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--- |
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base_model: meta-llama/Meta-Llama-3-8B |
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library_name: peft |
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license: llama3 |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Llama3_8B_final_Task2_2.0 |
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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_final_Task2_2.0 |
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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: 0.5334 |
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- Accuracy: 0.9129 |
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- Precision: 0.9050 |
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- Recall: 0.9231 |
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- F1 score: 0.9140 |
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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: 8 |
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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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| 0.6105 | 0.2725 | 200 | 0.3962 | 0.8486 | 0.8793 | 0.8091 | 0.8427 | |
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| 0.4553 | 0.5450 | 400 | 0.3388 | 0.8757 | 0.8729 | 0.8803 | 0.8766 | |
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| 0.4318 | 0.8174 | 600 | 0.3273 | 0.89 | 0.9006 | 0.8775 | 0.8889 | |
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| 0.3155 | 1.0899 | 800 | 0.3807 | 0.89 | 0.9281 | 0.8462 | 0.8852 | |
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| 0.3279 | 1.3624 | 1000 | 0.3757 | 0.8943 | 0.9601 | 0.8234 | 0.8865 | |
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| 0.2451 | 1.6349 | 1200 | 0.3784 | 0.89 | 0.8683 | 0.9202 | 0.8935 | |
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| 0.2956 | 1.9074 | 1400 | 0.3187 | 0.9143 | 0.9318 | 0.8946 | 0.9128 | |
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| 0.2107 | 2.1798 | 1600 | 0.3999 | 0.89 | 0.8513 | 0.9459 | 0.8961 | |
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| 0.1744 | 2.4523 | 1800 | 0.6330 | 0.8857 | 0.9788 | 0.7892 | 0.8738 | |
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| 0.191 | 2.7248 | 2000 | 0.4101 | 0.91 | 0.9444 | 0.8718 | 0.9067 | |
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| 0.1378 | 2.9973 | 2200 | 0.4604 | 0.8957 | 0.8582 | 0.9487 | 0.9012 | |
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| 0.0703 | 3.2698 | 2400 | 0.4276 | 0.9 | 0.8958 | 0.9060 | 0.9008 | |
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| 0.0582 | 3.5422 | 2600 | 0.5431 | 0.9086 | 0.9527 | 0.8604 | 0.9042 | |
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| 0.0887 | 3.8147 | 2800 | 0.4993 | 0.9157 | 0.9534 | 0.8746 | 0.9123 | |
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| 0.0976 | 4.0872 | 3000 | 0.4540 | 0.9157 | 0.9269 | 0.9031 | 0.9149 | |
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| 0.0179 | 4.3597 | 3200 | 0.5068 | 0.92 | 0.9086 | 0.9345 | 0.9213 | |
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| 0.0277 | 4.6322 | 3400 | 0.5119 | 0.9157 | 0.9269 | 0.9031 | 0.9149 | |
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| 0.0231 | 4.9046 | 3600 | 0.5334 | 0.9129 | 0.9050 | 0.9231 | 0.9140 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
