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--- |
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base_model: meta-llama/Llama-3.2-11B-Vision-Instruct |
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library_name: peft |
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license: llama3.2 |
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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: Llama-3.2-11B-Vision-Instruct |
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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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# Llama-3.2-11B-Vision-Instruct |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co./meta-llama/Llama-3.2-11B-Vision-Instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7115 |
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- Bleu: 0.3191 |
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- Rouge1: 0.6462 |
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- Rouge2: 0.3482 |
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- Rougel: 0.5529 |
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- Bertscore Precision: 0.8764 |
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- Bertscore Recall: 0.8935 |
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- Bertscore F1: 0.8848 |
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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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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 3.0 |
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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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| 1.7942 | 0.6202 | 50 | 1.7909 | 0.2890 | 0.6131 | 0.3240 | 0.5197 | 0.8199 | 0.8912 | 0.8535 | |
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| 1.7177 | 1.2403 | 100 | 1.7262 | 0.3165 | 0.6445 | 0.3454 | 0.5501 | 0.8724 | 0.8928 | 0.8825 | |
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| 1.7198 | 1.8605 | 150 | 1.7158 | 0.3184 | 0.6462 | 0.3475 | 0.5520 | 0.8753 | 0.8932 | 0.8841 | |
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| 1.6898 | 2.4806 | 200 | 1.7115 | 0.3191 | 0.6462 | 0.3482 | 0.5529 | 0.8764 | 0.8935 | 0.8848 | |
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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 2.2.0a0+81ea7a4 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
