241 | 0.7794 | 0.7507 | |
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| 0.1821 | 30.0 | 30 | 1.1567 | 0.1925 | 0.5281 | 0.2989 | 0.4593 | 0.7054 | 0.7756 | 0.7387 | |
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| 0.109 | 40.0 | 40 | 0.5242 | 0.3915 | 0.6689 | 0.5316 | 0.6370 | 0.6878 | 0.7709 | 0.7268 | |
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| 0.0378 | 50.0 | 50 | 0.1193 | 0.5971 | 0.7701 | 0.7585 | 0.7700 | 0.6839 | 0.7688 | 0.7237 | |
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| 0.0098 | 60.0 | 60 | 0.0554 | 0.6254 | 0.7862 | 0.7867 | 0.7875 | 0.6799 | 0.7694 | 0.7217 | |
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| 0.0064 | 70.0 | 70 | 0.0482 | 0.6329 | 0.7889 | 0.7890 | 0.7899 | 0.6798 | 0.7690 | 0.7215 | |
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| 0.0059 | 80.0 | 80 | 0.0459 | 0.6331 | 0.7877 | 0.7877 | 0.7888 | 0.6777 | 0.7670 | 0.7194 | |
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| 0.0057 | 90.0 | 90 | 0.0451 | 0.6347 | 0.7897 | 0.7895 | 0.7907 | 0.6807 | 0.7675 | 0.7213 | |
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| 0.0056 | 100.0 | 100 | 0.0446 | 0.6353 | 0.7885 | 0.7889 | 0.7893 | 0.6807 | 0.7674 | 0.7213 | |
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### Framework versions |
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- PEFT 0.13.0 |
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- Transformers 4.45.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.20.1 |
