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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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base_model: google/gemma-2b |
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model-index: |
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- name: RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenTrue |
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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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# RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenTrue |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0495 |
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- Accuracy: 0.9820 |
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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: 1.41e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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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: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.9068 | 0.03 | 250 | 0.5546 | 0.7177 | |
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| 0.5566 | 0.06 | 500 | 0.2048 | 0.9170 | |
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| 0.5143 | 0.08 | 750 | 0.1646 | 0.9370 | |
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| 0.4865 | 0.11 | 1000 | 0.1396 | 0.9457 | |
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| 0.4771 | 0.14 | 1250 | 0.1204 | 0.9510 | |
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| 0.4452 | 0.17 | 1500 | 0.1118 | 0.9565 | |
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| 0.436 | 0.19 | 1750 | 0.1063 | 0.9570 | |
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| 0.4433 | 0.22 | 2000 | 0.0942 | 0.9615 | |
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| 0.4541 | 0.25 | 2250 | 0.0878 | 0.9647 | |
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| 0.4361 | 0.28 | 2500 | 0.0822 | 0.9672 | |
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| 0.4626 | 0.31 | 2750 | 0.0766 | 0.9700 | |
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| 0.4595 | 0.33 | 3000 | 0.0714 | 0.9720 | |
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| 0.4375 | 0.36 | 3250 | 0.0720 | 0.9715 | |
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| 0.4338 | 0.39 | 3500 | 0.0693 | 0.9727 | |
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| 0.4082 | 0.42 | 3750 | 0.0675 | 0.9720 | |
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| 0.4306 | 0.44 | 4000 | 0.0635 | 0.9745 | |
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| 0.4296 | 0.47 | 4250 | 0.0629 | 0.9750 | |
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| 0.4318 | 0.5 | 4500 | 0.0590 | 0.9767 | |
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| 0.4226 | 0.53 | 4750 | 0.0575 | 0.9775 | |
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| 0.435 | 0.56 | 5000 | 0.0556 | 0.9785 | |
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| 0.4501 | 0.58 | 5250 | 0.0557 | 0.9790 | |
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| 0.3923 | 0.61 | 5500 | 0.0542 | 0.9785 | |
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| 0.4222 | 0.64 | 5750 | 0.0541 | 0.9790 | |
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| 0.3891 | 0.67 | 6000 | 0.0538 | 0.9787 | |
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| 0.4123 | 0.69 | 6250 | 0.0551 | 0.9790 | |
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| 0.3805 | 0.72 | 6500 | 0.0521 | 0.9805 | |
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| 0.4269 | 0.75 | 6750 | 0.0529 | 0.9800 | |
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| 0.382 | 0.78 | 7000 | 0.0530 | 0.9802 | |
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| 0.422 | 0.81 | 7250 | 0.0517 | 0.9812 | |
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| 0.4621 | 0.83 | 7500 | 0.0506 | 0.9812 | |
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| 0.3963 | 0.86 | 7750 | 0.0498 | 0.9820 | |
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| 0.4097 | 0.89 | 8000 | 0.0495 | 0.9820 | |
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| 0.4705 | 0.92 | 8250 | 0.0492 | 0.9822 | |
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| 0.4248 | 0.94 | 8500 | 0.0493 | 0.9820 | |
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| 0.3938 | 0.97 | 8750 | 0.0495 | 0.9820 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
