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--- |
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license: llama3 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: meta-llama/Meta-Llama-3-8B |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: llama3-8B_Fact_U |
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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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# llama3-8B_Fact_U |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co./meta-llama/Meta-Llama-3-8B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1146 |
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- Accuracy: 0.7953 |
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- Precision: 0.8252 |
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- Recall: 0.7692 |
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- F1 score: 0.7963 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 0.9272 | 0.2509 | 200 | 1.1877 | 0.5765 | 0.5589 | 0.8801 | 0.6837 | |
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| 0.7133 | 0.5019 | 400 | 0.7246 | 0.7 | 0.6912 | 0.7647 | 0.7261 | |
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| 0.6476 | 0.7528 | 600 | 0.6219 | 0.7565 | 0.7945 | 0.7172 | 0.7539 | |
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| 0.5766 | 1.0038 | 800 | 0.5714 | 0.76 | 0.85 | 0.6538 | 0.7391 | |
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| 0.4292 | 1.2547 | 1000 | 0.5870 | 0.7588 | 0.7474 | 0.8100 | 0.7774 | |
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| 0.4047 | 1.5056 | 1200 | 0.6078 | 0.7824 | 0.7694 | 0.8303 | 0.7987 | |
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| 0.4753 | 1.7566 | 1400 | 0.5654 | 0.7929 | 0.8575 | 0.7217 | 0.7838 | |
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| 0.3831 | 2.0075 | 1600 | 0.5672 | 0.8012 | 0.8289 | 0.7783 | 0.8028 | |
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| 0.2666 | 2.2585 | 1800 | 0.6054 | 0.8047 | 0.8416 | 0.7692 | 0.8038 | |
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| 0.3267 | 2.5094 | 2000 | 0.6620 | 0.7929 | 0.8182 | 0.7738 | 0.7953 | |
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| 0.2742 | 2.7604 | 2200 | 0.6985 | 0.7941 | 0.8579 | 0.7240 | 0.7853 | |
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| 0.3125 | 3.0113 | 2400 | 0.6373 | 0.7859 | 0.7955 | 0.7919 | 0.7937 | |
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| 0.1743 | 3.2622 | 2600 | 0.8362 | 0.8047 | 0.8730 | 0.7308 | 0.7956 | |
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| 0.1837 | 3.5132 | 2800 | 0.7889 | 0.7965 | 0.8440 | 0.7466 | 0.7923 | |
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| 0.2013 | 3.7641 | 3000 | 0.8958 | 0.7659 | 0.7425 | 0.8416 | 0.7890 | |
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| 0.1572 | 4.0151 | 3200 | 0.8662 | 0.8012 | 0.864 | 0.7330 | 0.7931 | |
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| 0.0993 | 4.2660 | 3400 | 0.8730 | 0.8024 | 0.8477 | 0.7557 | 0.7990 | |
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| 0.0704 | 4.5169 | 3600 | 1.0571 | 0.8012 | 0.8421 | 0.7602 | 0.7990 | |
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| 0.0598 | 4.7679 | 3800 | 1.1146 | 0.7953 | 0.8252 | 0.7692 | 0.7963 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.44.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
