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--- |
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library_name: peft |
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license: gemma |
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base_model: google/codegemma-7b-it |
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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: code-bench-CodeGemma-7BIT-cg-nv9n_it_fs |
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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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# code-bench-CodeGemma-7BIT-cg-nv9n_it_fs |
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This model is a fine-tuned version of [google/codegemma-7b-it](https://huggingface.co/google/codegemma-7b-it) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0645 |
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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: 3 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 5 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.7408 | 0.0530 | 50 | 0.6774 | |
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| 0.5161 | 0.1061 | 100 | 0.5082 | |
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| 0.4379 | 0.1591 | 150 | 0.3828 | |
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| 0.338 | 0.2121 | 200 | 0.2834 | |
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| 0.2648 | 0.2652 | 250 | 0.2229 | |
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| 0.2033 | 0.3182 | 300 | 0.1773 | |
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| 0.1824 | 0.3713 | 350 | 0.1469 | |
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| 0.1561 | 0.4243 | 400 | 0.1352 | |
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| 0.1482 | 0.4773 | 450 | 0.1283 | |
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| 0.1349 | 0.5304 | 500 | 0.1212 | |
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| 0.1514 | 0.5834 | 550 | 0.1157 | |
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| 0.1318 | 0.6364 | 600 | 0.1137 | |
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| 0.1327 | 0.6895 | 650 | 0.1119 | |
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| 0.1363 | 0.7425 | 700 | 0.1109 | |
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| 0.1249 | 0.7955 | 750 | 0.1075 | |
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| 0.1172 | 0.8486 | 800 | 0.1067 | |
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| 0.1187 | 0.9016 | 850 | 0.1077 | |
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| 0.1195 | 0.9547 | 900 | 0.1049 | |
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| 0.1044 | 1.0077 | 950 | 0.1022 | |
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| 0.1111 | 1.0607 | 1000 | 0.1025 | |
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| 0.1041 | 1.1138 | 1050 | 0.1019 | |
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| 0.1076 | 1.1668 | 1100 | 0.0989 | |
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| 0.1062 | 1.2198 | 1150 | 0.0991 | |
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| 0.1108 | 1.2729 | 1200 | 0.0968 | |
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| 0.1085 | 1.3259 | 1250 | 0.0961 | |
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| 0.0955 | 1.3789 | 1300 | 0.0949 | |
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| 0.0922 | 1.4320 | 1350 | 0.0943 | |
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| 0.1065 | 1.4850 | 1400 | 0.0935 | |
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| 0.1032 | 1.5381 | 1450 | 0.0920 | |
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| 0.094 | 1.5911 | 1500 | 0.0910 | |
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| 0.099 | 1.6441 | 1550 | 0.0903 | |
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| 0.099 | 1.6972 | 1600 | 0.0895 | |
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| 0.0967 | 1.7502 | 1650 | 0.0893 | |
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| 0.0976 | 1.8032 | 1700 | 0.0887 | |
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| 0.0942 | 1.8563 | 1750 | 0.0876 | |
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| 0.0914 | 1.9093 | 1800 | 0.0865 | |
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| 0.0956 | 1.9623 | 1850 | 0.0855 | |
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| 0.0851 | 2.0154 | 1900 | 0.0853 | |
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| 0.0832 | 2.0684 | 1950 | 0.0851 | |
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| 0.095 | 2.1215 | 2000 | 0.0854 | |
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| 0.0775 | 2.1745 | 2050 | 0.0840 | |
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| 0.0826 | 2.2275 | 2100 | 0.0828 | |
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| 0.0795 | 2.2806 | 2150 | 0.0831 | |
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| 0.0826 | 2.3336 | 2200 | 0.0828 | |
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| 0.0864 | 2.3866 | 2250 | 0.0810 | |
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| 0.0832 | 2.4397 | 2300 | 0.0802 | |
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| 0.0817 | 2.4927 | 2350 | 0.0796 | |
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| 0.0766 | 2.5457 | 2400 | 0.0789 | |
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| 0.0823 | 2.5988 | 2450 | 0.0783 | |
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| 0.0795 | 2.6518 | 2500 | 0.0780 | |
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| 0.0798 | 2.7049 | 2550 | 0.0771 | |
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| 0.0833 | 2.7579 | 2600 | 0.0770 | |
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| 0.0775 | 2.8109 | 2650 | 0.0760 | |
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| 0.0851 | 2.8640 | 2700 | 0.0755 | |
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| 0.0699 | 2.9170 | 2750 | 0.0746 | |
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| 0.0804 | 2.9700 | 2800 | 0.0743 | |
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| 0.0657 | 3.0231 | 2850 | 0.0746 | |
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| 0.0733 | 3.0761 | 2900 | 0.0735 | |
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| 0.064 | 3.1291 | 2950 | 0.0733 | |
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| 0.0662 | 3.1822 | 3000 | 0.0731 | |
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| 0.0643 | 3.2352 | 3050 | 0.0722 | |
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| 0.0625 | 3.2883 | 3100 | 0.0721 | |
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| 0.0621 | 3.3413 | 3150 | 0.0718 | |
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| 0.0664 | 3.3943 | 3200 | 0.0716 | |
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| 0.0696 | 3.4474 | 3250 | 0.0708 | |
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| 0.0626 | 3.5004 | 3300 | 0.0705 | |
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| 0.0653 | 3.5534 | 3350 | 0.0701 | |
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| 0.0564 | 3.6065 | 3400 | 0.0697 | |
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| 0.0623 | 3.6595 | 3450 | 0.0692 | |
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| 0.0613 | 3.7125 | 3500 | 0.0688 | |
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| 0.0607 | 3.7656 | 3550 | 0.0686 | |
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| 0.0582 | 3.8186 | 3600 | 0.0685 | |
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| 0.0555 | 3.8717 | 3650 | 0.0680 | |
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| 0.0549 | 3.9247 | 3700 | 0.0677 | |
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| 0.0618 | 3.9777 | 3750 | 0.0673 | |
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| 0.0549 | 4.0308 | 3800 | 0.0674 | |
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| 0.0514 | 4.0838 | 3850 | 0.0674 | |
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| 0.0492 | 4.1368 | 3900 | 0.0670 | |
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| 0.0576 | 4.1899 | 3950 | 0.0670 | |
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| 0.0563 | 4.2429 | 4000 | 0.0665 | |
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| 0.0512 | 4.2959 | 4050 | 0.0665 | |
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| 0.0557 | 4.3490 | 4100 | 0.0663 | |
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| 0.0691 | 4.4052 | 4150 | 0.0663 | |
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| 0.0662 | 4.4582 | 4200 | 0.0661 | |
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| 0.068 | 4.5113 | 4250 | 0.0659 | |
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| 0.0674 | 4.5643 | 4300 | 0.0657 | |
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| 0.0637 | 4.6173 | 4350 | 0.0655 | |
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| 0.0701 | 4.6704 | 4400 | 0.0654 | |
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| 0.0655 | 4.7234 | 4450 | 0.0651 | |
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| 0.0676 | 4.7765 | 4500 | 0.0650 | |
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| 0.0621 | 4.8295 | 4550 | 0.0649 | |
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| 0.0664 | 4.8825 | 4600 | 0.0647 | |
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| 0.0652 | 4.9356 | 4650 | 0.0646 | |
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| 0.0626 | 4.9886 | 4700 | 0.0645 | |
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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 2.5.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
