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--- |
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base_model: meta-llama/Meta-Llama-3.1-8B |
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datasets: |
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- svenwey/LogDataset |
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library_name: peft |
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license: llama3.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: logdataset1024samples15epochs_llama3-1_16bit_64eval_natural_adalora_2 |
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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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# logdataset1024samples15epochs_llama3-1_16bit_64eval_natural_adalora_2 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the svenwey/LogDataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0752 |
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- Model Preparation Time: 0.0137 |
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- Linecount Difference Smape Score: 0.7773 |
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- Linecontentlength Difference Smape Score: 0.6338 |
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- Linecontent Sacrebleu Score: 0.4516 |
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- Linecontent Sacrebleu Withoutexplicitnumbers Score: 0.4777 |
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- Timestamps Smape Difference Score: 0.7332 |
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- Timestamps Formatconsistency Score: 1.0 |
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- Timestamps Monotinicity Score: 1.0 |
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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: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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: 15.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Linecount Difference Smape Score | Linecontentlength Difference Smape Score | Linecontent Sacrebleu Score | Linecontent Sacrebleu Withoutexplicitnumbers Score | Timestamps Smape Difference Score | Timestamps Formatconsistency Score | Timestamps Monotinicity Score | |
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|:-------------:|:-------:|:-----:|:---------------:|:----------------------:|:--------------------------------:|:----------------------------------------:|:---------------------------:|:--------------------------------------------------:|:---------------------------------:|:----------------------------------:|:-----------------------------:| |
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| 1.0869 | 0.9998 | 1147 | 1.1504 | 0.0137 | 0.7016 | 0.5256 | 0.3531 | 0.3637 | 0.6414 | 0.9531 | 0.9531 | |
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| 0.6665 | 1.9996 | 2294 | 1.5606 | 0.0137 | 0.7120 | 0.5669 | 0.4394 | 0.4462 | 0.6706 | 0.9844 | 0.9844 | |
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| 0.7854 | 2.9993 | 3441 | 1.7849 | 0.0137 | 0.7278 | 0.5911 | 0.4532 | 0.4643 | 0.6926 | 0.9688 | 0.9688 | |
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| 0.9092 | 4.0 | 4589 | 1.9795 | 0.0137 | 0.7370 | 0.5918 | 0.4415 | 0.4605 | 0.6926 | 0.9688 | 0.9688 | |
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| 1.0273 | 4.9998 | 5736 | 2.1496 | 0.0137 | 0.7426 | 0.5876 | 0.4323 | 0.4541 | 0.6975 | 0.9844 | 0.9844 | |
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| 1.1379 | 5.9996 | 6883 | 2.3043 | 0.0137 | 0.7554 | 0.6070 | 0.4534 | 0.4805 | 0.7112 | 0.9844 | 0.9844 | |
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| 1.2381 | 6.9993 | 8030 | 2.4486 | 0.0137 | 0.7618 | 0.6078 | 0.4553 | 0.4755 | 0.7194 | 0.9844 | 0.9844 | |
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| 1.326 | 8.0 | 9178 | 2.5735 | 0.0137 | 0.7655 | 0.6224 | 0.4573 | 0.4797 | 0.7235 | 1.0 | 1.0 | |
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| 1.4048 | 8.9998 | 10325 | 2.7091 | 0.0137 | 0.7602 | 0.6199 | 0.4522 | 0.4765 | 0.7160 | 1.0 | 1.0 | |
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| 1.4702 | 9.9996 | 11472 | 2.8050 | 0.0137 | 0.7396 | 0.5926 | 0.4319 | 0.4448 | 0.6944 | 0.9844 | 0.9844 | |
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| 1.5236 | 10.9993 | 12619 | 2.9013 | 0.0137 | 0.7473 | 0.6007 | 0.4234 | 0.4437 | 0.6984 | 1.0 | 1.0 | |
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| 1.5639 | 12.0 | 13767 | 2.9654 | 0.0137 | 0.7528 | 0.6115 | 0.4416 | 0.4739 | 0.7073 | 1.0 | 1.0 | |
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| 1.5956 | 12.9998 | 14914 | 3.0193 | 0.0137 | 0.7666 | 0.6197 | 0.4456 | 0.4609 | 0.7295 | 1.0 | 1.0 | |
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| 1.6152 | 13.9996 | 16061 | 3.0513 | 0.0137 | 0.7411 | 0.5917 | 0.4208 | 0.4372 | 0.6896 | 0.9844 | 0.9844 | |
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| 1.6235 | 14.9967 | 17205 | 3.0752 | 0.0137 | 0.7773 | 0.6338 | 0.4516 | 0.4777 | 0.7332 | 1.0 | 1.0 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
